Risk Management Special Report trading technologies for financial-market professionals
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October 2013
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2013 Over the last 20 years, Waters has grown from a single print publication to a multi-brand, multi-screen, multiplatform, content-rich user experience. Business and technology executives within global financial trading firms continue to rely on us for insights into the issues they face as consumers and purchasers of financial technology With independent expert journalism, we continue to serve and engage with the financial technology community offering, analysis, news and features, through our websites, apps, publications, conferences, training, briefings, webcasts, videos, awards, reports, whitepaper content creation and lead generation. www.waterstechnology.com Sell Side
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Once Bitten ... ne would hope that the maxim, “Once bitten, twice shy,” could be used to appropriately describe the financial services industry in a post-financial-crisis dispensation, characterized by enlightenment, prudence, and dare I say it, even a modicum of humility. Only time will tell. In many respects, risk management practices across the financial services industry were a ticking time bomb leading up to the financial crisis of 2008. While all market participants had at least some degree of risk management framework in place, what transpired in the wake of Bear Stearns’ and Lehman Brothers’ failures—the two highest profile bank collapses that contributed in no small way to the global credit crisis and the chain of financial and psychological events that ultimately dragged the world’s economy to the very brink of complete meltdown—was, once the dust had settled, a return to the risk management drawing board where any and all financial services firms scrutinized their processes and procedures, and questioned their assumptions and models to ensure that if and when another similar event occurred, they would be better prepared to deal with the contagion. Leading up to the financial crisis, all but the most sophisticated risk management practices were backward-looking and were invariably run on an overnight basis, given the complexity of the calculations and the computing resources needed to run measures such as Monte Carlo simulations and stress-tests. Needless to say, real-time risk management or even intra-day risk snapshots, generating enterprise-wide risk “maps,” and managing counterparty credit risk on an ongoing basis during the course of the trading day, were, until recently, pipe dreams for all but the very largest sell-side firms with the deepest budgetary pockets. And, while those scenarios might still ring true for some of the industry’s laggards, it’s fair to say that the 2008 clarion has been well and truly heeded. In the virtual roundtable section of this special report, which starts on page four, participants look at the extent to which buy-side and sell-side firms have embraced the move to managing their various exposures on a real-time basis; the technical, operational, and, in certain cases, the cultural challenges that need to be addressed before firms are in a position to monitor their risk on a real-time or intra-day basis; and the pivotal role that data plays in their risk management endeavors, along with the associated challenges around ensuring its cleanliness, reliability, uniformity, and the ease with which it can be shared across the enterprise. Only once these questions have been satisfied can the real-time risk management label be considered meaningful. ■
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Victor Anderson Editor-in-Chief
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Special Report Risk Management
Deloitte Study Finds Increased Risk Management Technology Spending Advisory firm Deloitte’s eighth biennial survey on risk management practices concludes that 65 percent of financial services firms have upped their risk management and compliance spending, while 58 percent say more increases are expected in the next three years. The most encouraging result from the study is that 17 percent of participants expect a significant budget increase of at least 25 percent, much of which will focus on improving technology. Less than a quarter say their data management, process architecture, workflow logic, or data governance platforms were “extremely” or “very” effective. Meanwhile, less than half
consider their operational risk technologies similarly effective. Deloitte points out that the latter sentiment remains mostly unchanged
from 2010—though on specific risk measures including liquidity, credit, and sovereign risk, increased confidence was reported, as was the usage of enterprise risk management (ERM) platforms, now implemented by 62 percent of respondents. The survey also identified a growing disparity between small and large firms, with those topping $10 billion in assets subject to more regulatory scrutiny earlier, and therefore are better prepared. Survey participants included chief risk officers (CROs) and senior risk managers at 86 financial institutions, with $18 trillion in combined assets under management.
Bloomberg Integrates LCH.Clearnet’s Swaps Margin Calculator Bloomberg’s Professional Service can now directly access LCH.Clearnet’s SwapClear Margin Approximation Risk Tool (Smart) to determine capital funding requirements for swaps. Smart is designed to help users anticipate and manage how large price movements affect capital needs. By locating risk analytics on the same platform as clearing and execution portals, the collaboration will allow Bloomberg
clients to instantaneously simulate portfolio exposure and pre-cleared margin requirements, with no additional cost. The Bloomberg Professional Service is the first multi-asset trade execution platform to integrate the tool, with major buy-side firms including Scotlandbased Aberdeen Asset Management among its early proponents. The financial information mainstay is among a handful of technology provid-
ers registering or intending to register as a swaps execution facility (SEF) under new Dodd–Frank Act rules.
