Supplementary reading, FE5223 by Epitrain Pte Ltd

It’s all about Risk Financial Markets present risk in many forms. Market Participants embrace these Risks, as these Risk present profit opportunities. Fundamentally, “No Risk, No Reward”.

5 types of Risk 1) Market Risk a. Price Risk, also known as Direction Risk: has to do with whether the market goes your way or not. b. Liquidity Risk: Prices can go your way, but if there is no one for your to trade against to get out of your Position, you are still not a happy camper 2) Credit Risk (also known as Counterparty Risk) Primarily about whether your counterparty is able, and willing, to pay you. 3) Operation Risk Risks arising from operational matters, such as work flow, procedures and technology. a. Technology Risk An example of technology risk was an incident involving a Japanese bank. A trader there received an order to sell 1 share at 300,000 yen, but mistakenly entered an order to sell 300,000 shares at 1 yen. Needless to say the market crashed. The prices that were traded would have been progressively lower, as the erroneous order traded off the “best” bid, and then the next best, and so on. This would have resulted in significant “slippage” from the then current market price of 300,000 yen. The resultant Short 299,999 position was put onto the bank’s own books, since the customer only wanted to sell 1 share. In covering the Short, the bank ended up buying at prices higher than it had sold at, resulting in a significant loss for the bank, and a change in career for the trader. Mistakes like these are unfortunately all too common in the market, as traders are under pressure to enter orders as quickly as possible before prices change, and to secure a place ahead in the price queue. These mistakes are popularly referred to as “fat finger” errors. Possible Solutions for fat fingers would include: 1. Position Limits Primarily intended to prevent a trader from accumulating a position larger than what the bank wants him to be exposed to, a Position

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Limit could have prevented the order from being executed and hence saving the bank the loss. Position Limits could allow Long and/or Short positions, or often times with the case of Mutual Funds, allow only Long positions. The behaviour the Position Limit is configured to follow could be to either reject the entire order, if the order’s quantity (in this case 300,000 shares) is larger than the Position Limit set (say 50,000 shares), or to execute the allowable quantity (in this case essentially 50,000 minus any existing Short positions) and reject the balance. So if the Trader was say already short 10,000 shares, the Position Limit could have allowed the execution of 40,000 shares from the order and rejected the remaining 260,000. The bank would still have been hurt by the error, but to a much lesser extent. Consider this: If the trader was say Long 50,000 shares at the time he entered the erroneous order, how many shares from the order would have been executed and how many rejected?

2. Fat Finger Checks Fat Finger Checks are logical checks built into Order Management Systems specifically to guard against fat finger errors. Fat Finger Checks typically check the new order’s price and quantity against the current market price and the trader’s pre-set quantity threshold. Variance from the current market price could be set in terms of percentage or number of ticks1. So if a variance threshold was set for +/-5%, then given the current market price of 300,000 yen, the lowest acceptable order price would have been 285,000 yen, and the erroneous order would have been flagged for further attention before being sent to the market. There could be more than 1 threshold in force. Say +/-5% for the first threshold and +/-10% for the second threshold. A pop-up alert could be triggered if the order’s price was beyond the first threshold but not beyond the second. The trader who entered 1

Tick: The minimum upward or downward fluctuation in the price.

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the order could verify the order price against the current market price, and dismiss the alert himself, causing the order to be sent to the market. Should the order’s price be beyond the second threshold, the pop-up alert might require dismissal by the Chief Dealer before the order can be sent to market. This would provide a second pair of eyeballs to verify the order’s accuracy and authenticity. Fat Finger Checks can also check if the order’s quantity is unusual for the trader. The trader could set a quantity threshold of say 10 and 100 units, or whatever is appropriate for his trading style and typical order size. Again the first threshold could be a pop-up alert which he can dismiss. The second threshold could be an “iron gate” which will not allow the order through, regardless of dismissal of the alert. This is great for preventing a case of shaking finger tips entering a quantity of 1,000 where the intention was for 10. Of course if there was genuinely an order for 1,000 units, the trader would not be able to enter that as a single order. Instead, he would have to enter 10 x 100 unit orders. Consider this: Sending 10 orders of 100 units each still achieves the objective of sending 1,000 units to the market, but what are the negative impacts? What does this mean for latency in sending the orders? What does this mean for the price-time priority queue position? What does this mean when trying to cancel or modify the orders?

4) Systemic Risk This refers to the domino effect, where the failure of one bank to pay its counterparties, causes those counterparties to fail and hence default on their obligations to their counterparties, and so on.

5) Reputational Risk Some negative events might not lead to actual financial loss, but rather have a reputational impact on the bank. See this story as an example: http://www.bbc.com/news/business-31248913

Are Counterparty Risk and Systemic Risk Similar?

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Both Risks arise from a failure to pay. But there are some differences. Counterparty risk is where your direct counterparty defaults. Systemic Risk is where you are impacted by a default by a party that you may not have directly traded with. Counterparty Risk can be directly mitigated by your middle office, by setting appropriate counterparty limits. For example, you might set a counterparty limit of USD 20 million for bank such as Citibank, but may set a lower limit, like USD 5 million for a smaller, weaker bank. This reduces the impact on you should the weaker bank default, since your exposure to it would have been smaller. Systemic Risk however would be much harder to mitigate, since the defaulting party might not even have traded with you. So even if you had set counterparty limits of zero for the defaulting party, meaning your traders could not have traded with that party, its default could still impact you if the default caused your counterparty to become unable to pay you.

Basic Risk Management The inescapable Truth is that “in order to make more than risk-free rate, one must take risk”. Risk taken in the markets can be in Market Risk (which is the most basic form of trading, where we buy an instrument in the hope that its price will rise), or Credit Risk (say in the form of writing Credit Default Swaps). The Truth is that nobody can tell the future, and any view we take on the market could result in a profit or a loss. The problem is that untrained traders tend to follow hot tips, or only focus on the profit potential of a trade, and overlook the potential loss (i.e. the risk). Typically, professional traders look for Risk-Reward ratios of a minimum of 1:3. That is, they are willing to risk $1 to earn $3, or more. But all too often, unskilled traders end up buying a $0.90 share, believing that it will rise to $1.00, and do not manage their down-side risk should the market fall instead. They end up holding their Long positions indefinitely, locking up their capital and risking 100% of their capital should the company go bankrupt. They have essentially risk $0.90 to make $0.10. Not a wise move, with a terrible Risk:Reward.

Stop Loss Orders: “He who trades without Stops, soon stops trading” – Gerard Tong Key to trading are Stop Loss Orders. These orders prevent run-away losses by cutting your position at price levels pre-determined by you. Taken together, the above means that in any trade, one should have 3 price points in mind: 1) Entry Price 2) Take Profit Price

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3) Stop Loss Price These will allow you to derive your Risk:Reward. How we determine these 3 price points in a lesson for another day. “Plan a Trade, trade the Plan” – old market adage The best analysis and plans are pointless if one does not have the discipline to follow through on the plan.

Case Study: Long-term Capital Management (LTCM) If the fundamental tenet that “in order to make more than risk-free rate, one must take risk” holds true, is it possible to make money all the time as claimed by some trading operations?

The most famous hedge fund collapse involved Long-Term Capital Management (LTCM). The fund was founded in 1994 by John Meriwether (of Salomon Brothers fame) and its principal players included two Nobel Memorial Prize-winning economists and a bevy of renowned financial services wizards. LTCM began trading with more than $1 billion of investor capital, attracting investors with the promise of an arbitrage strategy that could take advantage of temporary changes in market behaviour and, theoretically, reduce the risk level to zero. http://www.investopedia.com/articles/mutualfund/05/hedgefundfailure.asp#ixzz3jEZAbrX2 A large hedge fund led by Nobel Prize-winning economists and renowned Wall Street traders that nearly collapsed the global financial system in 1998 as a result of high-risk arbitrage trading strategies. The fund formed in 1993 and was founded by renowned Salomon Brothers bond trader John Meriwether. LTCM started with just over $1 billion in initial assets and focused on bond trading. The trading strategy of the fund was to make convergence trades, which involve taking advantage of arbitrage between securities that are incorrectly priced relative to each other. Due to the small spread in arbitrage opportunities, the fund had to leverage itself highly to make money. At its height in 1998, the fund had $5 billion in assets, controlled over $100 billion and had positions whose total worth was over a $1 trillion. Due to its highly leveraged nature and a financial crisis in Russia (i.e. the default of government bonds) which led to a flight to quality, the fund sustained massive losses and was in danger of defaulting on its loans. This made it difficult for the fund to cut its losses in its positions. The fund held huge positions in the market, totalling roughly 5% of the total global fixed-income market. LTCM had borrowed massive amounts of money to finance its leveraged trades. Had LTCM gone into default, it would have triggered a global financial crisis, caused by the massive write-offs its creditors would have had to make. In September 1998, the fund, which continued to sustain losses, was bailed out with the help of the Federal Reserve and its creditors and taken over. A systematic meltdown of the market was thus prevented.

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http://www.investopedia.com/terms/l/longtermcapital.asp

Markets: Why they exist, nature and types What are Markets? Congregation of Buyers & Sellers, allowing: - Price Discovery - Volume Discovery - Risk Transfer (Hedgers & Speculators) - Performance of Obligations

Types of Markets Markets can come in various forms. On the 2 extremes of the spectrum are unregulated Over-theCounter markets and organised Exchanges. 1) Over the-Counter (OTC) Comparative lay-man example: Selling second-hand mobile phones in to a second-hand dealer shop. Going around to stores to find out the price value of the phone, and trying to get the best price. The problem with OTC markets is that there is limited price and volume discovery. This is not necessarily a bad thing, especially if you are the Price Maker, rather than the Price Taker. 2) Exchanges Comparative lay-man example: Taobao / e-bay is a platform where you can readily uncover the best prices and quantities that are up for sale or being sought after.

Existing between these 2 extremes are a host of other market structures, such as Dark Pools, Lit Pools, and ECNâ&#x20AC;&#x2122;s (Electronic Communication Network)

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Comparison between OTC and Exchanges Trading

Order Matching

Credit Risk Regulation

Arbitration

OTC Exchanges Opaque – Limited Price & Volume Enables Price and Volume Discovery, Discovery ensuring a “fair, orderly and transparent” market Counterparties find each other and Typically use “Price-Time Priority” match if they are satisfied with the matching, where earliest order at the price and quantities. Traders are “best” price gets matched first. This unlikely to end up trading at the promotes “fair, orderly and “best” price transparent” markets Bi-lateral Risk (between two parties) Treated as risk-free, because of Clearing process Non-prescriptive advice and Highly regulated with enforceable Rules guidance provided by association and penalties for non-compliance. bodies, such as the Singapore Foreign Exchange Market Rules come from the government Committee (SFEMC) and the regulator (e.g. MAS) and the exchange Association of Banks, Singapore (ABS) Bi-lateral legal recourse – e.g. Enforceable Rules and Arbitration International Swaps and Derivatives Committees bring about decisive and Association (ISDA) Master conclusive outcomes where disputes Agreement arise

Case Study: Ensuring Fair, Orderly and Transparent Markets, 14 Aug 15 -

SGX is warning investors to be mindful when dealing with shares from CEFC International (China-based firm) Recent buying in the company’s shares was concentrated in a small number of offshore accounts (it actually means that it is not a broad based buy, it’s only a few people buying and it could be an insider trading) The company’s shares climbed sharply to 36.5 cents on Aug 6, from 3-4 cents on Jul 10 These accounts accounted for more than 40% of the total traded volume during the period Reviewing CEFC’s announcement and the trades in its shares, and will work with relevant regulatory agencies to pursue action to maintain a fair, orderly and transparent market.

The graph of CEFC International shows a relatively flat graph for the last one year, however, for the last one month, the trading activity suddenly shoots. They also announces that the company loses money and yet the stock prices continue to shoot up. The SGX is coming into the picture to monitor the case to ensure fair & orderly market. If it is an OTC market, there will not be any control over scenarios like this. The purpose of SGX coming into the picture is to warn the public not to rush into buying the shares as the share price may drop drastically which will cause a deep impact to the public and economy.

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The Concept of Clearing “Clearing” enables the removal of Counterparty Risk. Whenever, Bank A trades with Bank B through an organised exchange, the exchange’s Clearing House substitutes itself as the counterparty to both Bank A and B. Hence, if Bank A defaults, Bank B is still able to receive its due payment through the Clearing House.

