Oil price volatility

OIL PRICE VOLATILITY IMPLICATIONS FOR WORLD FINANCIAL MARKETS

By,

Taijas Kumar

Alexander Vasiagin

Table of Contents Chapter 1 The Effect of Oil Price Volatility on World Equity Markets .......................................... 3 Introduction ........................................................................................................................................ 3 Literature ............................................................................................................................................ 4 Method Generalized Autoregressive Conditionally Heteroscedastic (GARCH) .............................. 4 Sample ................................................................................................................................................ 4 Applying the GARCH (1,1) model................................................................................................... 5 Results and discussion ...................................................................................................................... 7 Chapter 2 The Effect of Russian Currency Depreciation on World Economic Stability ............. 13 Introduction ...................................................................................................................................... 13 Economic stability factors that could get affected by Ruble .......................................................... 15 Economic impact of Sanctions ................................................................................................... 18 Econometric analysis ........................................................................................................................ 19 Chapter 3 Hedging Solutions for Equity Investment in the Current Oil Price Volatility ............ 23 Introduction ...................................................................................................................................... 23 Hedge ratio ....................................................................................................................................... 23 Long straddle .................................................................................................................................... 24 Long strangle .................................................................................................................................... 25 Synthetic long put ............................................................................................................................ 27 Hedging ETFs..................................................................................................................................... 28 Bear put spread ................................................................................................................................ 29 Chapter 4 Current Oil Price Volatility and Global Financial Crisis ............................................. 31 Weakening global demand .............................................................................................................. 31 a.

Commodity price effect ...................................................................................................... 31

b. Emerging Markets & Europe .............................................................................................. 33 Geopolitical risks .............................................................................................................................. 35 OPEC.................................................................................................................................................. 36 Chapter 5 How the Current Situation May Affect the Recovery of Greece and the Australian Economy in the Short Run ................................................................................................................. 39 How the recovery of Greece would be affected ............................................................................. 39 Oil-Price volatility impact on the Australian economy ................................................................... 42 References ............................................................................................................................................ 47 APPENDICES ..................................................................................................................................... 52 APPENDIX - A .................................................................................................................................... 52 APPENDIX – B ................................................................................................................................... 55 APPENDIX – C.................................................................................................................................... 58

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Table of Figures Figure ‎1.2 Volatility of Oil from 1986 – 2015......................................................................... 10 Figure ‎2.1 Source: Central Bank of Russia .............................................................................. 13 Figure ‎2.2 Source: Central Bank of Russia .............................................................................. 14 Figure ‎2.3 Source: Central Bank of Russia .............................................................................. 15 Figure ‎3.1 Options long straddle diagram. Source: theoptionsguide.com ............................... 24 Figure ‎3.2 Options long strangle diagram. Source: theoptionsguide.com ............................... 26 Figure ‎3.3 Synthetic long put diagram. Source: theoptionsguide.com .................................... 28 Figure ‎3.4 Bear put spread diagram. Source theoptionsguide.com ......................................... 30 Figure ‎4.1 Global commodity prices (1995-2009), Source - (Obstfield & Rogoff, 2009) ...... 32 Figure ‎4.2 Global statistics, Source - IMF(a), 2015 ................................................................ 33 Figure ‎4.3 Gasoline prices to Crude oil market ....................................................................... 35 Figure ‎4.4 US recessions and Global Oil Price, source: (WTRG Economics, 2011) .............. 37 Figure ‎5.1 Greece investment gap, Source: International Monetary Fund, 2013 .................... 40 Figure ‎5.2 Bond Yield, Source: PWC,2015............................................................................. 41 Figure ‎5.3 Terms of Trade, Australia, Source: RBA ............................................................... 43 Figure ‎5.4 Australia‘s trading partner growth*, Source: RBA, CEIC, Reuters ....................... 45 Figure ‎5.5 Monthly movement in the S&P ASX 200 Energy Index, Aug 2013 - Oct 2015 ... 45 Figure ‎5.6 Weekly movement in the S&P ASX 200 Energy returns, Aug 2013 - Oct 2015... 46

Database of Tables Table ‎1.1 ADF test at first difference ........................................................................................ 5 Table ‎1.2 Engle Test for ARCH effects ..................................................................................... 6 Table ‎1.3 GARCH(1,1) Model of Crude Oil Price .................................................................... 6 Table ‎1.4 OLS estimation of how Oil Volatility directly affects major world Equity Markets (Appendix-A) ............................................................................................................................. 8 Table ‎1.5 Limited sample till 3/01/15 of previous OLS (Appendix- B) .................................... 9 Table ‎1.6 OLS estimation of how Oil Volatility directly affects major banking stocks (Appendix- C) ............................................................................................................................ 9 Table ‎1.7 OLS estimate of S&P500 and Oil Volatility over a 30 year sample ....................... 11 Table ‎2.1 Cointegration Properties (Source: Dreger et. al 2015) ............................................ 20 Table ‎2.2 Conditional variances of VAR errors (Source: Dreger et. al 2015)......................... 21 Table ‎3.1 Long straddle option ................................................................................................ 25 Table ‎3.2 Long strangle option ................................................................................................ 26 Table ‎3.3 Synthetic long put .................................................................................................... 27 Table ‎3.4 Bear put spread ........................................................................................................ 29 Table ‎5.1 Output growth and inflation forecasts in %, Source: RBA...................................... 44

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Chapter 1 The Effect of Oil Price Volatility on World Equity Markets

Introduction Since 2011 oil has been reasonably stable, fluctuating around $100US per barrel of oil, but since June-2014 the price has drastically dropped and is expected to remain at relatively low for the next couple of years. The price at its peak in 2014 was at $112 a barrel, with its lowest hitting $43.39 in March-2015. Consequently the oil market has had a considerable increase in volatility and unpredictability causing investors to become increasingly wary and cautious, with similar surges in volatility last seen during the 1970s energy crisis and 2008‘s global financial crisis (Baffes et al., 2015). This increase in volatility and downward pressure in the price is been driven by multiple factors; weakening global demand, appreciation in the U.S dollar, geopolitical risks surging with the crisis between Russia and Ukraine and tensions in the Middle East (Baffes et al., 2015). While it is difficult to point out what the main contributor is, OPEC‘s stance in price support of oil and the rapid increase in supply appears to have played a major role in the movements of oil as of late (Baffes et al., 2015).

For the purposes of this question, we shall be looking at how the recent fluctuations in oil price volatility directly affects major world equity markets, as opposed to what affects the actual change in oil prices would have had. Oil Volatility or oil shocks can be best defined as an abrupt, unexpected variation in oil prices (Abrham, 2015). We shall use a Garch(1,1) and OLS model to regress the relationship between such volatility and major equity markets and their respective banking stocks. The reason we are using a GARCH model of volatility and not % change in price as our independent variable is because the Garch series for volatility intends to capture the risk faced by the markets due to the unpredictable fluctuations in the price of oil (Abrham, 2015). We are aware that exclusively testing oil volatility against markets and stocks with the relatively small sample size exposes us to omitted variable bias.

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Literature In emerging and developing stock markets, the effects of oil shocks are normally derived to be significant, where oil price shocks are linked to adverse reactions in many macroeconomic factors such as inflation, economic growth and interest rates (Arouri, Lahiani and Nguyen, 2012). According to Christoffersen and Pan (2015), on a general global level an increase in oil price uncertainty and volatility indicated tightening in funding constraints of financial intermediaries, which resulted in the stock market being negatively affected. Sadorsky’s (1999) also had previously stated that in the US economy, positive shocks to oil price volatility ―depress‖ real stock returns and hold a negative relationship over the long run.

Similarly, a study done by Masih, Peters and De Mello (2010) found that in South Korea, oil price shocks significantly affected their stock market, with an inverse relationship due to their heavy net import of oil. Ochoche Abraham (2015) conducted a study on how the recent fluctuations affected Nigera which is a primary net exporting state. His empirical findings suggest that the current intense volatility in the oil price will indeed have a negative impact on the economy because with a shortfall in credit to private sector, the stock market in general would contract.

Method Generalized Autoregressive Conditionally Heteroscedastic (GARCH) Tim Bollserslev (1986) created the GARCH model, which was specially intended to model and forecast conditional variance. The variance of our variable (oil price) is modelled as a function of past values in a GARCH (1, 1) where we used the data to create a singular series for that conditional variance. The mode equates as; This conditional variance essentially equates to the volatility of that consequent variable (Bollserslev,1986).