Northern Trust Launches Risk Reporting Capabilities Northern Trust has expanded its investment risk reporting capabilities to include ex-ante, or forward-looking, functionality. For this release, through a single consolidated report, users can see comparisons of risk levels and potential “risk mismatch” alongside the user’s managed assets. This means it is possible to look at how investment risks would impact funding
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levels and the ability of a fund to meet its liabilities over time, as well as how changes in liabilities may open up opportunities to reassess investment decisions, Northern Trust officials say, adding that this aims to aid clients in making informed decisions about more sophisticated liability management solutions. Clients can see, at a glance, how risks compare between assets and liabilities
and understand potential mismatch risks. Northern Trust has also added active allocation and risk contribution reporting. It claims the tool can help clients understand the risks associated with deviating from their stated target asset allocation. Finally, the Chicago-based fund administrator added also trend analysis that aims to add context around current risks.
Sponsor’s Statement
The Price of Everything, the Value of Nothing The financial crisis taught us many things, including the subjectivity and general inadequacy of the pricing and valuation models that the industry had come to rely on. S&P Capital IQ’s Dan Rosen proposes a fourpart approach to ensure these failings remain in the past. rom 2007 through 2009, the financial industry experienced a once-in-a-lifetime crisis, which nearly brought down the entire system. A report by the International Monetary Fund in April 2010 estimated bank write-downs and loan provisions between 2007 and 2010 at $2.3 trillion, with about two-thirds of these losses (or about $1.5 trillion) realized by the end of 2009.1 Originating first in the US subprime market, before spreading around the globe, the credit crisis highlighted many limitations of the industry’s general valuation practices and our understanding and management of risk. Market participants clearly misunderstood and underestimated the risks in many securities, especially with respect to systemic risk, default correlation, contagion effects, and liquidity. The crisis further made evident the intrinsic limitation and subjectivity of valuation models and pricing assumptions. When liquidity is thin, dealer quotes are unreliable and model parameters cannot be estimated based only on observed market prices. A quote by dramatist and poet Oscar Wilde comes to mind: “Nowadays people know the price of everything and the value of nothing.” If Wilde had been alive today, his quote might have read: “Before the crisis, people knew the price of everything and the value of nothing. Now they know neither of them.” When dealing with complex structures and markets with limited liquidity, it is important to understand the meaning and use of a price. We must acknowledge the sometimes “heroic” assumptions in models and the limitation of the information that we can reasonably extract from the market.
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Lessons Learned: The Four Rules of Valuation The credit crisis highlighted limitations of the industry’s general understanding of pricing and risk management practices. Regulators and industry associations have highlighted the
need for transparency and the inadequacy, in general, of some standard valuation and risk modeling approaches used by industry participants. For example, in the case of structured credit portfolios, practitioners relied too heavily on dealer quotes and external credit ratings. Several reports have been written that explore the underlying causes of the crisis, and discuss lessons for structurers, investors and regulators. 1. Independent valuation and internal modeling capabilities For investment institutions, it is essential to have valuations that are independent of the dealer and traders. Independent valuation must not be done in a theoretical vacuum—it must be intimately linked to market practices and a clear understanding of liquidity. According to the Senior Supervisors Group (2008) report, firms that performed better in late 2007 had developed in-house expertise to conduct independent assessments of the credit quality of assets underlying the complex securities to help value their exposures appropriately. In contrast, firms that faced more significant challenges tended to rely too passively on pricing services to determine values for their exposures. 2. Transparency, transparency, transparency! Valuations and risk management methodologies must be transparent in order to be useful to stakeholders. A transparent valuation process allows users to understand the key drivers of the price and the underlying risks. The methodology and the assumptions must be well documented and accessible. Models’ assumptions should be highlighted and reviewed continuously. All potential sources of risk must be incorporated and discussed. 3. Valuations based on good models based on fundamentals, not just dealer quotes The credit crisis underscored the need for effective, transparent and robust valuation methods for complex and illiquid securities,
and specifically for structured finance securities and credit derivatives. Consistency of valuation is also required across asset classes. The models and assumptions must be transparent. It is critical to ensure that there are good models, which capture all the risks and are implemented with good, reliable data. As the markets come under stress and credit instruments become illiquid, the reliance on dealer quotes must be thoroughly investigated. Firms should now realize the need for an infrastructure to model these positions internally and to develop analytics to check valuations and compare them with market quotes. In the case of structured finance securities, the commonly applied valuation models depended significantly on agency ratings, and this led to severe valuation issues throughout the crisis. 4. Understand model risk and vulnerabilities Model risk can be loosely defined as the risk involved in using models to value and measure the risk of financial securities and portfolios. From the perspective of marking-to-market a trading desk or an entire institution’s portfolio, model risk refers to the use of models for pricing products that are not reliably observed in the market. Given the limitations of models, the uncertainty in the underlying data and prices and the illiquidity in the market, it is important to develop a systematic approach for capturing and communicating model risk. ■ Attribution: Rosen, Dan. 2010. Re-Thinking Valuation: The Credit Crisis, Illiquid Markets and Model Risk. In: Rethinking Risk Measurement and Reporting, pp. 955–973. 1
These estimates are, of course, subject to considerable uncertainty and range of error, including data limitations, measurement errors from consolidation, inter-country variations, changes in accounting standards, and uncertainty on assumptions about exogenous variables.