How does the Clearing House works? Clearing house is a department in the exchange and made up of Clearing Members. All members of the exchange are required to clear their trades through the clearing house after trades are transacted. Clearing Members charge a Clearing Fee to provide clearing services. In comparison, Trading Members have trading access to the exchanges and execute trades. A Financial Institution can be both a Clearing and Trading Member. In return, the Clearing Member has to guarantee performance on the trade they have cleared in event the end-client defaults on the trade. Common Bond System Should a Clearing Member be itself unable to meet the obligations of the defaulted trade, the other Clearing Members, held together by the Common Bond System, would need to collectively take responsibility for the defaulted trades and may the necessary payments to the opposite parties. There are further lines of defence, should the default losses be even greater than what the Common Bond can absorb. Typically the exchange itself would maintain funds, such as SGX’s fidelity fund, to provide further resources to ensure performance of obligations. The government would provide a final line of defence in the case of government-backed exchanges. These multiple layers of protection allow cleared trades to be treated as having zero counterparty risk. Margining Of course Clearing Members are not going to simply assume the end-client’s default risk without any form of collateral.

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The collateral comes in the form of Margins. Key terms in margining are: Initial Margin

Maintenance Margins

Margin Calls

Account Equity Daily Settlement Price

The minimum amount that must be deposited before initiating positions; usually expressed as a finite dollar value (or other currency) per contract traded. The amount of Initial Margin can be changed by the exchange at its discretion, in response to changing market volatility. Typically the amount of margin required would be the expected loss over the time till the next review (typically 1 market day) When account equity falls below this level, margin calls are triggered. Maintenance Margins are typically a percentage of the Initial Margin requirement. Demand issued by Clearing Members for more funds to guarantee performance of open positions. When Margin Calls are issued, the end-client will have to deposit fresh funds or reduce his existing positions till the account equity is equal to, or greater than, the Initial Margin requirements for the open positions. The unencumbered monies in the client’s account. Can be viewed as Cash – Marked-to-Market losses Used to “mark-to-market” positions to determine unrealised profit or loss

Trading on margin is also known as Leveraged Trading, and presents high risk, since the end-client is liable for the full notional amount of the investment, not just the margins he has on deposit. Market Risk means the end-client could experience severe slippage, or even be unable to exit positions, resulting in losses greater than initially anticipated.

How much margin should be required? That depends on how much the market price is expected to vary from the time of the trade to the time of the next mark-to-market. The historically observed volatility (known as Historic Volatility) and expected volatility (known as Implied Volatility) would give a starting level for margins. If volatility is determined to be 5% for the time period required, then Initial Margins should be at least 5%. Collecting more than 5% would provide greater protection, but collecting too much margin would make the instrument capital intensive, and push clients to competitors who might offer lower margin requirements.

Another form of “margining” is often found when trawling the net; this is in the way of share financing. For example, the end-client deposits 15% of the notional amount, while the financial institution offers financing for the other 85% at a fee. This enables the end-client to trade a larger notional amount that he would otherwise have been able to, and the financial institution earns interest on the loan as well as commissions on the larger notional. This form of margining is typically offered by intermediaries and brokers, and not the approach used by the Clearing House.

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Why canâ&#x20AC;&#x2122;t end-clients trade directly into Exchanges? As discussed in class, one of the value propositions of exchanges is that they facilitate performance of obligations arising from trades, and remove counterparty risk through the clearing process. In order to remove counterparty risk, the clearing house, formed by Clearing Members, who are held together under the Common Bond System, collects margins. Margins are collected from trading members, who collect from their end-clients. Of course a trading member might trade on a proprietary basis, in which case there is no further end-client, and the trading member would post the required margins themselves. Basically, the beneficial owners of the trades, the parties that are directly exposed to, and responsible for, the losses that might arise from the trade, would be the ones posting the margins. So think about it as a concentric circle, with the exchange in the centre, surrounded by clearing members, and with the end-clients on the outer ring. The exchange thus recognizes the clearing members, who in turn recognize their trading members and the end-clients of the trading members. Since an exchange only recognizes the clearing members as described above, it would not allow endclients to trade directly into the exchange. What appears to be, or is marketed as "direct access", really still routes orders through a clearing member's risk control and/or a trading member's market architecture, even if on a post-trade basis. Industry Developments: Dodd-Frank Act In the wake of the AIG crisis, the Dodd-Frank Act was introduced, requiring US banks and counterparties dealing with US banks to clear their long-dated OTC trades on a Central Clearing Party (CCP). This was done to remove the bi-lateral credit risk, but encumbered the counterparties with the need for margining activities, which increases their capital requirements and financing costs. Estimating margin requirements present another challenge as one would not know how much margin would be required over the life of the position.

So why do Financial Markets Exist? Financial Markets exist for 2 main reasons. 1) Raising Capital 2) Exchange of products and financial instruments This is facilitated by Market Participants who perform different roles and functions, primarily: a) b) c) d)

Hedgers Speculators Intermediaries Regulators

Raising Capital Copyright ÂŠ EpitrainTM 2015

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Companies need capital to run their businesses. Capital can be raised: -

Via the Money Markets, where capital/money is lend and borrowed interbank

By issuing Bonds Bonds are contractual obligations; essentially loans from the investors to the company, which pay obligatory interest (termed coupon).

By issuing Shares Share gives a shareholder part ownership of the company. Shareholders have the voting rights and pay out discretionary dividends, depending on the performance of the company.

Bond and Shares can be issued OTC or on an Exchange. Consider: Can you articulate the benefits and drawbacks of listing on an exchange? On exchange, -

Primary Markets are where the issuers raise capital by offering its shares & bonds to the public by way of Initial public offerings (IPO) Secondary Markets are where all subsequent transactions take place; the transfer of instruments and provision of liquidity to investors. This allows investors to exit their investments and recover their capital for other use

5) Foreign Exchange Easily understood, Foreign Exchange, or FX as it is commonly called, is the exchange rate between 2 currencies. The rates we commonly see on the financial news wires are “Spot Rates”. Spot Rates are the exchange rates applicable for trades where the Settlement, or the actual payment and receipt of the 2 currencies, occur (usually) 2 business days after trade date.



Direct vs Indirect Quotes (aka Crosses) Most of the major exchange rates are quoted by banks continuously and readily available in the market, such as the USD/JPY and the USD/SGD. However, there are times when we require an exchange rate that is not readily quoted, such as the SGD/JPY. In these cases, we can use the available rates to derive the rates we require. For example, if the USD/JPY is 124.15 and the USD/SGD is 1.3545, we would be able to derive the SGD/JPY rate by dividing the USD/JPY by the USD/SGD, hence giving us a rate of 91.6574. We would typically round exchange rates involving the JPY to 2 decimal places, hence 91.66.

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Other currency pairs would typically be rounded to 4 decimal places.

Exchange Rate Regimes Exchange Rate Regime Fixed Rates Managed-float Free-float Currency Controls

Examples BND (Same as SGD) HKD (7.75-7.85) (Moves within the range) USD, GBP, EUR KRW,PHP,TWD

Foreign Exchange (Forwards) Of course we can agree an exchange rate today, but only settle the trade some time in the future, say in 3 months, time. Such deals are called “Forwards”. Forwards is presented here with FX as the underlying asset class, but the principles are applicable to all other asset classes, such as equities and commodities. Forwards are bilateral contracts in which one party agrees to buy (or sell) an agreed quantity of a specified underlying asset, at an agreed price. The contract is entered into today, but the receipt of the underlying, and payment for such, only take place in a future date as agreed in the contract. The main idea about trading Forwards is that the Trader gets to secure today’s rate. This allows Hedgers to “lock-in” today’s known rate, and hence allows them to avoid the unknown and uncertain rates of the future. For example, a Singapore-based importer might order goods today from a US-based supplier, which are to be delivered and paid for in 3 months’ time. The USD/SGD exchange rate for today (value Spot) is readily known, but the rate in 3 months’ time is obviously a random variable. As such, the Singapore importer might be interested to secure the USD/SGD rate today, so that he can derive an appropriate selling price for his goods. To do this, he would want to enter into a Forward deal with a bank, where he would receive USD in 3 months’ time (using that to pay his US Supplier) and deliver SGD to the bank in return (which he gets from the sale of the goods).

Of course the rate available today is the Spot rate, and hence to be settled in 2 days, but the actual exchange of currencies between the Importer and the bank only takes place in 3 months’ time. Given the concept of Time Value of Money, there is interest involved, and the Importer and the bank would have to compensate each other for the interest that they would have earned, had the currencies they were to have respectively received been paid in 2 days’ time.

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This results in an adjustment to the Spot rate. This adjustment is called the â&#x20AC;&#x153;FX Swap rateâ&#x20AC;?. Hence we can say that Forward rates are Spot + FX Swap rate. Swaps can be from Spot out to any date, or even from a forward date to another forward date (termed Forward-Forwards)

To derive the FX Swap rate we use the formula presented below. Key to this is the concept of Base and Quoted currencies. CCY Pair USD/SGD USD/JPY GBP/USD EUR/USD GBP/JPY

Rate 1.3545 125.15 1.5057 1.3022 188.44

Base 1 1 1 1 1

USD USD GBP EUR GBP

= = = = =

Quoted 1.3545 125.15 1.5057 1.3022 188.44

SGD JPY USD USD JPY

Hence we see that the Base currency is the constant â&#x20AC;&#x153;1â&#x20AC;? and the Quoted currency is the currency with the varying quantity, based on the exchange rate. Consideration: The GBP/JPY rate was derived from the USD/JPY and GBP/USD rates in the table. Are you able to derive the same? Are you able to derive the other possible crosses?

Deriving FX Swaps The formula for deriving the FX Swap follows. Key to applying the formula is to determine the Base and Quoted currency, and then applying the appropriate interest rate for the tenor. FX Swap =

(đ?&#x2018;&#x201E;đ?&#x2018;˘đ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x2018; đ??ź/đ?&#x2018;&#x2026;â&#x2C6;&#x2019;đ??ľđ?&#x2018;&#x17D;đ?&#x2018; đ?&#x2018;&#x2019; đ??ź/đ?&#x2018;&#x2026;) Ă&#x2014;đ?&#x2018;&#x2020;đ?&#x2018;?đ?&#x2018;&#x153;đ?&#x2018;Ą Ă&#x2014;đ?&#x2018;&#x2021;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;&#x153;đ?&#x2018;&#x; đ?&#x2018;Ťđ?&#x2019;&#x201A;đ?&#x2019;&#x161; đ?&#x2018;Šđ?&#x2019;&#x201A;đ?&#x2019;&#x201D;đ?&#x2019;&#x160;đ?&#x2019;&#x201D; Ă&#x2014;100

Worked Example Market Data: EUR 4s = 2.5%, USD 3s=1.0% USD 6s= 1.15%, Spot= 1.3235 What is the EUR/USD 4-month Forward? Notice we donâ&#x20AC;&#x2122;t have information about the 4-month USD deposit rate. We can however derive a good estimate of that by interpolating between the 3-month and 6-month rates. FX Swap =

(1.05â&#x2C6;&#x2019;2.5) Ă&#x2014;1.3235 Ă&#x2014;122 = 360 Ă&#x2014;100

-0.00650353

EUR/USD 4-month Forward = 1.3235 + (-0.006503) =1.313699647

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=1.316996 Notice that the FX Swap in this example carries a negative sign. We call this a “discount”. Swaps can also come at a “Premium”, meaning they carry a positive sign. FX Swaps – 2 legged Strictly speaking, a FX Swap involves 2 legs, a buy and a sell leg, either of which could be the near leg or the far leg. In the above example, the FX Swap would involve a near-leg (value Spot) and a far-leg (value 4-months). If the Spot trade was to buy EUR/USD 100,000, then the nearleg of the swap would be to sell EUR/USD 100,000 and the far-leg of the swap would be to buy EUR/USD 100,000. The buy and sell value Spot would close out the position, and hence there would be nothing left to settle on value Spot. The remaining far-leg will be settled when it comes due (i.e. in 4months’ time in this example).