Sample The singular independent variable we used for this report is $US Dollar per barrel of oil (St.Louis Fed, 2015) from 1-August-2014 to 1-August-2015, where we used the raw data to 4

generate a series for the volatility of oil prices. The dependant variables we used for our multiple single equation models were major Index data and multiple banking stocks prices for the same time frame of August-2014 to August-2015, derived from Yahoo Finance (2015).

Applying the GARCH (1,1) model Volatility is quite easily one of if not the most crucial concepts in finance, where the volatility of the variance is measured using standard deviation or variance. While this will suffice for very basic calculations involving rudimentary equations, it will fail to properly capture the complete risk involved in the volatility. For this we use the GARCH (1,1) model in Eviews to properly measure how the unpredictability of crude oil prices can affect world equity markets and major banking stocks.

The first step is to make our oil price data stationary and then test to see whether it contains ARCH effects. For this we run an Augmented-Dickey Fuller test to see whether oil prices are stationary at first difference. Table 1.1 confirms that our data is stationary at I(1).

Table 1.1 ADF test at first difference Null Hypothesis: D(OIL) has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on AIC, maxlag=15)

t-Statistic

Prob.*

Augmented Dickey-Fuller test statistic

-19.42127

0.0000

Test critical values:

1% level

-3.456408

5% level

-2.872904

10% level

-2.572900

Next we create a single equation model of Oil Prices, and implement the Engle test for ARCH at lag 1 (Savrasov, 2015). Looking at table 1.2, we can see that the results are significant, which confirms the existence of ARCH effects.

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Table 1.2 Engle Test for ARCH effects Heteroskedasticity Test: ARCH

F-statistic Obs*R-squared

7607.848 240.2321

Prob. F(1,246) Prob. Chi-Square(1)

0.0000 0.0000

Now that we have confirmed ARCH effects, we apply the GARCH(1,1) to our oil price equation, which is shown in table 1.3.

Table 1.3 GARCH(1,1) Model of Crude Oil Price Dependent Variable: D(OIL) Method: ML - ARCH (Marquardt) - Normal distribution Date: 10/27/15 Time: 23:50 Sample (adjusted): 8/04/2014 7/31/2015 Included observations: 251 after adjustments Convergence achieved after 28 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(2) + C(3)*RESID(-1)^2 + C(4)*GARCH(-1)

Variable

Coefficient

Std. Error

z-Statistic

Prob.

C

-0.208272

0.103274

-2.016707

0.0437

0.764445 0.951347 3.948165

0.4446 0.3414 0.0001

Variance Equation

C RESID(-1)^2 GARCH(-1)

0.358504 0.027589 0.825834

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood

-0.000015 -0.000015 1.558006 606.8454 -464.6997

Durbin-Watson stat

2.411042

0.468973 0.029000 0.209169

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

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0.202191 1.557994 3.734659 3.790842 3.757269

Using the variance equation from the GARCH(1,1) estimation, we can now generate a conditional variance series in Eviews, which is represented in Figure 1.1.

Results and discussion For the scope of our regression, we decided to test how oil price volatility would directly affect world Equity Markets and major banking stocks in the respective markets for the past one year. The notion that we are testing it directly and exclusively already implies that there will be a significant omitted variable bias, in addition due to the relatively small sample size we might also give us results that are contrary to literature.

We also decided to use a 10% significance level (Îą) for our T-tests because of the difficulties in finding significant results in econometric time series data. Birnbaum and Fisher (D. and 7

Fisher, 1975 & BIRNBAUM, 1970) argued that the idea of a standard level for statistical testing is not ideal, where they state that a fixed level does not make sense because it is based on the ―cost/benefit ratio‖ of the study. They also imply that the researcher‘s judgement plays a major role in setting a statistical benchmark. For the purpose of this report, we‘re adopting a 90% significance as opposed to the 95% norm and using a 10% level of significance (α) for only our T-tests. The remainder of all other tests will continue to use the 5% level of significance.

We created a simple linear regression on Eviews with oil price volatility (VOL) as our independent variable and the differenced market index as our dependant variable. Table 4 represents our primary regression output coefficients and corresponding p-values for each market. The highlighted p-values indicate coefficients that are significant, where p-value < .10. We also chose to use a one lagged value of oil volatility, in all our regressions. The reasoning behind this was that equity markets are not entirely efficient (Clarke, Jandik and Mandelker, n.d.) and generally take time to react to certain shocks directly or indirectly.

Table 1.4 OLS estimation of how Oil Volatility directly affects major world Equity Markets (Appendix-A)

LINEAR OLS ESTIMATION EQUATION D(MARKET INDEX) = Β *OIL VOL(-1) ± β + U 1

0

t

Sample: 08/05/2014 – 07/31/2015 MM/DD/YY WORLD INDICIES

S&P500 – USA

ASX200 – AUS

DAX – GRM

NIKKEI22 – JAP

SSE – CHN

Β

2.380191 (0.4282)

1.783352 (0.3370)

4.854691 (0.8364)

32.61495 (0.4419)

0.005979 (0.0661)

(P-value)

-5.077198 (0.4871)

-0.001146 (0.4205)

-3.419742 (0.9545)

-59.06704 (0.5721)

-0.013310 (0.1166)

R-squared

0.002533

0.003764

0.000177

0.002425

0.014240

1

(P-value)

β

0

According to our regression, the only market that had a significant direct (0.0661 < 0.10) impact from oil volatility was China via the Shanghai Stock Exchange (SSE). The relationship was a positive one, where for every one unit of volatility in oil, the SSE would react by .005979 in the same direction. The remaining regressions all produced insignificant results, which mean that the volatility of oil did not have a direct impact on the respective equity market. If we refer to the r-squared values we can shed some more light on this reason,

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where it shows that the explanatory power of our equation is considerably low, with all of them being less than 1% apart from China which was 1.42%. The reason why these figures are so low is mainly due to omitted variable bias, (Oster, 2014). It‘s highly likely that our regression contained omitted variable bias, due to not including different variables of macroeconomic factors such as interest rates, exchange rates, open interest, GDP, etc which would have given us results that would have possible been more accurate.

Table 1.5 Limited sample till 3/01/15 of previous OLS (Appendix- B) Limited Sample: 08/05/2014 – 03/01/15 MM/DD/YY WORLD INDICIES (LIMITED SAMPLE) Β

S&P500 USA

-

ASX200 AUS

-

DAX GRM

-

NIKKEI22 JAP

-

SSE CHN

-

2.350492 (0.4770)

3.802513 (0.0594)

-6.865862 (0.7522)

35.13385 (0.4419)

0.004112 (0.1153)

(P-value)

-4.599035 (0.5751)

33.64987 (0.5523)

-63.02545 (0.5799)

-0.007848 (0.2619)

R-squared

0.003592

-0.001947 (0.1772) 0.025156

0.000725

0.004261

0.018821

1

(P-value)

β

0

To test the relationship even further, we decided to further limit the sample to the most volatile period in our time frame which was from 1-August-2104 to 1-March-2015 displayed in table 1.5. Based on the limited regressions we can immediately see that the R-squared has increased substantially across the board which implies that our estimation has relatively improved. We also observe that the p-value of the ASX200 went from 0.3370 in the general sample to 0.0594 in the limited sample, which implies that oil volatility had a significant direct impact on the Australian market in this concentrated time frame as opposed to the entirety of the sample.

Table 1.6 OLS estimation of how Oil Volatility directly affects major banking stocks (Appendix- C) LINEAR OLS ESTIMATION EQUATION D(BANKING STOCK) = Β *OIL VOL(-1) ± β + U 1

0

t

Sample: 08/05/2014 - 07/31/2015 MM/DD/YY LARGEST BANKS STOCKS IN REPECTIVE MARKETS Β

1

(P-value)

β

0

(P-value)

R-squared

JP MORGAN (USA)

0.238207 (0.0760) -0.521102 (0.1103) 0.012644

NATIONAL AUSTRALIA BANK (AUS)

0.029180 (0.6146) -0.059197 (0.6836) 0.001037

DEUTSCHE BANK (GRM)

0.048708 (0.6581) -0.109063 (0.6837) 0.000791

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MITSUBISHI UFJ (JAP)

7.604503 (0.0406) -0.004390 (0.1106) 0.017077

INDUSTRIAL & COMMERICAL BANK OF CHINA (CHN) 0.022786 (0.0975) -0.057242 (0.1109) 0.011639

We also decided to test the major banking stocks in the respective countries to see how they react to changes in oil volatility. We found that out of the five major banks, oil volatility had a significant impact on JP Morgan, Mitsubishi UFJ and ICBC. All three of them have positive relationships with volatility, with Japanese bank Mitsubishi UFJ displaying a high sensitivity to oil shocks with a coefficient of 7.6. However, the R-squared of our significant regressions are again very low with the highest one being 1.7077% for Mitsubishi UFJ. It was surprising that the S&P 500 did not have a significant result for oil volatility considering the U.S. is a large exporter of oil. To understand further, we decided to use the same test but using a larger sample size of 30 year daily data. Table 1.7 presents the output.