EUR -100,000

100,000

EUR 100,000

Spot (1.3235)

USD -132,350 +132.350

4-months USD (1.316996) -131,699.60

FX Swap legs in blue

Application on other Asset Classes As discussed, the concept of Time Value of Money being used to adjust the Spot rate to a Forward rate can be applied to other asset classes as well. We will of course need to change the factors that are considered in the Cost of Carry. The Cost of Carry is the cost of pushing the settlement from value Spot to a date in the future. Depending on the asset class in question, the component factors in the Cost of Carry would differ, but the idea of paying or receiving the differential, just as with the interest differentials in the FX Swap, remain. Asset FX Forwards

Spot +

Cost of Carry Interest Differential between currencies

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Equity Forwards Commodity Forwards

Interest â&#x20AC;&#x201C; Dividends Interest, storage, insurance, delivery costs

What makes a Forward a Future? Futures are contracts that are traded on exchange. They are traded today, with a settlement date somewhere in the future. The settlement date is not freely negotiated between the counterparties, but rather fixed by the exchange in the Contract Specifications. Contract Specifications laid out the pre-determined details of the contract, such as contract size, tick size, delivery date, etc. Standardization of contract specifications make it easier for buyers and sellers to trade, since there are fewer variables to negotiate. But that also means that the Futures contract would unlikely fit the exact size and date requirements, especially of Hedgers. The advantage of standardized contracts is that there would be typically be greater liquidity, since buying and selling interest is not split across 21 possible value dates in a month, but rather concentrated in one delivery date. It also makes it harder for counterparties to guess which side you are on (whether you are a buyer or a seller); imagine if you had bought USD/JPY 137,565.27 for value date 23Sep. If you again request a price in that quantity and that date, it would be easy for counterparties to guess that you would be selling this time round to square your position. This would not be the case if you had bought 1 contract of USD/JPY Futures, and then request for a price in 1 contract again, especially if the exchange does not publish your identity, but only your interest to trade 1 lot (which is typically the case). Market Participants and Roles Speculators deliberately take positions in the hope of making profits. They usually take large risks by anticipating future price movements, in the hope of making quick, large gains. Hedgers prefers to make an investment to reduce the risk of adverse price movements in an asset. E.g.: It takes 9 months for farmers to harvest the wheat. The farmer hopes to sell the wheat at $8. As the price of wheat can fluctuate in 9 months, the farmer hedges his price risk by agreeing to sell at $8 now and deliver the wheat in 9 monthsâ&#x20AC;&#x2122; time (essentially a Forward or a Futures Contract) to prevent the risk of price drops in future. The farmer will no longer be concerned over prices fluctuations in the future. The buyer who buys the wheat at $8 from the farmer could be the speculator, hoping that he/she buys at $8 and then sells the wheat at $12 to make profits out of it.

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Originators – People who make the buy-sell decisions. E.g.: Hedgers and Speculators, such as Prop Traders; empowered to make the buy/ sell decisions. They trade for the bank’s P&L or their own account Intermediaries – Brokers; connect Buyers and Sellers; usually not allowed to make money off Bid/Ask Spread, they collect brokerage / commission. Market Makers – quote Bids and Asks into the market continuously, thereby providing liquidity, with the aim of making the Bid/Ask Spread as other market participants buy from, and sell to, them. Being a market maker in OTC is probably more beneficial compared to being a market maker on an Exchange. This is because there is limited price and volume discovery in the OTC markets, which allows the market maker to quote any price to the client which profits the market maker the most. On an Exchange, there is transparency in price and volume and clients are aware of the prices. This means Market Makers are unable to quote wider prices (i.e. prices with wider bid-ask spreads) or “shade” prices (i.e. quoting lower prices when the Market Maker believes the client is a seller, or higher prices if he believes the client is a buyer) Consider: Why would the Market Maker quote higher or lower prices? How does it profit him to do so?

Product Structurers – create complex investment structures by combining derivative instruments. See Appendix “UBS 15-Month SGD Daily Memory Knock Out Discount Purchase Notes” as an example of a structured product (usual disclaimer about trade recommendations apply)

Market Basics What is an order? An instruction from a client or prop trader to execute in the market. An order consists of direction (buy or sell), quantity, instrument, price, and validity. Difference between order & trade? A Trade is an order that has been executed.

Buy & Sell are trade actions Long & Short are Positions

Common Matching Algorithms 

Price-Time Priority – Best Price gets filled first, first order in the queue at the Best Price level gets filled first (The best price gets the hit first. Once the best price gets filled, the process of first come first serve of the same price applies) Example: Trader A is the first to bid at $50, Trader B then comes along to bid at $60, Trader C is the last to bid at $50. An incoming Sell Market Order will hit Trader B’s order first, as it has the best

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price of $60. Once that order is filled, the sell order will hit Trader A’s $50 bid, then proceed to Trader C’s $50 bid.



Pro-Rata – Orders are given fills in proportion to their quantities. Price priority still holds (Pro-Rata is introduced to overcome price-time priority.) Example: Assume there are only the following 2 bids in the market: Order ID 123566 137786

Price $50 $50

Quantity 9,000 1,000

Time 09:10:51 09:12:12

An incoming Sell Market Order, with a quantity of 500 arrives at the exchange. Under Price-Time, OrderID 123566 would get all 500 lots and continue to work the balance of 8,500. OrderID 137786 will still be queuing behind, and probably have to wait a rather long time to get any fills. Pro-rata enables every bid to get a portion of the incoming order. The quantity that each bid is assigned is determined by the bid’s proportion of the total bid quantity. Hence in this case, OrderID 137786 would get 10% of the incoming 500. 

Market Maker Allocation – designated Market Makers are given fills ahead of other participants, even if those participants are ahead in the queue time-wise. Price priority holds. Example: Assume there are only the following 2 bids in the market: Order ID 123566 137786

Price $50 $50

Quantity 9,000 1,000

Time 09:10:51 09:12:12

OrderID 137786 belongs to a Market Maker, and the percentage allocated to Market Makers is 50%. The incoming Sell Market Order for 500 lots will be split in this manner: Step 1: Market Maker’s OrderID 137786 is allocated 250 lots. Step 2: The remaining incoming order’s quantity could then allocated based on Price-Time or Pro-rata, depending on what is set up in the trading engine. If it is Price-Time, then the remaining 250 lots will be assigned entirely to OrderID 123566. If it is Pro-rata, 90% of the remaining 250 lots, that is 225 lots, will be allocated to OrderID 123566, and the balance 25 lots will be allocated to OrderID 13776 (giving the Market Maker a total of 275 lots)

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These matching algos can only operate on an organised exchange, or some other form of centralized market place such as an ECN. If trading was occurring on an OTC market, the buyer and seller would be contacting each other directly and exclusively; as such, there would be no opportunity for any of the matching algo's to be in force. Typical set-up in a Financial Institution Front Office: handles trading and client-facing matters, such as portfolio management and advisory services. Middle Office: manages risk by implementing position & loss limits. In order to prevent concertation risk, banks will impose counterparty limits which prevent the bank from trading excessively with a single counterparty and risking a huge loss of principal should the counterparty default. Back Office: handles Reconciliation, the process of confirming trades ensure accuracy of trades and positions, and Settlement.

1) Financial Market System Functionality We have seen how trading operations are typically organised into Front-, Middle- and Back-Offices. Here we explore the key functions within the financial market systems that each of these Offices use.

Front Office Duties Uses the Order Management System. ď&#x201A;ˇ

Handles incoming orders from clients and executes trades

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

Develop trading strategies



Client management



Continuous monitoring and reporting of market conditions including analysis such as technical & fundamental analysis

Roles and Motivations – Front Office Roles

What do they do

How do they profit

Prop Traders

Make Buy-Sell decisions

Profit from taking Directional Risk

Market Makers

Quote 2-way prices continuously, provide liquidity to the market

Profit from Bid-Ask Spread

Sales Dealers

Entice end-clients to trade

Earn Commissions and Mark-ups

Product Structurers

Compose structurers to create complex exposures from clients

Earn Mark-up and Commissions, often built into the structure and not visible to the end-client

Arbitragers

Aim to simultaneously buy and sell highly correlated instruments

When one is mis-priced, the paired trade locks-in a profit

Key functions of Order Management Systems 



Market Watch (aka Price Screen / Market Data / Price Window) o

Display “best” prices and quantities from the market (aka top of the book)

Displays “Last” traded price

Displays other market information, such as High, Low, Previous Close, time-stamps,

etc. Market Depth o Displays prices beyond the “best” bid and ask. o Reveals important information about liquidity, but takes up space Position Window o

Displays Open Positions

Displays realised and marked-to-market P&L

Order Book o

Provides an order entry interface

Provides an interface to view, amend and cancel working orders

Provides a view of executed orders (partially and fully executed)



Order Parameters Orders sent would have the following components:  Direction  Quantity

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    

Instrument Price Validity Order Type

Order Types  Limit Orders – These are the “normal” type of orders, where the Trader is attempting to buy at a price below current market prices, or sell at a price higher than the current market price. Limit Orders specify Quantity and Price of the order.  Market Orders – This order type will seek out the best available price or prices that will fill the specified Quantity. There is no “price” specified for Market Orders.  Stop Order – a type of order that only becomes active when the trigger price is reached; used for preventing run-away losses (hence the name), or to establish positions in a “break” strategy. Apart from the Trigger Price, Stop Orders would need to have a secondary price field for the “triggered” order and a Quantity. For example, where the current market price is $50, a trader who is already Long 100 shares of Company X might want to sell off his holdings to prevent run-away losses if the market falls below the Support he has identified at $48. If the Trader were to enter a Sell Limit Order with a Quantity of 100 and a Price of $48, the trading engine would interpret things as the Trader wanting to sell at a price of $48, or better. Since the current market price is $50, the trading engine would determine that the current market price represents a “better” fill, and hence would execute the order now, thus squaring off the Trader’s position at a price of $50, which is not what the Trader wants. By entering the order as a Stop Order with a Quantity of 100 and a Trigger Price of $48, and a secondary price of $46, the trading engine would know that the order is not to be executed until $48 trades. Thus it won’t execute the order now. Instead it will watch and wait till there is a $48 that appears as the “Last” traded price. Only when that happens, it will execute the order. Let’s assume the market depth at the time the Stop Order is triggered is as follows: Bids $48 bid x 40 shares $47 bid x 30 shares $46 bid x 20 shares $45 bid x 15 shares

Asks $49 ask x 50 shares $50 ask x 80 shares

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Given that the secondary price is $46, once the Stop Order is triggered, it will first sell the 40 shares at $48, then sell the 30 shares at $47, and sell the 10 shares at $46. At this point it would have sold 90 shares only; but given that the secondary price is essentially a “Limit” order with a price of $46, the trading engine would not sell at prices below $46. Instead, it will offer the remaining 10 shares at $46. Hence the market depth, after the triggering, would look like this: Bids $45 bid x 15 shares

Asks $46 ask x 10 shares (remainder from the Stop Order) $49 ask x 50 shares $50 ask x 80 shares

Let’s consider the outcome had the Stop Order had been entered as follows: Trigger = $46 Secondary Price = market Quantity = 100 shares In this case, when the Stop is triggered, it would execute as a Market Order; it would thus clear the bids from $48 down to $46 as described above, selling a total of 90 shares, but in this case, since it is a Market Order without any “Limit Price”, it would also sell 10 more shares at $45 in order to fill the full quantity of the order. The market depth after the triggering would thus appear as: Bids $45 bid x 5 shares

Asks $49 ask x 50 shares $50 ask x 80 shares

There is often confusion amongst new Learners between Stops and Limits. I don’t like to present the following table because it encourages memorization rather than understanding, but Stops and Limits can be viewed as follows: Buy Order Sell Order Buy Stop Sell Limit Current Market Price Buy Limit Sell Stop



Iceberg Orders Iceberg Orders are an order type, typically provided by the OMS rather than the exchange’s trading engine, which splits a large order into smaller orders to conceal the “real” volume.