Figure 1.1 Volatility of Oil from 1986 â&#x20AC;&#x201C; 2015

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Table 1.7 OLS estimate of S&P500 and Oil Volatility over a 30 year sample Dependent Variable: D(S_P500) Method: Least Squares Date: 10/27/15 Time: 18:32 Sample (adjusted): 1/06/1986 10/20/2015 Included observations: 7462 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

VOL(-1) C

-0.201995 0.563251

0.044371 0.151254

-4.552449 3.723887

0.0000 0.0002

0.002770 0.002637 11.80974 1040446.

-29010.23 20.72479

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.268677 11.82534 7.775994 7.777848 7.776631 2.117077

0.000005

We can see that oil volatility is incredibly significant over a larger sample size with a negative relationship, where when volatility increases the returns on market decrease and vice versa. This result is very much unlike the results we gathered for our original one year sample regression where oil volatility in the American market was considered greatly insignificant. Hence, we can confirm that sample size does play a crucial role in creating accurate measures on how oil volatility would directly affect markets and stocks.

To summarise our results, out of the 5 major equity markets we tested in S&P500, ASX200, DAX, NIKKEI225 and SSE, it was only the SSE Index that was deemed to have been significantly affected directly by the volatility in oil prices. The relationship was a positive one, albeit a marginal one of 0.005979. However, when we limited our sample size to the most volatile period in the past year we found that the oil shocks had a significant impact on the ASX200, with a positive relationship of 3.80. We also confirmed by reviewing our low rsquared figures that it was probable our regression contained omitted variable bias. We also confirmed that the sample size in question was too small to create a robust regression that properly conveys the relationship between oil volatility and equity markets. Again, to reiterate our point we tested oil price volatility against our variables, which is a measure of 11

unexpected fluctuations in oil price and the risk involved, as opposed to testing for purely how the change in oil prices affected markets. In addition we also tested what impact oil volatility had against major banking stocks in the respective markets, with JP Morgan, NAB, Deutsche Bank, Mitsubishi UFJ and ICBC. We found that JP Morgan, Mitsubishi UFJ and ICBC had significantly impacted by the volatility in oil prices. All three had positive relationships, with ICBC having a lowest sensitivity of 0.022786 and JP MORGAN of 0.23820 whereas Mitsubishi was incredibly sensitive to oil volatility with a sensitivity factor of 7.604503.

In conclusion, the results we found in our regression is the opposite of what literature states, where our significant outputs state that the relationship between oil volatility and its dependant variable is positive. Whereas past literature states that over the long run, the relationship is negative where high fluctuations cause a contractionary response in global equity markets. The reason for the discrepancy in our results and previous studies would primarily be because of our sample size and lack of extra variables, where past research involved significant data, our regression captured only the past year. In addition, the level of expertise involved in the previous studies is far greater than ours, done by professionals in the industry. .

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Chapter 2 The Effect of Russian Currency Depreciation on World Economic Stability

Introduction Currency remains tightly correlated towards the oil prices being the main driver towards the Russian economy as per the chief economist (Dmitry Polevoy) for Russia at Groep NV in Moscow who projects that Ruble struggled to advance in September due to risks related to Federal Reserve rate decision and China (Bloomberg, 2015). From his statements made in early September 2015 in spite of a major downfall in December 2014 of nearly 50% towards the dollar (Figure 2.1), it definitely is a double-whammy of collapsing oil prices and western sanctions, which are quite frankly driving up inflation (Central Bank of Russia, 2014).

Figure 2.1 Source: Central Bank of Russia

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The Ruble weakened 8 percent in August as Brent crude, the grade used to price Russiaâ&#x20AC;&#x2DC;s main export blend, fell 7.4 percent. That retreat pushed the currencyâ&#x20AC;&#x2DC;s 30-day correlation with oil to 0.82 in Moscow, the highest according to Moscow Exchange data for the currency going back to 2003. (Bloomberg, 2015). All in all, the price of oil is being tracked by the Russian currency very closely as continuous month losses have resulted in a record low as per Figure 3 with regards to a direct correlation occurring on the Ruble to dollar exchange rate (Figure 2.2).

Figure 2.2 Source: Central Bank of Russia

Since the beginning of 2014, the capital outflow from Russia has reached record numbers. In the first three quarters of the year, it has already exceeded $85 billion (Figure 3). Mostly it happened due to the conflict in Ukraine and the resulting Western sanctions. One of the main results of capital outflow is an increased demand for foreign currency, in this case, U.S. dollars and euros.

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Economic stability factors that could get affected by Ruble As per moneycontrol (2015), below are the economic stability factors that could get affected

by Russian Ruble, ď&#x201A;ˇ

Global Trade Balances - Referring to imports and exports, this factor is a critical determinant of the global currency. According to Boggs (2015), when imports are greater than exports, you have a trade deficit. When exports are greater than imports, you have a surplus. A shift in the trade balance between two countries tends to weaken the currency of the country with greater deficit.

All in all, since value of Ruble is strongly linked to oil prices which are major drivers towards the Russian economy, the trade balances were affected as early as 2011 as you can see in Figure 2.3,

Figure 2.3 Source: Central Bank of Russia

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However, Russians know that they are neatly integrated with the rest of the world.

This basically means that when Russian consumers‘ spending power goes off a cliff thanks to the Ruble‘s more than 50 percent decline in 2014, the pain is going to be felt

by Western retailers. According to Newsweek 2014, Western banks, too, are nervous that the $610 billion of loans that they made to Russian companies and banks, while the good times rolled, are not going to be paid back.

This could have a heavy impact on the global debt issues, for example, the threat of Russian businesses defaulting on debt is spreading across even emerging markets such as India and Turkey, who, in theory, should be benefiting from lower oil prices as investment funds race to safe havens like the dollar. As per Vazquez, senior Latin America economist at Oxford Economics (ibtimes 2014), the currency crisis in Russia has also been a selloff in emerging market economies as 40-45% is Russian-driven due to the investor's mentality, which could also mean that if one emerging market tanks, all of them could go down. This seems quite true as emerging markets are already seeing spillover effects in commodity-centric countries such as Venezuela and Brazil. In special consideration , Venezuela has been hurt the most as their economy oil revenue consists of 95 percent of export earnings and the oil and gas sector is around 25 percent of gross domestic product, according to the Organization of the Petroleum Exporting Countries, 2014.

Moreover, around December 2014, American investment giant PIMCO underwent heavy losses on Russian Holdings as almost every Ruble option contract registered in the U.S has been made worthless and foreign-exchange brokers in New York and London told clients they‘re no longer taking Ruble trades (Bloomberg, 2014).

In a way, by clear evidences given above, the deflation in the economy and depreciation in Ruble is not only what worries the markets, but the adaptation of monetary policies after the occurrence of such issues is what will pave the way towards global stability. 

Exchange rate and the global stock market - It is important to note that the Ruble exchange rate is not only connected to the US dollar but also to the dollar-euro basket.

So when the Ruble value of the dual currency basket reaches the top or bottom boundary of the floating band, the Central Bank of Russia will carry out transactions on the sale (or purchase) of foreign currency at volumes equivalent to $350 million per day (Russia-direct, 2014). In November 2014, after a massive depreciation of 16

Ruble, the Central Bank of Russia let the exchange rate float freely; this saved it from speculative attacks. In the process, global bankers found it hard to earn on the exchange rate differences because since the rate was kept under a certain band, it was easy to predict on the same.