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E.g.: Trader X wants to buy 10,000 lots of December Crude Oil Futures at $50, however, by sending the large order at once may cause a stir in the market and Sellers may pull their existing offers from the market and instead offer at a higher price. In order to avoid this situation, Trader X would break the order up, and enter smaller volume orders into the market, in an attempt to make these look like ordinary little, unthreatening, trades. The immediate problem is that Trader X will have to stay at his trading terminal, keep count of the number of lots executed, and continually enter new orders when the existing one is filled. The longer term problem is kidney stones. Iceberg functionality on the OMS will allow Trader X to enter the order as follows (or in some such format): Instrument: CLZ5 Price: $50 Order Quantity: 10,000 Shown Quantity: 25 This would mean the OMS sends orders for 25 lots a time, and when the order is fully executed, sends another order for 25 lots. The OMS would keep track of the total quantity executed, and stop sending new orders when the total quantity has been executed. Pros It prevents other market participants from getting spooked and pulling their orders, or front-running your large order. Cons Each slice of the order is essentially a new order being received by the trading engine, and as such, gets assigned a place at the back of the price queue. This means other orders at the same price would get filled before your next slice. Of course over time, market participants could come to realise that there always is a new order being entered for 25 lots at $50, and begin to suspect the existence of an Iceberg. In order to verify their suspicions, they could sell 1 lots against the 25 lots order and see the counterparty (assuming this is a market that broadcasts counterparty information. OTC Aggregators would typically do this, given the need for settlement; Exchanges may or may not broadcast such information) When the next 25 lot order appears, they would sell 1 more lot. If the counterparty name that comes back is the same as the first one, you will be able to save the Titanic. Once it is known there is an Iceberg, they might consider front-running the Iceberg.

Consider this: how would you build an algo to detect the Iceberg, front-run the Iceberg when detected, and manage your position, both from the angle of taking

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profit, as well as detecting the Iceberg is no longer there (either fully executed or cancelled), and managing your position then.



Order Validities  Day Order – automatically purged if not executed by the end of the trading day. Care should be taken when in dealing with systems that handle orders for markets that close after the calendar day. For example, if a particular instrument closes its trading session at 0200h Singapore Time, a “day order” should remain valid until 0200h. Care should be taken that the OMS or Trading Engine does not cancel the order at midnight. Orders are typically treated as Day Orders by default if no other validity is specified. 

Good-till-Cancelled (GTC) – As the name implies, these orders remain valid until they are cancelled. The system would automatically reload the order the next day, if it has not been fully executed. This means the Trader does not need to wake every morning and participate in a game of “fastest finger first” to get his order into the trading engine ahead of others, to secure a place in the front of the queue. The drawback of this is that should the Trader forget about the order over time, he might end up with an unwanted position if it gets filled.



Good-till-Date, Good-till-Time – variations of GTC, these orders stay “live” till their specified date or time. As with Day Orders, care should be taken with when GTD orders expire on a specified date (calendar date or trading date). Fill-Or-Kill (FOK) – sounding like a Brit being rude, this order requires that it be immediately fully executed, or else cancelled. It does not join the queue and get worked, and it does not accept partial fills.



Example: The only Ask on the market is $50 for 100 lots FOK order to buy 150 at market is entered. The result is nothing traded, since the entire quantity specified on the order cannot be filled. 

Fill-And-Kill (FAK) – sounding like an Ozzie being rude, this order requires that it be immediately filled for as much of the specified quantity as possible, and cancel the remainder which cannot be filled; it does not join the queue and get worked. Example: The only Ask on the market is $50 for 100 lots FAOK order to buy 150 at market is entered. The result is 100 lots traded, and the remaining 50 lots cancelled.

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Middle Office Duties Uses the Risk Management System 

Risk Managers: administer limits such as position limits, loss limits and counterparty limits



Valuation Specialist, Quants: determine the fair value of derivatives for mark-to-market activities, determine VAR, correlation and other risk measures



Monitors Traders’ P&L

Key functions of Risk Management Systems 

calculation of marked-to-market P&L, VAR and other market risk measures



Provides an interface for Risk Managers to: o

set Position and Loss Limits for their traders

configure allowable instruments for Traders to trade

set Counterparty Limits

Blacklist Traders who might have violated any prescribed Limits or executed unauthorised trades.

Back Office Duties Uses Clearing & Settlement Systems 

Settlements Specialist: perform reconciliation activities; ensure accurate and timely payment and receipt of funds, securities and instruments



Clearing Specialist: attend to clearing activities such as posting trades onto the exchange, novation of trades and margin matters

Key functions of Settlement Systems 

Send and receive trade confirmations



Perform trade reconciliation between tickets from the Front-Office and Confirmation Slips from the counterparty’s Back-Office



Make appropriate payments, which might be on a Gross basis or net basis (through a Netting Agreement)



Track receipt of appropriate payments

Key functions of Clearing Systems Interfaces with Clearing House’s system to: 

receive margin requirement information (Initial Margin, Maintenance Margin)

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

receive trade information from the Clearing House



receive margin calls from the Clearing House

The system will also typically perform margin related functions for the margin customers of the bank 

perform margin requirement calculations for its end customers



monitor margin collateral of its end customers



issue margin calls to its end customers, and monitor responses

2) Limits We discuss the 3 main types of Limits. 1) Position Limits Position Limits are obviously important as they prevent the Trader from establishing position sizes in excess of pre-determined limits set by the Middle Office. A large position, while magnifying profits, would also magnify losses, and could lead to situations where the bank loses more money than it is prepared to. Position Limits should be designed such to automatically prevent the build-up of positions in excess of the allowed size. This removes the operational risk of having the Traders restrict themselves, or the Risk Managers monitoring the Traders’ position in real time and taking action should a position limit be breached. Indeed taking action post-trade might be too late, if the market gaps away overnight or crashes suddenly as we saw in the Flash Crash. To eliminate the risk of breaching Position Limits in event of a gap, the system should take into account working orders, and not just trades. In the case of Sarao Hedge, we can surmise that either the trading system employed by Sarao did not take into account working orders, or that Sarao had a really large margin account. 2) Loss Limits Working together with Position Limits, Loss Limits prevent the Trader from running his positions beyond the point where his losses exceed the amount of risk capital that is granted to him. Loss Limits would have to take into account both the Trader’s realised and unrealised profits and losses.

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Having hit the Loss Limit, the system should automatically prevent the Trader from entering new orders. Some systems might allow the Trader to enter orders that would close off his remaining open positions. Other systems might simply lock the Trader out entirely, and rely on the Chief Dealer or Risk Manager to close out the positions.

3) Counterparty Limits Counterparty Limit prevent a Trader from establishing too large a position with a given counterparty. This is especially meaningful in the OTC markets. On exchange, trades are cleared and margined through the Clearing House, and hence are typically treated as having no counterparty risk. The motivation behind Counterparty Limits is that you donâ&#x20AC;&#x2122;t want too large an exposure to a given counterparty, so that in the event that counterparty defaults, you will not lose as much. It is the same concept as diversification. 3) Settlement Risk and Solutions Counterparty risk, also known as Default Risk or Settlement Risk, can be broken into 2 parts: a) Pre-settlement Risk This is the risk that the counterparty defaults BEFORE you reach the settlement date. In this case, you have not paid the counterparty. The default means that the trade agreement you had with the counterparty no longer exists. You can still go find another counterparty and replicate the trade at prevailing market prices. However the prevailing market price would likely be different from the original price you traded at, and hence you could be facing a loss. But still, the loss is only the price difference. b) Settlement Risk In this case, the counterparty defaults AFTER you have paid him. Read the infamous case of Herstatt Bank. Having received the full traded amount of Deutschemarks from its counterparties, Herstatt Bank declared a default, before paying out the corresponding US Dollars to its counterparties. This meant that the affected counterparties were facing the possibility of losing the entire principal amount they paid, instead of just a price differential as in the case of pre-settlement risk.

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By having counterparty limits in place, the Middle Office is able to prevent a concentration of exposure to a given counterparty. The limits can be broken into Counterparty Exposure Limits (addressing Pre-settlement Risk) and Counterparty Settlement Limits (addressing Settlement Risk)

Other Possible Solutions Margining Another solution is to margin all position. Whilst clearing and margining was traditionally the purview of exchange-traded instruments, following the 2010 crisis, the US government enacted the Dodd-Frank Act, requiring all US banks to post their trades in longer dated instruments (such as Interest Rate Swaps and Long-dated FX Forwards), even if traded OTC, onto a Central Clearing Party (CCP) for clearing and margining. Whilst only legally enforceable on US banks, the Act has had global impact, in that it required US banks to ensure that their counterparties, regardless of nationality, similarly clear their leg of the trades. Other regulators have also implemented similar requirements, inspired by Dodd-Frank. This has given rise to the new segment of “OTC Clearing”.

Netting Agreements This nets all payments between counterparties, thereby reducing the notional amounts that need to be paid to each other. Let’s assume the following trades have been done between Bank A and Bank B Trade

Bank A

Bank B

Buys USD/JPY at 120.00

Sells USD/JPY at 120.00

Buys USD 1,000,000

Sells USD 1,000,000

Sells JPY 120,000,000

Buys JPY 120,000,000

Sells USD/JPY at 122.00

Buys USD/JPY at 122.00

Sells USD 2,000,000

Buys USD 2,000,000

Buys JPY 244,000,000

Sells JPY 244,000,000

Buys USD/JPY at 121.00

Sells USD/JPY at 121.00

No. 1

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Buys USD 1,000,000

Sells USD 1,000,000

Sells JPY 121,000,000

Buys JPY 121,000,000

Without Netting, at the JPY Settlement Cycle, Bank A would have to deliver JPY 241,000,000 to Bank B, and expect to receive JPY 244,000,000 from Bank B. This results in both banks requiring JPY to make the payments, and exposes them to each otherâ&#x20AC;&#x2122;s default.

With Netting, Bank B will simply pay JPY 3,000,000 to Bank A.

Trading Strategies 6) Trading Strategies Foreign Exchange (Strategies) There are many different Trading Strategies. Indeed Traders and Financial Engineers are dreaming up new ones all the time, but they all fall into the following key categories. 1) Hedge 2) Carry Trades 3) Speculative Directional 4) Arbitrage Hedge The idea of a hedge is that the Hedger has an existing underlying position, arising from some other activity, such as in the case of the Importer discussed above, and he wants to neutralize that risk.

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He thus executes a trade in the financial market to neutralize his pre-existing risk. This is in contrast to Speculators, who execute trades in order to create exposures for themselves deliberately. Example of hedging with a Forward: Extending our example of the Singapore-based Importer above, let us assume USD/SGD is presently at 1.3500, and the Importer has ordered goods worth USD 100,000. He has priced his goods to sell for a total of SGD 140,000. Based on today’s Spot rate, that would give him a profit of SGD 5,000. However, he does not have SGD to convert to USD at present. He will only have sufficient SGD in 3months’ time when he receives payment from some other deals he has made. This means he cannot buy USD for value Spot. He can only buy USD in 3 months’ time. If in 3-months’ time, the USD/SGD rate remains the same at 1.3500, then his expected profit would be the same; SGD 5,000. However if the USD/SGD rate rises to 1.4500, this would mean he would have to use SGD 145,000 to obtain the USD 100,000 he needs to pay his supplier. Given that he only gets SGD 140,000 from the sale of the goods, he will end up with a net loss of SGD 5,000. If USD/SGD rates fall to 1.2500, he would be able to obtain the required USD for SGD 125,000, thereby increasing his profit to SGD 15,000. As you can see, the Importer’s risk is that USD/SGD goes higher. Of course he stands to gain if the rate drops; but he is an Importer, not a FX Speculator, so he is not in the business of taking on FX risk for profit. He would rather secure a certain USD/SGD rate now, and know what profits he makes from this Import business, than hold on to his inherent FX risk, even though it might result in him making even more profits (with risk, comes profit potential; the other way to look at it is that with profit potential comes risk).

In order to hedge his exposure, he might choose to buy a USD/SGD Forward for USD 100,000. Let’s assume the FX Swap works out to be +0.0005 (a “premium”). With Spot at 1.3500, the Forward rate would thus be 1.3505. This means in 3 months’ time, he will have to pay SGD 135,050 to obtain the USD 100,000 he requires. He does end up with a slightly lower net profit from the sale of his goods (SGD 4,950 with the hedge instead of SGD 5,000 based on current Spot rate), but he has removed the risk of FX fluctuations. Carry Trades This is a strategy in which the Trader borrows in a currency with a low interest rate, and then converts it into the currency that he requires, which has a higher interest rate. For example, a property developer in Indonesia might be faced with an interest rate of 7.00% for a 1year loan when borrowing in Indonesian Rupiah (IDR). He deems this to be too high a cost of capital. He might instead borrow JPY, where the interest rate charged to him is just 0.50% for a 1-year loan. This means a 6.50% savings in interest expense.