Now, when the Central Bank of Russia let the Ruble float freely, its exchange rate mostly depends on fundamental economic factors (Russia-direct, 2014). However, the Bank

can

intervene

unexpectedly

in

order

to

punish

speculators

by

increasing/decreasing the Ruble abruptly. Therefore the strategy the Bank of Russia is using will deter speculators because attacking a free-floating currency is risky and costly.

Russian Ruble crisis has also caused the main Russian index, RTS index and the value of the Russian stocks to significantly decline in value. In additional to these Russian financial assets, other financial assets such as European stocks - have also been affected. Many European companies such as BMW developed strategies to export products to Russia and heavily started relying on the revenues. The European indices, such as DAX 30, suffered losses. When initial sanctions were implied in March 2014, DAX declined in value from 9,307.79 to 9,107.79 within 2 days (Market Watch, 2014).

With the constant teasing of the US regarding their quantitative easy program for the hikes in interest rate as per planned, the Bank of Japan and European Central Bank already unleashed a massive monetary stimulus therefore, promoting quantitative easing. Question is whether ECB and BOJ‘s quantitative easing are enough to offset the Fed‘s current stance? As per Vazquez, senior Latin America economist at Oxford Economics (ibtimes 2014), if the Feds start removing liquidity starting next year, it would have more of an impact than what ECB and BOJ could do to fill that depreciation Ruble void. 

Political Factors - To keep the global economy consistently stable, it's imperative to regulate monetary policies and trade relationships with varied countries when we consider oil-price volatility and currency valuations. This seemed to detestable havoc starting 2014 for Russia as per Newsweek (2014). To understand the impact of Ruble towards global markets, we should first clearly understand the root of the political influences that underwent recently.

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According to BBC (2014), after Russiaâ&#x20AC;&#x2DC;s annexation in March 2014, the European Union (EU) and the U.S have ratcheted up sanctions several times, tightening restrictions on major Russian state banks and corporations. With immediate effect, these sanctions casted a much higher rate of haemorrhage than 2013 as capital worth $75 billion left Russia in 2014 itself. Also, geopolitical circumstances, namely the Ukrainian crisis, affect the strength of the Ruble directly influencing the investment climate in Russia and, hence, capital outflow.

We now look at major influences on how political relationships and trades would be influenced by this depreciation of Ruble, globally speaking.

Economic impact of Sanctions According to Hufbauer, Elliott, Oegg and Schott (2007), several stage sanctions can be distinguished which is collectively called diplomatic sanctions, referring to the withdrawal of ambassadors and suspension of international negotiations. According to Dreger et. al (2015), Russia was excluded from the G8 meetings and bilateral talks on cooperation agreements and visa regulations were suspended. Restrictions to certain industries focus on banking, energy and defence sectors, for example, US prohibited commercial relations between US firms and sanctioned companies, most importantly, Bank Rossiya, SMP Bank and Volga Investment.

Such trade restrictions were followed on by EU as well but hurt them more in the process. Specifically, Germany who gets 30% of its oil and gas from Russia. Italy is also highly dependent on Russian energy and some of Russia's former Soviet bloc neighbours rely 100% on its gas deliveries. With depreciation of Ruble to 65 on a dollar the local companies in Russia are looking for further ways to hedge against currency volatility for exporting of goods. This according to Bloomberg (2015), is hard as Russia banned setting prices in currencies other than rubble in 2006. In other departments, sales of new cars fell by 27% from the previous year alongside the travel industry which took a heavy blow indicating that the key driver - consumer demand is consistently losing steam in Russia. W.r.t the ruble depreciation, China has also backed out of the oil and gas pipelines deals that it just made with Russia. Russia refused to allow the Chinese to build the pipelines and the cost of the oil and gas that were to be delivered in a few years are much higher than the regular price now (Emerging Equity, 2015).

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In its entirety, trade restrictions would raise costs for Russia but in the process harms the sanctioning countries as well. Countries such as Germany, Italy, China and Japan would be hit through lower growth perspectives considering that the Russian Ruble is forecasted to stay highly volatile. Countries sanctioning always have their own interests in mind but according to a study by Hufbauer, Elliott, Oegg and Schott (2007) who examined a large set of sanctions concluded that only one-third of them have been successful, at least partially.

To support our statements below we will determine through economic regression on whether bulk of depreciation is caused by decline in oil prices or not. Also, unanticipated sanctions matter for the conditional volatility of the variables that would be involved.

Econometric analysis The analysis will be to determine any relation between depreciation of Ruble towards decline in oil prices. The variables included for this regression would be the Ruble exchange rate towards the dollar, the oil price, composite indicators on sanctions from and against Russia.

Apart from that, the unexpected components of sanctions would be built from the residuals of the regression. Also since the Central bank of Russia reacted a lot of times to soften the depreciation we take into consideration the RUONIA (Ruble overnight index average) which is basically the interbank rate for overnight loans.

These variables include data taken between 1st January, 2014 to 31st March 2015 and the exchange rate and oil prices are logged whilst the RUONIA is on percentage terms.

Since we understand that there can be more than one cointegrating relationships among the variables, the Johansen test was used.

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Table 2.1 Cointegration Properties (Source: Dreger et. al 2015)

From table 2.1 we can observe that the long run parameters are well intact. In equilibrium, a rise in oil prices and an increase in the RUONIA will further lead to a decline in the Ruble value, which is basically an appreciation towards the U.S dollar. Also, the western sanction implementation is accompanied by the Ruble depreciation while the Russian sanctions merely compensate this effect.

Now we look at the dynamics of the conditional variances of VAR residuals as exhibited in table 2.2 significantly expanding on mediaâ&#x20AC;&#x2DC;s effect towards the Ruble decline.

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Table 2.2 Conditional variances of VAR errors (Source: Dreger et. al 2015)

In above, for the GARCH (1,1), the media index is allowed to drive the volatility of the required variables. A delay of one week (five lags) is put in for the expected impact potential of the media to take place. The reported effects are significant to the margin of 20% significance level. According to the findings, the GARCH effects are relevant in each of the cases with clear persistence for the oil price and the Ruble errors. We can also observe that media do not have an impact.

All in all, with unexpected sanctions playing a role for Russia, the additional volatility comes into picture as far as international commodity markets go. Since this will harm the global economic growth, policy decisions should be as transparent as possible. This effect is especially visible for western sanctions, but also relevant to the Russian sanctions. Since early 2014, Ruble had been increasingly depreciating against the US dollar which basically started during the conflict between Ukraine and Russia. Due to Russiaâ&#x20AC;&#x2DC;s relative openness, the economy is deeply exposed to exchange rate fluctuations. Furthermore, the impact on Russia was openly impacted by the western sanctions later.

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As Russia is heavily dependent on exports of natural oil, the decline of the price of the same could be another factor behind its sudden and constant decline since 2014. The above regressions showcase that the bulk of the depreciation is caused by the decline in oil prices.

Also, the conditional global volatility was brought up because of the unanticipated sanctions imposed on Russia considering the variables involved.

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Chapter 3 Hedging Solutions for Equity Investment in the Current Oil Price Volatility

Introduction Based on our research we can deduce that a negative relationship between oil price volatility and the equity markets exists, especially in the long run. From our findings, we have determined that the best way to hedge in the equity market is to use combination and spread strategies. These strategies assist investors decrease the risk of negative effects on equity markets in relation to an increase in current oil price volatility. However, the trade-off with such strategies is that the maximum potential profit is limited.

We have decided that the most suitable solutions to hedge in the volatile equity market is to use the following alternative strategies: long straddle, long strangle, synthetic long put, and hedging ETFs with a bear put spread.

Hedge ratio The hedging ratio is critical for any hedge fund manager or trader. The Hedge Ratio works out the amount of future contracts the investor needs to purchase in relation to their portfolio value. It is derived from the value of the portfolio divided by the value of the futures then multiplied by the Beta. As illustrated below;

N = number of contracts = Value of portfolio = Value of Futures β = Beta

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Long straddle The long straddle strategy is used for stocks or equity markets which are highly volatile. A trader or a hedge funds manager would utilise this strategy if they encompassed a bullish view on volatility. This strategy can be used as an excellent hedging tool for equity markets which have a significant correlation with oil price volatility.

Construction: Purchase- 1 call option at the money with the same expiration date as the put option purchased.

Purchase -1 put option at the money with the same expiration date as the call option purchased.

Figure 3.1 Options long straddle diagram. Source: theoptionsguide.com

The three main factors to consider are volatility, price and time. An increase in implied volatility will increase the probability that the stock market will move in a desired direction.

Time decay is another significant factor that influences our prospect of success. The less time until expiration date the lower likelihood of a desired outcome. Price of the options is also 24

extremely important when using options. The price should be at least fair value, this is dependant is which option pricing a hedge fund manager will employ.

Table 3.1 Long straddle option

Long 1 at-the-money call option Long 1 at-the-money put option Maximum profit

Unlimited

Maximum risk

Limited (Premium paid)

Breakeven point Upper break even = Strike price of call + net premium.

Lower break even = strike price of put â&#x20AC;&#x201C; net premium.

The benefit of using a long straddle in comparison to a long strangle, is that volatility does not have to move to the same magnitude, this is since the call and put options purchased are the same strike price and at the money. However, we still require high movement in either direction for this strategy to be successful.

The key danger to the long straddle is that expected volatility subsides to a lower level than we had anticipated and that the stock price does not move a great deal from the strike price.

The worst case scenario is that the stock remains at the strike price and we lose both the call and put premiums.

Long strangle The long strangle is employed by hedge fund managers and traders when the market conditions are considered to be very volatile. This particular strategy optimal use is for to hedge equity markets which are highly sensitive to oil price volatility.

Construction: Purchase 1 call option which is out of the money with the same expiry date.

Purchase 1 put option which is out of the money with the same expiry date.

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Figure 3.2 Options long strangle diagram. Source: theoptionsguide.com

The same three major features of volatility of the stock or equity, the price of the call and put options and time to expiration are relevant to the long strangle option as in the long straddle.

Table 3.2 Long strangle option

Long 1 out-of-the-money call option Long 1 out-of-the-money put option Maximum profit

Unlimited

Maximum risk

Limited (Premium paid)

Breakeven point Upper breakeven = Strike price of call + net premium.

Lower breakeven = strike price of put â&#x20AC;&#x201C; net premium.

The advantage of the long strangle in comparison to the long straddle is that premium costs are lower. This is due to purchasing the call and put options out of the money. This is a more aggressive hedging strategy as the volatility must move significantly for it to be a profitable exercise.

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The major risk of this strategy exists; if the equity market volatility is relatively quiet to what the outlook was expecting. In this instance, loss is limited and the hedge fund manager loss is the premium paid for the call option plus the premium paid for the put option.

Synthetic long put A synthetic long put hedging strategy is to combine buying a call option and short selling a stock. This allows us to create a long put position without buying a put. Traders and hedgers use this strategy to protect against sudden increases or decreases in volatility. Synthetic options are less affected by volatility and time passage. The hedging ratio is one-on-one where for every 100 short stocks; we buy a call option (Lehman 2011).

Table 3.3 Synthetic long put

Long 1 Call option Short 1 stock Maximum profit

Limited

Maximum risk

Limited

Breakeven point

Strike price + premium paid

To create a synthetic long put, we buy a call option and short sell a stock. This combination simulates a long put position. We make profit when the stock price trades down by the expiration date. The lower the stock price the higher the profit.

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Figure 3.3 Synthetic long put diagram. Source: theoptionsguide.com

The maximum profit is substantial when the stock price declines. The potential profit is the price of selling the short stock minus the premium paid for the call option. The maximum potential loss when the stock price increases is limited. It is the strike price minus the premium paid. The breakeven point is the strike price of the long call plus the premium (Jovanovic 2014).

Hedging ETFs An Exchange-traded-fund (ETF) is a basket of securities traded on stock exchange markets.

ETFs track stock indices, specific industries, or commodity futures such as oil. Investing in ETF options is similar to investing in stock options in that the investor has the same potential profit and risk when buying options.

Some of ETFs features include offering diverse exposure, liquidity, lower costs, and they accurately reflect the value of the tracked index or commodity. Exchange traded funds provide traders the ability to invest in a specific industry or commodity.

There are two types of ETFs listed on the stock exchange markets that are related to the oil industry: ETFs that invest in oil company stocks and ETFs that invest in crude oil futures.

One of the most popular commodity ETFs is the U.S. Oil Fund LP (USO) that invests in

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crude oil futures with one month to expiry date. This ETF is very sensitive to the movement of oil prices in the short-term (Bloomberg 2015).

Our proposed strategy to hedge ETFs is to establish a bear put spread as discussed below.

Bear put spread To hedge ETFs in a bearish market, the trader implements a bear put spread, also called a long put spread. To create a spread, the investor buys an in-the-money ETF put option and sells an out-of-the-money ETF put option. This spread strategy is profitable when the underlying ETF price decreases.

Table 3.4 Bear put spread

Long 1 put option at strike A Short 1 put option at strike B Maximum profit

Limited

Maximum risk

Limited

Breakeven point

Strike A - net premium

The strategy is to buy a put option at strike A and sell a put option at strike B. The purchased put option at strike A costs more than the sold put option at strike B which has the same maturity date. By selling a lower strike put, this strategy partially funds the bearish outlook of the underlying stock (De Weert, 2007)

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Figure 3.4 Bear put spread diagram. Source theoptionsguide.com

In this strategy, the trader makes a limited but substantial profit when the ETF price goes down. The maximum profit is the difference between strike A and strike B minus the net premium. The maximum loss is the net premium. The breakeven point is the price of the purchased put option with the higher strike minus the net premium. Increases or decreases in volatility will not have a substantial effect on the outcome.

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Chapter 4 Current Oil Price Volatility and Global Financial Crisis

We believe that in critical analysis, the current issues with respect to the previous recession factors, we could line up the assessment needed for concluding our analysis towards the same. As mentioned in the first chapter, the following have played a major role in the movements of oil as of late. We would express these critical roles with major global power players to depict our analysis.

Weakening global demand a. Commodity price effect As stated by the IMF (2015), considering the weak outlook for energy, metals, commodity prices and the growth in commodity-exporting developing economies could slow down over the coming years. Also a study published by the World Economic Outlook (2015) state that the current declines in the commodity prices could shave off about 1% point annually from the commodity exporters growth rate over 2015-17 as compared to 2012-14. Considering the energy commodities, it‘s even larger by about 2¼ % on an average.

All this obviously has a structural component to it. Potential output tends to grow more slowly in exporters. Same case is with the investments. They tend to aggravate the post boom slowdown. Oil importers on the other hand are taking full advantage of this fact. According to Adam Longson, an analyst at Morgan Stanley (2014), the strong fluctuations of oil severely hurts demand and ―gives buyers reasons to wear themselves off oil‖, where in addition consumers now have more alternatives for energy than only oil based sources. The sudden shift in supply and demand and stalled recoveries from Europe and Japan are helping oil importers in the long run. As per the Economists (2014), the country that consumes more than it produces gains from about $1 trillion dollars (Importers). This tends to highlight the fact that commodity prices follow same course as compared to the oil prices. According to Michael Cohen of Barclays (bank), a $20 drop in the world oil price would reduce American producers‘ earnings before interest by 20% (Economist, 2014). The problem with this is, how rapidly production would fall as a result of this is quite unclear as it varies by regions. We 31

have managed to find a relation of commodity prices to that of crude oil during and before the financial recession to emphasis on the correlation between the two as shown below,

Figure 4.1 Global commodity prices (1995-2009), Source - (Obstfield & Rogoff, 2009)

As you can see above, the commodity prices have been following the same path as compared to the crude oil with a sudden decline at the time of the 2008 recession. The gradual intertwining of both lines towards 2008 shows significant outlining of both variables towards global economic growth.

Letâ&#x20AC;&#x2DC;s take a closer look towards the commodity prices and oil price relations,

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Figure 4.2 Global statistics, Source - IMF(a), 2015

From above we can see that the commodity prices fell 0.8 % in Sept, 2015. Over 9 months of decline in the prices have accumulated around 21% which is led by a 24% drop in the crude oil prices (IMF(a), 2015).

It is quite clear that the supply is up, demand is down and the conditions that have created the crudeâ&#x20AC;&#x2DC;s quick price slump in late 2014 could remain in place for some time.

b. Emerging Markets & Europe As far as the emerging markets go, attractive valuations are unfortunately not enough for the market to rally. In a way the market needs a change in narrative.