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However, the capital he has raised in in JPY, but he needs IDR to operate. As such, he will convert the JPY into IDR by selling Spot JPY/IDR. This sequence of borrowing in a currency cheaper than the actual currency you require, and then converting it to the required currency, is called a Carry Trade (since you are benefiting from the cost of carry).

Consider this: 1 year later, he would have to convert IDR back to JPY to repay the JPY loan. We would not know what the Spot JPY/IDR will be in 1 year’s time. As such, the Carry Trade presents FX risk. This should come as no surprise, since the fundamental tenet that “in order to make more than riskfree rate, one must take risk” holds true; hence the 6.50% “profit” he earns from the interest differential must result in some kind of risk. Sure, he could try to hedge his FX risk using FX Forwards, as with the case of the Singapore Importer, but given the nature off interest rate differentials of FX Forwards, as reflected in the formula, would this really work?

Directional This is the most basic of trading strategies, going Long if one expects the market to head higher and going Short if the direction is expected to be heading lower. While directional trading requires the trader to have a strong view about the market, the Trader also needs to have a risk mitigation strategy in place to protect his capital in the event of a move in the opposite direction from what he expected. To arrive at his directional view, the Trader could employ Fundamental or Technical Analysis. Typically Technical Analysis is preferred in the short-term, because markets can take a long time to arrive at their fundamental equilibrium price. Technical Analysis also gives the Trader a clear Entry, Take Profit and Stop Loss price. These in turn allow the Trader to determine the Risk:Reward ratio as we have explored in the earlier lectures. Arbitrage A strategy which a trader takes advantage of the temporary mis-pricing of highly correlated instruments. Arbitrage involves the near simultaneous buying and selling of instruments to lock-in a profit. With Arbitrage, there are several sub-strategies. Arbitrage could be Geographic, across Fungible contracts, or Relative Value strategies as discussed in Lecture 3. In FX trading, arbitrage can occur between 3 currency pairs which share the same Base and/or Quoted currencies. Copyright © EpitrainTM 2015

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The table below shows an arbitrage between the USD/JPY, the GBP/USD and the GBP/JPY. Assume the following Spot rates are being quoted in the market. USD/JPY GBP/USD GBP/JPY

Bid 125.02 1.5047 188.25

Ask 125.05 1.5052 188.32

To arbitrage the market, the Trader would execute the following trades. Trade (Value Spot)

USD

JPY

Buy 1 mio USD/JPY at 125.05 (JPY1)

+1,000,000

-125,050,000

Buy GBP/ 1 mio USD at 1.5052 (1GBP)

-1,000,000

Sell GBP/JPY at 188.25 Net

GBP

+664,363.54 +125,066,436

-664,363.54

+16,436

Speed naturally would be of essence to ensure that he gets all 3 trades (often called “legs”) executed before the market moves, as discussed under Legging Risk in Lecture 3.

4) Arbitrage Arbitrage involves the simultaneous buying and selling of correlated instruments, with the objective of locking in a profit. The whole idea is that price differentials are temporary, and that they will return to their normal equilibrium soon.

Common forms of arbitrage include: 

Geographic arbitrage: where the same instrument is traded in different locations. Small, momentary price differences between the markets can provide arbitragers with the opportunity to exploit the price differentials. For example, Nikkei 225 Futures Contracts, which take their Final Settlement Price from the underlying Nikkei 225 Cash Index, are traded on SGX and the Osaka Stock Exchange (OSE). These are essentially the same contract, and while their prices would move in tandem, they may not be moving in lock-step. This could give rise to a situation where the SGX contract might be trading at 18,250 while the OSE contract is trading one tick higher at 18,255. By simultaneously buying the SGX contract and selling the OSE contract, an arbitrager will be able to lock in a 1 tick (5 index points) profit.

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Of course whether this is worth his while depends on his transaction cost. Commissions might wipe out the profit. Another consideration would be that this arbitrage trade would leave him Long SGX and Short OSE. This results in him having to maintain margins in both exchanges, which impacts his cost of capital. This cost will further erode his arbitrage profits. As such, it is possible that tiny arbitrage opportunities remain unexploited; it is just not worth exploiting them. Fungible Contracts Some exchanges have collaborative arrangements, which allow contracts traded on one exchange to be transferred to the other exchange for offset, thus squaring the position. The Mutual Offset arrangement (MOS) that exists between SGX and the Chicago Mercantile Exchange (CME) is one such arrangement. Under the MOS, trades in SGX can be transferred to CME, and vice versa. In these cases, the arbitrager will not need to be left Long and Short across the 2 exchanges, but could transfer the trades from one exchange to the other, and become square, hence removing the need for margins on either exchange. ď&#x201A;ˇ

Relative Value Arbitrage Another common form of arbitrage is relative value. This involves 2 instruments that are closely correlated. The instruments might be of different underlyings or of the same. For example, the share prices of banks in a country would generally move in tandem, barring any bank-specific factors such as default provisions. However the actual price movements might deviate slightly in the short-term, before returning to their equilibrium. An arbitrager might be able to sell the relatively more expensive share, and buy the relatively cheaper share, thus locking in a profit. When the share prices normalise, he should be left with a net profit, even if he loses money on one of the legs (well, thatâ&#x20AC;&#x2122;s the idea at least).

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Relative Value Arbitrage can also be done on instruments with the same underlying, but with different expiries (in the case of say Futures) or strikes (in the case of Options). For example, the EuroDollar Interest Rate Futures contract has many expiries listed. All contracts are based on the USD interest rates, hence they should move in tandem. But short-term factors might push one expiry month out of alignment with the others, thereby creating an arbitrage opportunity. This is also known as “calendar spread trading”. In the case of Options, an arbitrage might occur between say the 125.00-call and the 130.00call. As we know, the inputs for pricing an Option are Spot, Strike, Volatility, Interest Rates and Time. Assume the 2 Options have the same expiry date, and are on the same underlying (say USD/JPY). This means Spot, Interest Rates and Time are the same for the 2. We know the strikes for the 2 options, which means we can back-out Volatility (i.e. derive Implied Volatility). Should say the 130.00-call’s Implied Volatility deviate significantly higher from the volatility curve, we can exploit this by selling the 130.00-call and buying the 125.00-call. Being Long the 125.00-call would hedge our directional risk (i.e. the delta risk), leaving us with an arbitraged profit in the volatility. Note: buying the 125.00-call and selling the 130.00-call does give you a “call spread” or “bull spread” position, but here we are doing this to arbitrage the volatilities, whereas with the Call Spread, we are doing that to reduce the net premium cost. The resulting positions are the same, but the motivations are different.

Legging Risk Is Arbitrage really risk-free as is often mentioned in casual literature? No! As I keep saying, in order to make more than risk-free rate, one must take risk. In arbitrage trading, there is still Execution (aka legging) risk An arbitrager will need to execute all the legs of his trade in as quick succession as possible. If he is slow, other market participants might hit the prices he needs to complete his arbitrage. In this case, he will be left with just one leg of his trade, exposing him to directional risk, which is not the objective of arbitrage! The algorithmic arbitrager will thus also need to cater to situations where he is unsuccessful in executing all his required legs, and manage the risk of the resulting positions accordingly.

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Proxy Instruments Sometimes we are unable to establish the position we desire directly. For example, amid the current weakness in the Chinese stock market, we are unable to establish a Short position on Chinese stocks on the exchanges due to regulatory prohibitions. But we are not without alternatives. We can find proxy instruments to build our Short position, such as using Futures contracts on the Chinese Index. Another alternative might be the use of CFD’s (Contract for Difference). CFD’s are essentially OTC instruments, where a CFD provider quotes prices which the client would trade against. The CFD provider deals as “principal” and takes the opposite side of the client’s trade. The key to proxy instruments is strong correlation with the actual instrument you are trying to trade. Your module on statistics and correlation would cover this in greater detail.

Market Supervision and Regulatory Functions 1) Market Supervision Systems Particularly of interest to exchanges and market regulators, Market Supervision Systems seek to detect trading infractions or suspicious market activity, for further investigation and prosecution. Several different aspects of market activity warrant monitoring, such as:      

Unusually large volume of a transaction Frequency of transactions Variance of order and price from theoretical value Order to trade ratio (which could have been used in the Sarao case) Trading activity between related accounts (such as between 2 sub-accounts belonging to the same parent beneficiary) Trading activity patterns between apparently unrelated accounts (such as where one account buys from the other, and then sells it back to that same counterparty, repeatedly)

Regulators have to be ever vigilant and evolve their monitoring efforts to match the evolving manipulation of the wrong-doers. In the following segment, we present the monitoring of Futures and Options traded prices for unacceptable variance from their theoretical values. Objective: Monitoring variance of Futures or Options price from the underlying.

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Futures and Options derive their price from the price of the underlying instrument, amongst other factors. Futures prices can be represented as: Spot + Cost of Carry Options prices can be derived from the inputs of: Spot and Strike (Intrinsic Value), and Extrinsic Value (comprising Volatility, Interest Rates and Time to expiry). As we can see, Spot prices are key to determining the theoretical price of a Futures contract. To that end, in attempting to monitor quotes in the market to ensure that all quotes represent current market value, and to ensure that markets are not being subject to manipulation or deceptive practises, Exchange would want to monitor the Spot-equivalent of the current Futures and Option prices in the market. In the case of Futures, this can be achieved by stripping out the Cost of Carry, leaving us with the equivalent Spot price of the quote. In the case of Options, it can be achieved by stripping out the Extrinsic Value. Monitoring Variance from Spot Using a Futures quote as an example, let us say the Futures Offer is 14,500. Using the inputs of Notional Amount, Interest Rates, Dividends and Time, we derive the Cost of Carry to be say +280. This would imply that Spot equivalent price of this Futures Offer is 14,220 (being 14,500 â&#x20AC;&#x201C; 280). By plotting this quote on a chart, overlaid with the current Spot index price, which let us say is 14,215, we see that the variance is 5 index points out of 14,215, or mere 3 basis-points, which is acceptable and no cause for suspicion or investigation. However, if there was a Bid quote of 14,250, using the same Cost of Carry, we would get an implied Spot price of 13,970. This would immediately indicate to use that the Bid quote is far off-market. This in itself might not be of concern, except from the standpoint of indicating that there is little buying interest or liquidity in the market. But if a trader were to enter a sell order into the trading engine, and thus trade against the Bid quote, the Exchange might want to investigate if the sell order was a fat-finger error (where the trader accidentally entered the wrong order details), or if there is any fraudulent activity taking place in this off-market trade. Variance Tolerance The actual traded prices of the Futures contracts could in reality vary from their theoretical price somewhat, given temporary buying or selling pressure, or expectation of future price movements.

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As such, the exchange would not want to investigate every single trade that exhibited a deviation from theoretical price. It would be necessary for the exchange to determine what variance threshold would be tolerated before the system would trigger an alert and start the investigation process. The threshold would be determined by performing regression analysis on the historically observed bid-ask spread as well as the historical variance of the bid and ask from the underlying cash index. Regression Test data set Determining an appropriate look-back window on which to perform the analysis is critical. Too small a window would not capture longer trends in the market; too large a window could result in inappropriate parameters being used. Time Series analysis techniques need to be used to determine the optimum look-back window, with Stress Tests applied separately to capture the extreme tail events.

Monitoring Size of Trade The size of the trade would be another item that needs to be monitored and could give an indication as to whether a given trade should be investigated. A single, small sized trade might not warrant further investigation. But a large sized trade, or a series of small, off-market trades from the same account(s) would need further investigation. Visualization of Outputs Having loads of data and analysis is not effective for the monitoring effort if they are contained in tables, making them hard for humans to observe and detect anomalies. Visualization of data is a key consideration in designing systems. We use bubbles to indicate when trades occur, and the size of the bubble corresponds to the size of the trade. Hovering the mouse over the trade would show trade details, such as counterparties, price, quantity, and timestamp.

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The outputs from this can be visualized as follows:

Note that the red line shows the Spot equivalent price of the Futures Offer, while the blue line shows the Spot equivalent price of the Futures Bid.