The tearing of the multi-year commodity price bubble i.e., Chinaâ&#x20AC;&#x2DC;s managed growth slowdown, has been shockingly damaging for those countries which would had otherwise benefitted from the low input oil prices. For example, U.S should had gained from the steep 33

fall of oil prices however the sudden shift from spending to saving has resulted in a very little pass-through effect to the lower commodity prices.

Also, Turkey should had been a beneficiary by low oil prices as the oil imports represents approximately 60% of the country‘s trade deficit. However, the brutal combination of geopolitical tensions and wrongly evaluated policies overwhelmed Turkey‘s balance sheet improvements (Lazard 2014).

Spending cuts in the gas and oil industry has been attributed to the weaker oil prices which eventually reduced economic activity and created job losses in the emerging countries.

However, in Asia, India is the one reaping the benefits. Oil accounts for a third of India‘s exports. Furthermore, cheaper exports handle inflation which according to Economist (2014) has already fallen to 10% in 2013 to 6.5% in 2014. This leads to lower interest rates for India, therefore, upgrading the investments for the country. In support of our statements, according to the DIPP (2015) website, the total FDI received is $28 billion which is the highest FDI destination.

In Europe, diesel demand grew by 7.2% in Q1 of 2015 which coincides with the shock drop in oil prices around that time. This helped in buoying brent oil prices according to Reuters (2015). It is important to assess diesel as a strong fundamental ramification for outlook on the brent oil as diesel constitutes 45% of Europe‘s commodity output and 43% of its demand (Reuters, 2015). According to CNBC (2015), Croatia, Slovakia and the Czech Republic are set for 2-3% growth in 2015 which seems to be the only countries benefiting in Europe from the net lowering of oil prices and commodity prices.

All in all, if you carefully look at the figure for Europe you can see that the Brent which is the benchmark crude oil slumped by 21% since May 2015 meets the common definition of a bear market.

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Figure 4.3 Gasoline prices to Crude oil market

Furthermore, in the emerging markets, as tested by Jason Daw, head of Asian FX strategy at Société Générale, analyzed 17 periods from year 2000 till 2015. They found out that the all emerging market currencies except for Chinese Renminbi tends to weaken during the examined periods including the oil importers such as Turkey, India, South Africa, Singapore and South Korea which furthermore proves our point that weaker oil prices means slows economic activity.

Geopolitical risks According to the World Bank (2015), Geopolitical developments have had much less of an impact than what has been present in the past. With the threat of ISIS in Iraq, and the Russia Ukraine conflict, the volatility of oil was initially very high. But During mid-2014, as it became evident that the conflicts did not disrupt the supply of oil, the volatility tapered off.

In addition, the alternative oil industry of Shale oil, Sand oil and Biofuels are immune to geopolitical risk and provide buyers with a more stable supply.

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OPEC With the tensions in the Middle East easing mid-2014, the lid on the supply of oil was removed, with civil war ravaged Syria and Iran being able to produce crude oil once more.

The result of this in conjunction with weakening demand brought upon a fall in oil prices (Plumer, 2014). In response, OPEC engaged itself in a price war with oil producers in the US, where they began allowing the prices to keep dropping with the expectation that new drilling projects in the US would become far too unprofitable to proceed with and eventually shut down (Worstall, 2015). This is increasingly risky due to the fact that a majority of OPECâ&#x20AC;&#x2DC;s members are developing countries whose economies rely heavily on the price of oil. The reason for this prompt by OPEC is due to the Shale oil extraction boom in the US which added an extra 9 million US barrels per day into the global market. The result of this was a considerable loss of market share for OPEC (Worstall, 2015). However the conflict in the Middle East, with civil war in Syria and tensions in Iraq offset the effects of the booming shale oil on global prices until recently where tensions began easing and the supply of oil from those countries began increasing (Plumer, 2014). This combination of an increase in supply of crude and shale oil in addition with weaker global demand began to drop prices significantly. Consequently, during OPECâ&#x20AC;&#x2DC;s 2014 meet they opposed the idea of reducing production, and agreed on keeping it unchanged which would result in further downward pressure on the price of oil.

Ever since OPEC was formed in 1960, its policies affecting the oil market has impacted the world economy multiple times with severe consequences of economic downturns (OPEC, 2010). According to Kumar Ashok (2009), plenty of major economic crises since 1973 have had very strong correlations with major oil shocks which have been the reason of pricing policies on behalf of OPEC.

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Figure 4.4 US recessions and Global Oil Price, source: (WTRG Economics, 2011)

Referring to the figure above, we can see the relationship between the volatility of oil prices as a result of OPEC policy and how the US economy was impacted. We can observe that that the oil price shocks are strongly correlated with US downturns, with multiple shocks coming as a result of OPEC policy.

In accordance with history, the recent impact of OPECâ&#x20AC;&#x2DC;s decision has created severe surges in the volatility of oil, with economies reacting favourably and adversely. Developing countries that are net commodity and oil importers are certainly profiting from lower inflation, import costs and financial spending pressures, According to the World Bank (2015) a decrease in 10% of oil prices would result in an increase of 0.1-0.5% GDP in oil importing countries.

Where the impact of the lower oil prices is meant to boost Chinaâ&#x20AC;&#x2DC;s activity by 0.1-0.2%, with the country on track to becoming the largest oil importer in the world. Additionally, several large countries such as Brazil, India and South Africa have enjoyed lower inflation, increased buyer sentiment, lower import costs and a smaller deficit (WorldBank, 2015). However, net exporting commodity and oil countries are facing massive adversity, The World Bank (2015)

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states that GDP of oil exporting countries could drop by 0.8-2.5% following a 10% fall in prices. Russia is forecasted to drop by 2.7% and Canada already in a recession with the economy shrinking by 0.8% for second quarter in a row. The major superpower in trouble due to OPEC policy would be Russia, whose oil exports account for 75% of total export income with experts forecasting a recession in mid-2016 if oil prices do not increase.

In essence, it comes down to which side succumbs first. If the shale oil producers of the US start to considerably fall off due to the high volatility in oil, the prices would stabilize in the short run due to the moderation of supply in response to demand. However, if Saudi Arabia and OPEC face too much downward economic pressure off the price in oil would have to reduce the production to meet equilibrium in demand.

In terms of concluding whether or not this volatility will cause a new global financial crisis, we have to objectively look at Russia which is the major superpower in trouble whose oil exports account for 75% of total export income with experts forecasting a potential recession in mid-2016 if oil prices do not increase. Also the unexpected sanctions imposed on Russia as explained on Chapter 2 create additional volatility on the international market which in turn affect economic growth and imposes drastic policy changes for major power players.

All in all, for the purposes of this assignment we will proceed under the evaluated assumption that a negative relationship exists where an increase in volatility results in a fall in returns of market as estimated by our regression analysis. However, please note, for further rigorous analysis it is important for us to use the updated data with continuous timely regressions solely because of the reason that unanticipated news and factors have to be expediently estimated.

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Chapter 5 How the Current Situation May Affect the Recovery of Greece and the Australian Economy in the Short Run

How the recovery of Greece would be affected We would now talk about how the current oil-price volatility would affect the recovery of Greece. But first, we need to understand the oil-output relationship for Greece based on the global economy.

We know that Greece is a medium-sized economy heavily dependent on oil and is thereby more vulnerable than the other economies towards the changes in the oil-price international market. They rely excessively on energy imports with minor domestic oil reserves. As per Papapetrou (2009), the Greece energy dependency was 71.9% in 2006 which was well above the European average while the oil consumption represents 64% of all energy consumptions in the country. Keeping in mind that Greece is a European Union country which experienced extremely remarkable growth rates after the early nineties enabling the country to enter the Euro-zone, Greece went through turmoil in the past decade. Factors responsible? Rising unemployment, lack of fiscal and monetary policies and sharp declines in investment expectations.

The key question however is how Greece would react in the future towards the oil supplyglut with unpredictable price volatility? There are 3 primary points we think would affect the Greece‘s economy based on the oil-price volatility. 

Investor’s demand - With lowering demand of oil in Greece and the current stature European countries are in, as the Euro declines further against the US dollar, it could get more expensive for Europe to buy oil in spite of the current oil supply-glut. Fact is the more time the oil prices take to bounce back, the more volatility will increase (Moors, 2015). It is this lowering in demand that could eventually push the prices lower. This would further decline Greece from potential investments in the country.

As per PWC (2015), the recovery of Greece is dependent on investments and investments are based on trust by the markets. With euro declining further it would seem impossible to drive potential investors towards the country. To support this fact, 39

we look at Figure 5.1; the main reason for Greece‘s recovery problems has been the investment gaps constantly present. And with the current euro rates, Greece could be looking at further increasing that investment gap by 2016. This in turn would definitely increase distrust within the system as budgetary improvements will not bring the desired recovery needed from the country (PWC, 2015).

Figure 5.1 Greece investment gap, Source: International Monetary Fund, 2013



Bond Yields - The lack of confidence regarding the non-Greek investors towards Greece‘s past failures to implement critical reforms and the political system‘s inability to affect change in addressing the real problems highlight the fact that Greek companies borrow at a lower cost as compared to the country itself (PWC, 2015). The deficit can be clearly seen when we compare the interest rate of bonds issued by the Greek companies (e.g. Hellenic Petroleum, OTE, Titan) and the ones issued by the Greek companies established in other European countries (e.g. Coca-Cola HBC)(Figure 5.2).

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Figure 5.2 Bond Yield, Source: PWC,2015

As per figure 5.7, we can clearly see that Coca-Cola HBCâ&#x20AC;&#x2DC;s are at 2.4% whereas the others are at 6% average yield.

In its entirety, Greek company issuances occupy the highest levels of high-yield debt, requiring a premium to be paid over the other bonds. Furthermore, this will increase the expenses of conducting oil businesses and costs of running future oil contracts thereby, cutting into the return and will also lower the attractiveness of the oil even further (Moors, 2015).

ď&#x201A;ˇ

Tourism - As per PWC (2015), for tourism to flourish again in Greece, it is quite imperative that the country takes advantage of the oil supply-glut so that producers of petrol and diesel help in booming the tourism industry. Tourism being a traditional extrovert sector, to attract funds from investors, it must improve both the real estate and the hotel management. Since transportation costs are going to get cheaper by the excess supply of oil in the country, the government should focus on emphasising on flexible tourism which indirectly could help in getting trust from the non-Greek investors.

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Also because of the geographical positioning of the country, Greece could benefit from trade activities and energy distribution. By entrusting these factors back to the government and by showcasing timely improvements, the banks could release assets trapped in their balance sheets (PWC, 2015).

Oil-Price volatility impact on the Australian economy With plunging oil prices and its hefty impact on a global scale, Australia is being impacted by the brutal yet sustained fall in the oil price. The oil-price volatility seems to represent a new paradigm for the Australians. Late October, CitiGroup reported that the fall in prices was massive over that month which was equivalent to 25 basis points cut on the interest rate more than $3.1 billion per annum (Sydney Morning Herald, 2015). Since then the oil price has experienced even a larger plunge, over the past six months as it has come off by 55 percent overall.

All in all, people will have more money in their pockets which in turn would take out the pressure off the cost of living thereby also driving the shoppers to dive into the discretionary retail space, at least, in theory. Local business would take this as an opportunity for large scale setups or expansions of their establishments. It will also help reduce the price of items that use oil, example, plastics and cost of transporting goods. In theory, these should eventually enable the supermarkets to cut the price of goods as well.

But, the main question is how much time would this new lower-costing energy environment take to sink in the Australian economy? Consumers are not the only cautious-ones about the future of where this oil-price volatility would lead. Even the Reserve Bank of Australia are planning to make a decision on interest rate movements by February 2016. Assuming that the oil-price volatility would not recover, the central bank would have to critically decide on whether or not to ignore this effect for inflation and consumer price index. Naturally the RBA would discount the direct effects of volatile price movements, looking instead at the underlying catalyst of inflation. Furthermore, looking at the airline industry, there have been no reported comments from Qantas or Virgin airlines regarding the oil prices as fuel is a big component of their business (Sydney Morning Herald, 2015).

Because of the supply-glut from shale gas producers, Russia and OPEC, there have been casualties on the Australian territory as well. For example, producers of LNG such as Origin, Oil Search and Santos. According to Reuters (2015), commodity analysts have a divided 42

opinion about how much further the oil prices would drop and how long it would stay there.

For the impact of these factors we look at the import and export relation which can be highlighted under terms of trade. Bulk commodities prices have fallen since February 2015 and Australiaâ&#x20AC;&#x2DC;s terms of trade are comparatively lower as a result (RBA, 2015). Below you will find the RBA forecasts (terms of trade) for coming years (Figure 5.3).

Figure 5.3 Terms of Trade, Australia, Source: RBA

The banks forecast highlights that the terms of trade have been revised to 1 and a half per cent which is consistent with the overall decline in the commodity prices because of the oil supply-global glut. These forecasts are based on price of the Brent oil of $70 per barrel which is 19 per cent higher than the actual rates in October 2014. However, this is in-line with the near-term futures pricing (RBA, 2015).

Moreover, the inflation and the growth output is a vital component for economy forecasts and below you can see the forecasted inflation till the year 2017 which is based on current policies and legislation (Table 5.1).

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Table 5.1 Output growth and inflation forecasts in %, Source: RBA

As per table 5.1, if the current oil slump pushes up to $70 per barrel and sticks for a few years as expected by analysts (Reuters 2015), then, LNG exports are expected to grow strongly by around ¾ percentage points towards the GDP by 2016/17. The decline of headline inflation over the past year could partly be explained by the lowering of automobile fuel prices and the repeal of the carbon tax policies.

If we take a broader look at the impacts of the Australian economy, the assumptions and underlying reasoning towards our forecasts could have drastic variations from the predictions for a couple of reasons. One of the key sources of uncertainty is on the basis of the Chinese economy. The developments there have a critical impact on the commodity prices, particularly for iron ore and coal which then affects the terms of trade and in-turn would affect the exchange rate drastically. Also, the local government investments and RBA interventions would play a major role in deriving consequences from the oil-price variations.

Furthermore, Australia‘s trading partners are likely to be imparted stimulus because of low energy prices, particularly of oil, since they are oil importers such as Woodside and Santos.

Figure 5.5 showcases a general forecasted view of the growth consisting of Australia‘s major trading partners. The data was taken from Reuters (2015), RBA (2015) and CEIC (2015).

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Figure 5.4 Australiaâ&#x20AC;&#x2122;s trading partner growth*, Source: RBA, CEIC, Reuters

After briefly discussing the forecasts for trading partners, inflation and the terms of trade, we lastly, touch on S&P ASX 200 energy index. With oil and gas producers having a hard time by the decline in S&P ASX 200 Energy index (as per Figure 5.6), the potential of energy sector seeing the bright light at the end of the tunnel seem slim (Yahoo Finance, 2015).

Figure 5.5 Monthly movement in the S&P ASX 200 Energy Index, Aug 2013 - Oct 2015

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We now calculated the rate of return for the S&P ASX 200 energy index to get a sense of the change in return volatility in the energy sector.

Figure 5.6 Weekly movement in the S&P ASX 200 Energy returns, Aug 2013 - Oct 2015

As you can see from Figure 5.7, the returns do show increased volatility particularly since November 2014 with heavy fluctuations further on.

Should Australia be worried? The economy is already in shock from the recent fall in oil, gas and iron ore prices which definitely will not help the state governments in managing their budgets. However, these same oil price drops will help other industries in Australia such as the primary industry which depends on petrol, fertilizers and diesel that are produced from oil and gas. Thereby, it is expected that these primary industries will become more profitable because of the fall in prices.

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APPENDICES APPENDIX - A

S&P500 Dependent Variable: D(S_P500) Method: Least Squares Date: 10/27/15 Time: 18:31 Sample (adjusted): 8/05/2014 7/31/2015 Included observations: 250 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

VOL(-1) C

2.380191 -5.077198

2.999403 7.295988

0.793555 -0.695889

0.4282 0.4871

0.002533 -0.001489 15.59779 60336.18 -1040.513 0.629729

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.659400 15.58619 8.340103 8.368275 8.351442 2.055633

0.428213

ASX200 Dependent Variable: D(ASX200) Method: Least Squares Date: 10/27/15 Time: 14:57 Sample (adjusted): 8/05/2014 7/31/2015 Included observations: 247 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

OIL_VOL(-1) C

1.783352 -0.001146

1.853693 0.001420

0.962054 -0.806920

0.3370 0.4205

0.003764 -0.000303

Mean dependent var S.D. dependent var

S.E. of regression

0.008630

Akaike info criterion

Sum squared resid

0.018247

Schwarz criterion

Log likelihood F-statistic

824.3978 0.925547

Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared

52

0.000114 0.008629 6.659091 6.630675 6.647651 1.947590

Prob(F-statistic)

0.336971

DAX Dependent Variable: D(DAX) Method: Least Squares Date: 10/27/15 Time: 18:36 Sample (adjusted): 8/05/2014 7/31/2015 Included observations: 244 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

OILVOL(-1) C

4.854691 -3.419742

23.48115 59.90854

0.206748 -0.057083

0.8364 0.9545

0.000177 -0.003955 137.7388 4591219.