2) The term “off-market transaction” and managing Large Orders Do not be confused! An “off-market transaction” is a trade that is transacted at a price that is away from the current market price, such Trade No. 7 in the Market Monitoring System example presented above. An “off-market transaction” is NOT a trade that is concluded outside of the centralized market place, such as an exchange’s trading engine. Under exchange rules, all trades in its listed instruments, such as Shares, Futures and Options, should be executed via the centralized trading engine. This is to promote price and volume discovery. Trades which are concluded outside of this centralized market, or that have been discussed between 2 counterparties, and they have co-ordinated their actions to ensure that one party is waiting for the other party to enter his order into the trading engine and then trades against that order immediately, are considered “pre-arranged trades”. Pre-arranged trades constitute trading infractions. (SGX Futures Trading Rule 4.1.13 Pre-arranged Trade Prohibited) An exception to this rule would be Negotiated Large Trades (NLT).

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Negotiated Large Trades are trades that are negotiated directly between 2 counterparties, without going through the exchange trading engine. The price and quantity is agreed, and then posted onto the exchange for clearing (somewhat like the OTC Clearing we discussed in Lecture 3, but in this case, the traded instrument is actually listed and available for trading on the exchange, whereas in the OTC Clearing case, the instrument traded is an OTC instrument). What makes an NLT an NLT, and hence not a trading infraction, is the “L” – “Large” The exchange stipulates various thresholds for various instruments. If the trade being negotiated is large than this threshold, it is considered and NLT. For example, SGX lists that the Nikkei 225 Futures contract’s NLT threshold is 25 lots. So 2 counterparties engaging in a transaction for 26 lots would not be in violation of the rules.

Consider this: Why would exchanges allow large orders to be executed via NLT?

3) Market Maker Supervision Systems Market Makers provide a valuable service to the markets by creating liquidity through showing bids and asks continuously. In return for their service, exchanges and aggregators often offer them incentives, ranging from cash payouts, to clearing fee rebates, to discounted or free co-location services. In order to ensure that they are getting the “bang” they are paying for, exchanges and aggregators would want to monitor the quality of their Market Makers’ quotes. There are 3 main considerations in Market Maker quotes:   

The width of the bid-ask spread The quantity of the orders The consistency of their presence

A Market Maker’s obligations would typically look something like this: “Quote 20 lots a side (bid and ask), no more than 3 ticks wide (bid-ask spread), for all of the trading day except for lunch time, and the 15 minutes before the Close.” How would we design a Market Maker Supervision System? 



We will need to be able to isolate the orders from a given Market Maker, from those of other market participants or Market Makers. To do this, we would need to monitor the account ID(s) of the Market Maker. We need to define the maximum acceptable bid-ask spread. By comparing the bids and asks for a given Market Maker, we would then be able to determine the spread being quoted by that Market Maker and compare that against the maximum acceptable threshold. Alerts would be sent if a Market Maker is quoting wider than his obligation.

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

Would we want to investigate each and every incident? If no, how will we determine an appropriate filter? The bid and ask order volumes quoted by the Market Maker should be compared against his obligations to determine if he is quoting in sufficient size.

Assuming tick size to be $0.01, if the Market Maker makes the following quotes, he would not be in violation of his obligations as listed above. Bid Qty 20

Bid $50.00

Ask $50.02 $50.03

Ask Qty 10 10

In implementing the solution, one might be tempted to think about using the VWAP of the orders to determine the effective spread, and that would seem correct in this case. However, if he quotes as follows, he would be in violation. Bid Qty 20

Bid $50.00

Ask $50.02 $50.04

Ask Qty 10 10

Despite his VWAP being $50.03, suggesting a 3-tick spread, the reality is that within 3-ticks, other market participants are unable to trade 20 lots. In implementing systems, be mindful of situations like these, where a particular “solution”, such as using the VWAP, seems to be correct in certain cases, but is really only correct in those cases and not representative of the true requirements.

Consider this: Are Market Makers and Exchanges truly “partners who want the same thing”?

5) Market Manipulation Price Manipulation for Fixing, Daily Settlement Price, etc. Manipulation occurs where unethical traders try to push market higher or lower, in order to affect bring about their desired effect. Their trades are not driven by any real market view, but purely by the objective of temporarily moving the market in a particular direction. Common occurrences of such manipulation are in the daily settlement price (DSP) and during the expiry of barrier options. Daily Settlement Price The DSP is the price that an exchange uses to mark-to-market open positions. Depending on where the traded price of the Longs and Shorts are, in relation to the DSP, positions could be subject to margin calls. Such margin calls would of course increase the capital requirement of the trader. As such, the trader could become motivated to attempt to push the DSP higher or lower, as would be required by his position, in order to avoid the margin call.

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Of course this can only be done if the market is still open, is illiquid, and the VWAP of his position is close to the presently expected DSP. In order to move the market, he would have to execute a few trades, thereby adding to his position size, and hence taking on more risk. So it is only feasible to do this if the position is substantial in size, meaning a substantial margin call, the market is illiquid enough to not require him add too much to his position. In doing this, the trader ends up facing overnight gap risk, since after the market closes, and the DSP is published, the trader would not be able to manage his exposure overnight (unless there is a viable proxy instrument that he can use. We have discussed proxy instruments in Lecture 2) Even without any significant overnight moves, the trader would be facing price risk the next day. In pushing the market before the close, he would have added to his real position. The VWAP of this additional position, compared to the VWAP of the price he exits this additional position the next day, will result in P&L. Hence pushing the market to impact DSP is only worth considering when one’s position size is large. I stress that market manipulation, in any form, is unethical to say the least, and can be even considered criminal under some jurisdictions (for example in SGX; see SGX Futures Trading Rule 13.8.1 http://rulebook.sgx.com/en/display/display.html?rbid=3271&element_id=1439 and its practice note)

Manipulation is more commonly observed in events that would generate a final, realized P&L (a margin call is more an inconvenience rather than a bonus-impacting event). 2 common events that would generate realized P&L are with the Final Settlement Price for exchange traded futures and with the settlement reference price for binary options. Manipulation for Binary Options Binary Options Binary Options basically make a fixed pay-out or not, depending on whether the Strike has been crossed. There is nothing in between, and there is no “unlimited profit” as with being Long vanilla options.

For example, a USD/JPY Binary Call with a Strike of 125.00, maturing on the 15th December, will make a payoff of $50,000 to the Holder of the Call, if Spot USD/JPY is above 125.00 on that day. If the Premium paid for this binary is $10,000, we would refer to this as a 1:5 Copyright © EpitrainTM 2015

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Notice that with the Binary, the profit is flat, and the Premium paid is less than that for a comparable Vanilla with the same Strike, Maturity and exercise style. The lower Premium arises from the fact that there is no “unlimited profit” for the holder of the Call, which means there is no risk of “unlimited loss” for the Writer; and as we have seen, profit has to commensurate with risk, hence lower risk for the Writer translates into a lower premium. Keep this concept in mind when you do your derivative pricing modules. Normal Binaries would observe the reference price at expiry, and determine if a payoff should be made or not. There are also “one-touch” binaries, which make the payoff as soon as the Strike is touched, any time along the life of the option. In both cases, if the underlying reference price is presently very close to the Strike, and the pay-out in consideration is large enough, there would be incentive for the Holder of the binary to attempt to push the market past the Strike, and hence collect the pay-out. Continuing our earlier example, if Spot USD/JPY is currently 124.95, the Holder of the binary would be tempted to buy USD/JPY in the Spot market, in order to push the market higher to 125.00 and collect the $50,000 pay-out. As with the earlier case of manipulating the DSP, this action would cause him to accumulate a position, which would subsequently need to be liquidated, which would thus generate a profit or loss depending on the VWAPs, but if successful, the momentary rise of the underlying reference price above the Strike would mean the trader gets to collect his pay-out! Conversely, the Writers of the binary would be defending the Strike level, selling in the Spot market, attempting to prevent the Spot USD/JPY from rising about 125.00. As a result, we often see “sticky strikes” in the market, where for the hours, or even days, before the expiry of a very large binary, the market can be stuck at a given price level, unable to rise or fall, as both parties’ actions keep the market from going anywhere. Consider this: Would the Premium for a one-touch binary be more or less than a regular binary, all other details being the same?

Monitoring and Enforcement against Manipulation Whilst markets may have rules against manipulation, the detection and prosecution of such activity is challenging. As with our example of the USD/JPY 125.00 binary, how will the market regulator prosecute if the Trader, whom we know to be guilty of manipulation, claim that he had a trading strategy that required him to buy substantial amount of USD/JPY at 124.95, independent of his binary position? How will regulators detect his USD/JPY buying activity? If this was being traded on an exchange, they might be able to monitor activity by trading account numbers; but if this was on the OTC market, how would detection be conducted? Even if on-exchange, the trader could use a different account to buy USD/JPY when he is pushing the market from the account he used to buy the binary. This would make it harder for the regulators to connect the trading activity.

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SGX Futures Trading Rules 3.4.1 Market Manipulation “A Member, Approved Trader or Registered Representative shall not manipulate or attempt to manipulate the price of a contract or of any underlying, or corner, or attempt to corner, any underlying.” We explore more on market monitoring systems and their challenges in Lecture 4.

Case Study: Sarao Hedge Manipulation may take many forms. With Sarao, it was about NOT trading. The challenge is for regulators to recognise, detect, and prosecute wrong-doing. Regulators have to build algorithms to police the algorithmic traders, and implement rules that enable them to prosecute wrong-doers. But when do the rules start to stifle growth and the freemarket mantra? As one market tightens its rules, that creates opportunities for regulators arbitrage, as traders exploit markets with weaker enforcement or less stringent rules. This causes trading activity to flow to other markets. Where is the balancing point between regulation and open markets?

6) Trading Infractions The tenets of a sound market are fair, orderly and transparent trading. Exchanges governed by rules to promote such sound practices. OTC markets less formally regulated, but are still subject to regulation by the monetary authority or such authority that grants banking licenses. Breaching such rules and regulations constitute trading infractions, which attract penalties, fines and even jail terms in certain situations.

Disclosure of Orders Consider this: Broker A approaches Broker B to find out if there is any “market-on-close” orders. Does Broker B breaks any law if he/she is to respond to the question? Answer: Broker B would have infringed the rules (or at least ethics, if he is operating in an unregulated market) if he discloses client orders. By sharing this information, especially if the order is large, Broker A may see it as an opportunity to buy ahead of the client and then sell the stock back to make money out of it. Since the price of the security would have been elevated, the execution of trades for the clients will no longer be at the best possible price. The SGX Rule governing this is:

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SGX Futures Trading Rules 3.4.7 Disclosing Orders Prohibited “A Member, Approved Trader or Registered Representative shall not disclose an order to any Person, except to the following for official purposes: (a) An officer of the Exchange; (b) An employee or agent of the Member for the purpose of executing the order; (c) The Member's sponsoring Clearing Member for the purpose of clearing the order; or (d) Such other Persons as required by law.”

Front Running This is the practice of placing your own order ahead of your client’s large order. For example, the current market price is $50 and your client gives you an order to buy 1,000,000 shares of ABC Bank “at best” (essentially a Market Order). Having received this order, you believe that the sheer size of the order will push the market higher. As such, you put in a buy order for your own account before executing your client’s order. This creates a Long position for yourself. You then execute your client’s order, the size of which pushes the market price to $52. You then sell off your Long position and make $2 Front running is wrong in that you have failed in your fiduciary duty to obtain the best possible price for your client. Even if your client had specified a price to his order, say $50, making it a Limit Order, and you had jumped in ahead of him at $50.10, you could still be accused of front-running, in that your order is preventing your client’s order from getting filled. The SGX Rule governing this is: 3.4.13 Front Running — Priority of Customers' Orders A Member, Approved Trader or Registered Representative shall not trade in contracts for its own accounts or for an account associated with or connected to that Member, Approved Trader or Registered Representative, if that Member, Approved Trader or Registered Representative also has in hand Customers' orders (including discretion orders) to do the same at the prevailing market price or at the same price.

Algorithmic Trading

4) Financial Market Architecture – Design Considerations

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Financial market systems need to be fast, robust, secure, and consistently available. Speed is crucial to prevent latency especially in algorithmic trading. In order to reduce latency, some of the common factors to consider are listed below. 1. Hardware Buy the best that you can afford. But the better the product, the costlier it is, meaning less money for your Ferrari. Machine Sizing and Spec:  Identify peak loads when running your programme, forecast growth in load (possibly from more instruments being traded, increase in liquidity in the market leading to more orders to process), and size the machine accordingly.  Also consider if your programme is coded to utilize all available hardware resources? For example, there is no point buying a multi-core machine if your programme only uses one core.  Solid State Drives increase processing speed.  Field Programmable Grid is a new technology that encodes the logic into hardware itself, rather than running software separate from the hardware, to improve speed. 2. Software The programme needs to be coded for efficiency. How you program the software will affect the latency. 

 

Use appropriate base software: using database software as an example, Oracle trumps MySQL, while dedicated Complex Event Processing (CEP) engines, such as KDB+, trumps Oracle. Using code libraries make the coding work easy, but such programmes typically perform slower than purpose-built code. Use Optimization Techniques, such as:  For databases, avoid left joints, and UPD’s  Short circuit evaluation: o if A=2, B=5, sell, else do nothing. o Will skip once A not 2  Use of Variables: Instead of running a code several times, run it once, store the result as a variable, then read the variable as often as needed. o For example, in pricing an Option, you would need to determine the appropriate interest rate to use for the expiry. This might require you to interpolate rates from the yield curve. If you are pricing a strip of different strikes for the same maturity, run the interpolation once, store that value and use as an input to price all the strikes. Do not programme such that each strike being priced performs the interpolation calculation independently (all of which would give the same result).

5) Fast, efficient communications

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Connectivity 

Lines – ensure sufficient bandwidth. Lasers and microwaves are also being used these days in place of fibre

Messaging format 

Data Packet Sizes – Information such as buy/sell, instruments, traded volume, traded price, counterparty, and payment instructions are required to be sent to the counterparties of a trade in order for them to confirm their positions and settle the trade. However, the information that is most critical to the algorithmic trader is the traded volume, and maybe the traded price. Some exchanges offer 2 message formats, one containing the critical information and the second containing the full information. The CME is one such exchange, offering the ITCH and OUCH message formats. This allows Traders to code their applications such that they can read the ITCH message quickly first, which allows them to update their positions and determine the next order that needs to be sent, and read the OUCH message thereafter, which allows them to perform the post-trade actions of updating the risk metrics, confirmation and settlement.

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6) Architectural Measures There are also measures you can take, in terms of how you set up your system components. •

Co-location Putting your algo server together with the exchange server, to reduce the distance and hence increase speed; you may have a fibre line allowing your order to travel at the speed of light, but Time is still [speed of light x distance]

•

Post-Trade Risk Management Does away with Pre-trade checks, hence removing one of the stops your orders needs to pass through on its way to the market. This reduces latency, but means that any breaches in Limits will only be detected after the trade is done, rather than before. Positions that breach the Limit will have to be unwound, likely at a loss, given the bidask spread, but for algo traders, the reduction in latency is worth the risk.

•

Drop-Copy API A term in the market that refers to a set-up where a copy of orders being sent to the market are routed to the Risk Management engine.

This means the Risk Management engine knows about the orders about the same time as the Trading engine, instead of having to wait for an update from the Trading engine. This reduces the window where problem orders, such as orders that might breach

Limits or are sent by an erroneous code in the algo, are identified. Early identification means early resolution and hence lower losses from the error. Of course the resolution process, such as cancelling the erroneous orders or cutting the resultant positions, needs to be automated as well. If it is still manual and takes 5 minutes to resolve, saving half a second with the use of Drop-Copy API isn’t going to help.

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7) Robustness and availability 

Back-up Systems Critical applications, such as Trading engines and Risk Management engines, should have a Back-up. When the Primary engine fails, the Back-up will kick in. This allows the system to be available consistently during the operational hours. The consideration around Back-ups is how long it will take between the time the Primary fails and the Back-up is “live” and ready to roll. The first step is to ensure that the Primary is constantly monitored, such that its failure is quickly noticed. Fail-over to the Back-up system can then be initiated. All upstream systems (such as trading terminals, in the case of a Trading engine failure) should be automatically routed to the Back-up. Downstream systems will have to be set up to take the feed from the Back-up now. All this should be an automated process to reduce the time required for the fail-over.



Hot vs Cold Back-ups The issue with trading and risk management applications is that they need to have a complete list of the orders and trades for accurate calculations to be performed. Once the Back-up is “live”, it must have a complete set of the order and trade history from the Primary; without a complete set, there will be gaps in the data which would result in incorrect outputs. This leads us to the issue of Hot or Cold Back-ups. Hot Back-ups could entail the Back-up being up and running, even when the Primary is doing fine, and constantly being updated, possibly by a Drop Copy API, just as though it was the Primary. This way, in event the Primary fails, there can be a seamless fail-over to the Backup. Of course this is a resource intensive solution, and there can still be challenges in having a perfectly synchronised data set between the Primary and the Back-up. Cold Back-ups would be offline, and require the system to be booted up, and loaded with a copy of the data from the Primary, before being put into the live environment. This obviously would take time. So it is a cost / risk balancing act as to whether to utilize a Hot or Cold Back-up. There are of course a lot of options in between boiling water and ice.

8) External dependencies

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Data Accuracy o Ensure data is taken from authoritative sources, such as order data directly from the Trading engine. This ensures data accuracy. o Be mindful of data sources located in different time zones, and make adjustments for the time-stamps on such data when processing the data. Up-and-down stream dependencies o

System availability: Are the up-stream systems you depend on available (online) or updated when you need them? For example, if you are running a Margin calculation programme for your bank, you would need to receive inputs from the Exchange’s Clearing House system regarding Initial and Maintenance Margin requirements. Is the Clearing House system online when you are running your process, or is the Clearing House system operating in a different time zone, which is likely the case if you are trading into a foreign market? Even if the Clearing House system is online, has it been loaded with the current Margin requirement values, or is it still populated with the previous day’s data? For example, we are located in Singapore, but the exchange we are trading into is in Europe. When we run our process at 10am Singapore Time, the European exchange Clearing House system might be still populated with the previous day’s Margin information, if they only upload the new Margin information during their morning, i.e. 2pm Singapore Time. If this is the case, we would need to design our system to read the updated European Margin information after 2pm Singapore Time, and run the associated calculations then.



Data format: is the data that your system requires available in a format or language that your system can read? Or do you need to build a translator?

Similar considerations would apply to the downstream systems you need to send your outputs to. 9) Functional Requirements The more functions you have, the more your development, testing and maintenance costs, and slower time to market. AS with all things in Life, what is a “need” and what is a “want”? Are there work-arounds available?

10) Non-Functional Requirements Consider issues such as processing and response speed.

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If a risk report is required every hour, but takes 2 hours to generate, that’s a problem.

7) Latency Considerations for the Arbitrager and Algorithmic Trader Arbitrage is a game of speed. If you are slow, you either miss the opportunity (not so bad), or you fail to hit all the required legs, leaving you exposed to directional risk (that’s worse than missing the opportunity altogether). This has led to arbitragers building algorithmic trading solutions to reduce latency in the execution cycle, from identifying mispriced instruments, calculating fair value, determining that an arbitrage opportunity exists, and sending the necessary orders to capture the arbitrage profit.

How do we reduce latency? There are many levers to pull here. 

We can build a bot.



Ensure the bot is coded for efficiency.



Code the bot to use thin message formats for market and trade data, such as the ITCH messages from CME.



Invest in state of the art hardware.



Pay for the fastest connectivity available.



Pay for co-location services.

But all these costs add up, and erode our profitability. We have to consider if there will be sufficient arbitrage opportunities, and the size of the profits from these opportunities, how many competing arbitragers we have to contend with in the market, and determine if the expected profits can cover the development and operation cost? These consideration apply to all algorithmic traders. Time to market is another important consideration for algorithmic traders. Whilst backtesting is crucial to ensure our strategy and our coded logic work correctly, taking too long to deploy our system after we have identified an opportunity, might mean that our competitors deploy before we do, and capture the opportunity.

Algorithmic Trading Algorithmic Trading is the automation of the usual trading business flows (i.e. Pre-Trade, Trade, Posttrade) Algorithmic trading uses pre-programmed logic on electronic platforms to enter orders. Such orders are used to establish new positions or manage existing positions.

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The automated of the market analysis, performed pre-trade and post-trade, as well as the instructions to the market greatly reduce latency. Automation of the market analysis also removes human error and judgment heuristics (you will learn more about this in Behavioural Finance), though some might argue that removing the human means removing the ability to fine-tune and calibrate responses to market events. It’s a little like the selfdriving cars that are being developed today. Should the car’s sensors fail to recognize an oil slick on the road, instead interpreting the wet patch as water, it would drive straight into disaster, whereas a human would be able to make a judgment call; the dame human who will likely drive too fast or too slow, or make an illegal U-turn because he thinks it will save time, and instead ploughs into traffic on the opposing side.

Difference between HFT and Algorithmic Trading Often confused or taken interchangeably, though actually possessing differences, High Frequency Trading (HFT) is a form of algorithmic trading, where many orders and sent in rapid succession, and positions are held for short durations, in some cases for a matter of seconds. HFT trades in shortterm positions at high volumes to make small profits in each trades.

The speed required by HFT’s, from analysing the market, to determining the orders that need to be sent, to the actual order sending, and the management of the resultant positions, means that HFT has to be automated and hence algorithmic. Algorithmic Trading on the other hand, might not require the speed and low latency required of HFT. HFT’s use sophisticated technology for rapid identification of trading opportunities, such as price mismatches, and send orders quickly to capture the opportunity.

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Main Phases in Algo Trading Process

Concept / Idea Birth

Monitor & Adjust

Algorithmic Trading

Back-testing / Develop

Execution & Deployment

Concept Birth How do you do anything, if you don’t know what you want to do? Like all things in Life, we need to start with an idea, a concept. What sort of strategy do we think would make us money, given the market conditions we intend to operate in? What trading strategy (refer to Lecture 4 notes) should we employ? For example, we might postulate that when the price of an underlying instrument, say crude oil futures, moves by more than 5% in either direction (up or down) as compared to its settlement price from the previous day, it will revert to close within 3% of the previous day’s settlement price. Hence, with the daily settlement price at $50.00, we postulate that should prices drop below $47.50 today, it would represent an opportunity to go Long, with the expectation that the market will revert to close today above $48.50, representing a profit potential of $1.00 On the other side, if prices rallied above $52.50 today, it would represent an opportunity to go Short, with the expectation that it will close below $51.50 today, again giving us a $1.00 gain. Now we know that this won’t always hold true, and that there will be some days when we have gone Long or Short, and the market continues to fall or rally respectively. This will lead to the need to cut our position to prevent run-away losses. Having learnt the concept of maintaining a minimum 1:3 Risk:Reward ratio, we might postulate that our stop loss levels should be $47.20 and $52.80 for Long and Short positions respectively. We imagine that the number of times the market reverts, hence giving us the $1.00 profit, is greater than the number of times the market extends its move to trigger our stop-loss. Copyright © EpitrainTM 2015

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In developing our strategy, it is important to anticipate the actions of other market participants. See the case study below on the “Plus 1” algos.

Back-testing and Development Having got the idea of how you are going to make money, you need to back-test your strategy. You would need to analyse the historic prices of crude oil futures to determine the number of times the market would have reverted to give you your $1.00 and the number of times it would have triggered your stop. Totalling the P&L from that, given an arbitrary position size, would determine the overall profitability of the strategy. Adjusting the Entry Price, say pushing it further away from the DSP before triggering your entry trade, but keeping the stop-loss where it was, could result in an improved overall profitability, given that winning trades would now yield more than $1.00 and losing trades will cost less than $0.30. Alternatively you might find that the stop-loss is too close, getting triggered way too often, in which case you might elect to shift the stop-loss price further away. This might violate the 1:3 rule, but if you were to approach this from a probability-adjusted pay-off stand-point, it could still be justifiable. For example, if the adjusted stop-loss price level means that when you make, you make $1.00, but when you lose, you lose $2.00, this might seem unacceptable. However if 85% of the trades entered into under these parameters are profitable, the probabilityadjusted profit would be $0.85, which compared with the probability-adjusted loss of $0.30, could still be a viable strategy. Standard deviations could be used to calibrate the entry and stop price levels.