-1547.005 0.042745

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

8.831355 137.4672 12.69676 12.72542 12.70830 2.020648

0.836380

NIKKEI Dependent Variable: D(NIKKEI) Method: Least Squares Date: 10/27/15 Time: 15:51 Sample (adjusted): 8/05/2014 7/31/2015 Included observations: 246 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

OILVOL(-1) C

32.61495 -59.06704

42.34802 104.4213

0.770165 -0.565661

0.4419 0.5721

0.002425 -0.001663 196.2139 9393977.

-1646.739 0.593154

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.441947

53

20.77537 196.0509 13.40438 13.43288 13.41586 2.069672

SSE Dependent Variable: D(SSE) Method: Least Squares Date: 10/27/15 Time: 16:23 Sample (adjusted): 8/05/2014 7/31/2015 Included observations: 238 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

OILVOL(-1) C

0.005979 -0.013310

0.003238 0.008452

1.846418 -1.574784

0.0661 0.1166

R-squared Adjusted R-squared

0.014240 0.010063

Mean dependent var S.D. dependent var

S.E. of regression

0.020663

Akaike info criterion

Sum squared resid

0.100762

Schwarz criterion

Log likelihood F-statistic

586.5973 3.409260

Hannan-Quinn criter.

Durbin-Watson stat

Prob(F-statistic)

0.066084

54

0.002099 0.020768 4.912582 4.883404 4.900823 1.853213

APPENDIX â&#x20AC;&#x201C; B Limited Sample S&P500 Dependent Variable: D(S_P500) Method: Least Squares Date: 10/28/15 Time: 01:47 Sample (adjusted): 8/05/2014 2/27/2015 Included observations: 143 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

VOL(-1) C

2.350492 -4.599035

3.296786 8.186037

0.712965 -0.561815

0.4770 0.5751

0.003592 -0.003475 16.14327 36745.34 -599.6561 0.508319

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

1.157413 16.11530 8.414771 8.456210 8.431610 1.962960

0.477046

ASX200 Dependent Variable: D(ASX200) Method: Least Squares Date: 10/27/15 Time: 14:59 Sample (adjusted): 8/05/2014 2/27/2015 Included observations: 142 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

OIL_VOL(-1) C

3.802513 -0.001947

2.000563 0.001436

1.900721 -1.356173

0.0594 0.1772

R-squared Adjusted R-squared

0.025156 0.018193

Mean dependent var S.D. dependent var

S.E. of regression

0.007865

Akaike info criterion

Sum squared resid

0.008661

Schwarz criterion

Log likelihood F-statistic

487.5513 3.612740

Hannan-Quinn criter.

Durbin-Watson stat

Prob(F-statistic)

0.059394

55

0.000477 0.007938 6.838750 6.797119 6.821833 1.848752

DAX Dependent Variable: D(DAX) Method: Least Squares Date: 10/27/15 Time: 18:37 Sample (adjusted): 8/05/2014 2/27/2015 Included observations: 140 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

OILVOL(-1) C

-6.865862 33.64987

21.69817 56.47438

-0.316426 0.595843

0.7522 0.5523

0.000725 -0.006516 116.5250 1873773.

-863.7789 0.100125

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

16.05372 116.1472 12.36827 12.41029 12.38535 2.156891

0.752157

NIKKEI Dependent Variable: D(NIKKEI) Method: Least Squares Date: 10/28/15 Time: 01:45 Sample (adjusted): 8/05/2014 2/27/2015 Included observations: 141 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

OILVOL(-1) C

35.13385 -63.02545

45.55563 113.5821

0.771230 -0.554889

0.4419 0.5799

0.004261 -0.002903 203.4085 5751127.

-948.5087 0.594795

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.441880

56

23.57049 203.1139 13.48239 13.52422 13.49939 2.127463

SSE Dependent Variable: D(SSE) Method: Least Squares Date: 10/28/15 Time: 01:47 Sample (adjusted): 8/05/2014 2/27/2015 Included observations: 133 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

OILVOL(-1) C

0.004112 -0.007848

0.002594 0.006965

1.585181 -1.126777

0.1153 0.2619

R-squared Adjusted R-squared

0.018821 0.011331

Mean dependent var S.D. dependent var

S.E. of regression

0.015221

Akaike info criterion

Sum squared resid

0.030350

Schwarz criterion

Log likelihood F-statistic

368.9039 2.512799

Hannan-Quinn criter.

Durbin-Watson stat

Prob(F-statistic)

0.115336

57

0.002993 0.015308 5.517351 5.473887 5.499689 2.162635

APPENDIX â&#x20AC;&#x201C; C JP MORGAN Dependent Variable: D(JP_MORGAN) Method: Least Squares Date: 10/27/15 Time: 18:27 Sample (adjusted): 8/05/2014 7/31/2015 Included observations: 250 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

VOL(-1) C

0.238207 -0.521102

0.133668 0.325146

1.782076 -1.602671

0.0760 0.1103

0.012644 0.008662 0.695116 119.8302 -262.8115 3.175795

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.053012 0.698146 2.118492 2.146664 2.129830 2.084484

0.075960

NATIONAL AUSTRALIA BANK Dependent Variable: D(NAB) Method: Least Squares Date: 10/27/15 Time: 18:24 Sample (adjusted): 8/05/2014 7/31/2015 Included observations: 247 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

VOL(-1) C

0.029180 -0.059197

0.057874 0.145068

0.504197 -0.408062

0.6146 0.6836

0.001037 -0.003041 0.344186 29.02373 -86.03043 0.254214

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.614576

58

0.013108 0.343664 0.712797 0.741213 0.724238 1.999051

DEUTSCHE BANK Dependent Variable: D(DEUTSCHE_BANK) Method: Least Squares Date: 10/27/15 Time: 18:30 Sample (adjusted): 8/05/2014 7/31/2015 Included observations: 250 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

VOL(-1) C

0.048708 -0.109063

0.109917 0.267370

0.443132 -0.407909

0.6581 0.6837

0.000791 -0.003238 0.571599 81.02799 -213.9013 0.196366

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

Durbin-Watson stat

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.008330 0.570676 1.727211 1.755382 1.738549 2.128303

0.658056

MITSUBISHI UFJ Dependent Variable: D(UFJ) Method: Least Squares Date: 10/27/15 Time: 18:15 Sample (adjusted): 8/05/2014 7/31/2015 Included observations: 246 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

VOL(-1) C

7.604503 -0.004390

3.693411 0.002741

2.058937 -1.601461

0.0406 0.1106

R-squared Adjusted R-squared

0.017077 0.013049

Mean dependent var S.D. dependent var

S.E. of regression

0.015485

Akaike info criterion

Sum squared resid

0.058511

Schwarz criterion

Log likelihood F-statistic

677.2378 4.239223

Hannan-Quinn criter.

Durbin-Watson stat

Prob(F-statistic)

0.040562

59

0.000875 0.015587 5.489738 5.461239 5.478263 1.943337

INDUSTRIAL AND COMMERCIAL BANK OF CHINA Dependent Variable: D(ICBC) Method: Least Squares Date: 10/27/15 Time: 18:05 Sample (adjusted): 8/06/2014 7/31/2015 Included observations: 237 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

VOL(-1) C

0.022786 -0.057242

0.013698 0.035769

1.663518 -1.600338

0.0975 0.1109

R-squared Adjusted R-squared

0.011639 0.007433

Mean dependent var S.D. dependent var

S.E. of regression

0.087292

Akaike info criterion

Sum squared resid

1.790694

Schwarz criterion

Log likelihood F-statistic

242.6382 2.767291

Hannan-Quinn criter.

Durbin-Watson stat

Prob(F-statistic)

0.097542

60

0.001507 0.087619 2.030702 2.001436 2.018906 1.875869