Beware Confirmatory Bias when doing back-testing. Don’t be so eager to find the Golden Goose that you mistake a roasted duck. Ensure your back-testing goes back sufficiently far in time to establish that the observed reversion patterns in the present are not just a local phenomenon, but something that endures over time. Yet be careful not to go so far back in time that the old data is no longer relevant to the present – don’t get run over by the Ford T-model! Stress test your strategy: Ideally, ensure that your test data covers periods where the market experiences exceptional stress, such as flash-crashes or political unrest in the Middle East for oil. How would volatility and liquidity under such conditions impact your trading strategy?

Having established the optimum price levels for your trading strategy, you would need to get the logic programmed into your algorithmic server. The base logic required in our example might be as follows: Variables

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Let Long-entry price (LEP) = 0.95 x DSP Let Long-stop price (LSP) = LEP – 0.30 Let Long-take profit price (LTP) = 0.97 x DSP Let Short-entry price (SEP) = 1.05 x DSP Let Short-stop price (SSP) = SEP + 0.30 Let Short-take profit price (STP) = 1.03 x DSP

Step1 => Read and store DSP Step 2 => Monitor LAST traded price Where LAST <=LEP, send buy market order, quantity = 10 (Order 1) If Order 1 done, a. end Step 2 b. send Order 2 (Sell stop order, Trigger price = LSP, quantity = 10) OCO Order 3 (Sell Limit order, Limit price = LTP, quantity = 10) Where LAST >=SEP, send sell market order, quantity = 10 (Order 4) If Order 4 done, c. end Step 2 d. send Order 5 (Buy stop order, Trigger price = SSP, quantity = 10), OCO Order 6 (Buy Limit order, Limit price = STP, quantity = 10)

After programming it, we would need to test that the code is correct, and the algorithm triggers the orders in accordance with our strategy’s requirements. There needs to be a balancing point between performing sufficient testing to ensure the strategy and code work, and time to market. Don’t forget, you are not the only trader in the market. Others are looking for winning strategies too, and the early bird gets the worm (well, on the flip side, the early worm meets the bird, but hey, in order to make more than risk-free rate, one must take risk)

Deployment Having completed testing, it is time to put your strategy into the market and see if it works.

Monitor and Control There is need to constantly monitor your algo in production. Market conditions may change, or previously undiscovered bugs may surface. Beware the Observer Effect. What might have worked perfectly in the test environment, where data was static, might have a different effect in the real market, where orders are dynamic. For example, Copyright © EpitrainTM 2015

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putting a huge buy order into the test environment would merely have caused trades to be executed at the best available prices in your time series / data set; however putting the same huge order into the live market will cause the Offers to run, resulting in greater slippage for your order, and hence poorer profitability.

We can also view these stages in terms of the usual Trade Lifecycle phases: Pre-Trade  

Strategy Creation Market Analysis (to trigger orders)

Trade  

Order Sending Position update

Post-Trade (Risk Management)  

Position Management Profit and Loss Management

Similar to regular trading, positions and P&L from algo trading would need to be monitored to avoid overly large positions and run-away losses.

Post-Trade (Settlement)  

Margining Settlement

Market Maker’s “Plus 1” Algo Objective: Be the “best” Bid in the market Input: Best Bid in the market currently. Let BBM = Best Bid in Market Source: Exchange’s Order Feed Process & Output 1) Get in front of Best Bid by showing a better Bid 2) Check market’s Best Bid again (disregard own order) 3) Send new better Bid if required (cancel previous bid) 4) Repeat Process and Output: Let market best Bid = BBM (tick = 0.01) Let Spread = variable S = 0.01 Copyright © EpitrainTM 2015

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Let own Best Bid = OBB Where own Bid price = OBP = BBM+S And own Bid Qty = variable OBQ = 10 Let OBB = Latest OBB n

Let OBB i = Previous OBB o

Start Step 1 => check BBM Step 2 => send Step 3 => check BBM If BBM UID = Own UID, Step 3 If BBM UID ≠ Own UID, Step 2 Loop

Case Study: Market Supervision Systems detect irregular trading pattern

The price pattern above was observed on the Market Supervision System. The market bid rose steadily from 50 up to 60 in a matter of seconds, followed by the 60-bid being traded off, and then the 59-bid being traded off. Once the 59-bid was traded, the market best bid then appeared as 50; and the pattern repeated itself. Having observed the above, Market Supervision department suspected possible market manipulation, and triggered an investigation. Findings: There were 2 market markers employing the “Plus 1” Algo. The 50-bid was a genuine bid in the market. However the 2 market makers ended up unwittingly pitted against each other, and ended up walking the market upwards between their bids, as each market maker put in a higher bid, based off the other market maker’s bid. Independent of the market makers, there was a third algo operating. This third algo basically was programmed to sell to create a short position, if the market runs up by 10 cents within a 5-minute window, believing that such a move was over exaggerate and hence expected a pull back. This resulted in the trades at 50 and 60.

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Of course once this third algo’s orders had traded off both market makers’ bids at 59 ad 60, the best prevailing bid was the genuine 50-bid, which then appeared as the top of the book price. From here, the 2 market makers’ algos would then start walking the market up again …. And rinse and repeat.

Safe-guards and Exception handling How could we avoid a repeat of this situation? We would need to programme some safe-guards into the market maker algo (indeed all algo’s should have safe-guards appropriate for their strategy). In the case of the “Plus 1” algo, a possible safe-guard could be to define and determine abnormal behavior in the algo. For example, if the algo sends a “better bid” more than three times in a 5- minute period (the exact values would be dependent on the volatility of the market), without any trades, the algo should cancel all its orders and square its associated position. Of course this would mean that all orders, trades and positions that are associated with this particular algo need to be identified and tagged, thus allowing only these associated orders and positions to be cancelled and cut. This is especially applicable where the trader is running more than one algo simultaneously. Alerts and notifications should then be sent to the appropriate parties, such as the algo administrator, to highlight the occurrence for investigation and follow-up.

Consideration: What would have happened if the market maker’s algo objective was changed to “be the best Bid and Ask in the market”? What would have happened? What would you need to do to prevent the problem?

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Standard Trading Process Flow

High Frequency Trading Process Flow

Note the removal of humans for order sending and analysis, and the shifting of risk management to post-trade to reduce latency. Traders are removed from the front office. Their roles are replaced by HFT through sophisticated automation and programing for rapid trading. As trades need to be executed rapidly to prevent latency, they do not go through pre-trade checks. Therefore, any trades beyond prescribed position Copyright ÂŠ EpitrainTM 2015

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limits will only be known to the Middle-office after the orders are already at the trading engine, and hence could be potentially executed, or in the case of market orders, after the position has already been established. This means that by the time the Middle-office is aware of these breaches, the damage might already be done. This results in the need for post-trade risk management to take place in near real-time. To achieve this, drop-copy API’s, where a copy of the order is sent to the post-trade risk management engine at the same time it is being sent to the trading engine, are employed. The processes within the risk management engine are automated (algorithmic risk management?) to reduce latency. Similar to trading, alerts need to go out to the appropriate people in an appropriate form; there is no point alerting the risk manager of a position limit breach via email, unless that risk manager is constantly staring at his email inbox.

Safe-guards in Design Considerations for Algo Trading As we have seen, algos can go awry. In order to control them, we need to employ safe-guards. Safeguards can be grouped into those that can be employed by the Algo Traders, and those employed by the trading venues (such as Exchanges or Aggregators) Safe-guards employed by Algo Traders 

Back testing of Strategy Program the system and back test it through simulations. Using canned data, played in the simulation environment, allow us to determine if the algo works.  Adequacy vs Time Market  Observer Effect  Anticipate the actions of others



Testing of code  Exception Handling (To design your system for auto shut down function when faced with situations that you did not anticipate because it was not program into the system)  Order tagging – when running multiple strategies, which strategy does it belong to? (Tag the orders to the algo to avoid confusion and drive the next action)



Limits: Position and Loss  Stop Loss Orders; code components



Exception handling: Black Swans (Extremely rare events, You did not know you did not know, hence we did not mitigate the risks)

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Alerts: Manual and Automated, sent in the appropriate form

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

Automated Kill-switches; turn off the algo when market conditions are not “normal” or as expected

Safe-guards employed by Trading Venues 

Throttling Throttling limits the number of orders that can be sent from a single trading ID, or connection to the trading engine. For example, a throttle may limit the number of orders sent per second to 20 orders. This prevents the algo from flooding the trading engine, leveling the playing field (somewhat) and preventing denial of service attacks. Of course algos can overcome throttles by simply buying more connections to the trading engine; if the algo trades are large enough, the increase in cost would not be a prohibitive hurdle.



Circuit Breakers These trigger official trading pauses when a given instrument has traded more than a prespecified amount from a reference point. For example, a Circuit Breaker might be triggered when the price of XYZ share has moved 5% from its previous day’s settlement price. The idea behind Circuit Breakers is that they provide a panicked market with time to digest events and analyze the situation, and hence dispel panic and fear, and restore orderliness to the market while preventing any further movement in prices. Circuit Breakers last for a pre-determine period of time, say 15-minutes. When trading resumes, the hope is that the market will again trade in a calm, logical, orderly fashion. It is important to co-ordinate the triggering and lifting of Circuit Breakers on instruments that are traded on more than one venue, or on highly correlated instruments. In the case of the Flash Crash, it was found that un-coordinated Circuit Breakers across the various American exchanges actually causes the markets to sling-shot each other lower. As the first exchange was halted, the second continued to trade lower, till it hit its Circuit Breaker. When the first exchange resumed trading, traders there continued to sell as they could see that the price of the instruments on the second exchange were already lower. The same happened when the second exchange resumed trading… and down and down we went.

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Case Study: Flash Crash, 6 May 2010

   

By the afternoon of May 6, 2010, the Dow Jones Industrial Average (DJIA) had already fallen by more than 300 points on the day. It then began a precipitous decline of nearly 700 points in a few minutes, amounting to a roughly 1,000 point drop on the day at that point. 20 mins later, the market rebounded, regaining most of the 700 point drop on the DJIA. The 1,000 point decline was historical, representing the largest one-day decline in the history of the DJIA.

Causes Algorithmic trading systems   

There was a large sell-order of 75,000 E-mini futures on the S&P 500 Index by an unidentified investor. The investor executed his sell-order through an automated algorithmic system, without taking into considerations of price or time. The sell order was rapidly executed within 20 mins which the system would usually takes five hours to complete. Hence causing a turmoil in the market.

High-Frequency Traders 

The sell-side pressure on the E-minis immediately accumulated significant long positions in the security.

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 

The surge in trading activity generated significant volume, which interacted with the initial seller’s algorithmic system to increase the rate at which contracts were fed to the market, creating an iterative loop. The combined sell-side pressure between the initial seller and HFTs drove the prices down by 4% in just five minutes.

Liquidity crisis  

As the buy-side interest was extremely low, fundamental traders turned away from the contracts fearing that the price declines were a result of larger forces in motion that did not understand. A total of combined 80,000 contracts sold and only 50,000 contracts bought resulted in an imbalance of 30,000 contracts which drives the prices down to its lowest point.

Lesson Learnt 1. Algorithmic trading systems that do not take price & time into consideration may cause considerable dislocation to markets by triggering extreme price movements. 2. Official trading pauses can be an effective way to restore orderliness to the markets while preventing any further declines in prices. E.g.: Circuit-breakers Preventive Measures Due to the nature of HFT in competitive time pressure to execute trades without going through extensive safety measures which creates latency, risk management is relatively harder to be implemented in HFT. Therefore, implementing of control measures are crucial to prevent potential manipulation.           

Kill switch to suspense an individual firm’s trades with erroneous trades or an excessive trading volume. Throttling – to control no. of orders Circuit Breakers – If market falls by certain % within certain timing, the trading engine will shut down the particular stock/ industry / whole engine Penalties for excessive cancellations to discourage manipulative strategies Regulations to address market manipulation and threats to integrity Minimum order exposure time E.g.: Orders cannot be cancelled within minimum duration Time Stamping Rigorous Back-testing Logical checks on time-lag between cycles Independent Code Review Independent Strategy Review

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