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A Work in PROGRESS
Risk management practices across the financial services industry are not new by any means, but they are continually being tweaked by buy-side and sell-side firms as they look to generate more regular and granular views of their exposures, increasingly across the entire organization. This virtual roundtable investigates the extent to which firms are managing their risk on an intra-day or real-time basis, and the technical challenges associated with generating such snap-shot risk analyses.
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Roundtable
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To what extent are buy-side and sell-side firms looking to manage their risk on a real-time or intra-day basis? Tim Clark, chief risk officer, Triad Futures: I think many buy-side firms limit risk by choosing funds and managers that fit their needs and risk profiles. Most do not look at risk in real time, but firms like AlphaMetrix have platforms that do an excellent job for buy-side firms that want real-time risk controls.
Debbie Williams Vice President, Business Development S&P Capital IQ Tel: +1 888 806 5541 Web: www.capitaliq.com
Garrett Nenner, managing director, Rosenblatt Securities: As strategies have become more complex, with multiple security types across the board—especially with the unusually reactive present-day volumes and the corresponding price volatility—realtime risk control has certainly come to the fore as a significant concern. Intra-day risk management is better suited to the longerterm investor who is expecting a larger move in price over that term—which can be days, months, and so on—where volatility and price movements tend to be smoothed out over the lifetime of that held position, and is less of a present concern and more of a general need-to-know.
of risk reporting, sell-side firms can at least now access both fast risk numbers on the desk, and firm-wide numbers with reasonable accuracy at an aggregate level. During this time, buy-side risk practices were largely ignored while regulators focused on market risk and then credit risk for the banks and brokers. The credit crisis brought renewed attention to the Jeremy Skaling, interplay between market head of product and credit risks, and the “The credit crisis brought renewed attention to the interplay management, impact on investors and their between market and credit risks, and the impact on investors Eagle Investment portfolios. The differences and their portfolios. The differences between the buy side and Systems: Since between the buy side and the sell side, however, will define an entirely new ‘efficient the financial crisis, the sell side, however, will buy-side investment define an entirely new frontier’ for risk solutions. Real-time risk management on managers have been “efficient frontier” for risk the buy side is focused on two core capabilities: the ability to integrating risk solutions. Real-time risk update positions and market data on demand, and the ability to management on the buy side management into their daily operations run real-time scenario analyses while adjusting holdings and is focused on two core capaalongside performance market conditions to test strategies and understand impact. bilities: the ability to update measurement and positions and market data Debbie Williams , S&P Capital IQ attribution to better on demand, and the ability understand the risks taken to generate returns. This move to to run real-time scenario analyses while adjusting holdings and consolidate risk measurement with daily performance cycles market conditions to test strategies and understand impact. also provides an accurate view for exposure analysis. Integrating risk and performance on a single, cleansed and validated data What risk measures are buy-side and sell-side firms platform removes the data quality inefficiencies that are inherent looking to extrapolate on an intra-day basis, and what when operating across data silos. This also allows for accurate are the business benefits of producing such measures? and timely analysis. Williams: We see our clients producing a very wide range of metrics. Asset managers are inherently a fairly diverse group, Debbie Williams, vice president, business development, both in terms of the instruments they hold, as well as the strateS&P Capital IQ: For sell-side firms, risk worked hard to catch gies they pursue. Given this diversity, it’s not surprising that up to the speed with which exposures were moving. Over the risk metrics that are used are very wide-ranging, as well. the last 15 to 20 years, front-office systems added risk analysis Long-only equity investors are focused on understanding risk in capabilities and risk management groups built out their own a relative sense, and watch their expected tracking error closely. monitoring tools. Although, as an industry, we are still defining Fixed-income investors and long/short strategies require a the “efficient frontier” between speed and accuracy at all levels
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larger variety of absolute measures, including both credit and interestrate sensitivities. If markets begin to move intra-day, this group of risk measures can also move quickly depending on how the portfolio is positioned. Because of this, we see managers recalculating a core group of measures several times each day. Additionally, we see them recalculating these measures based on potential market scenarios and hypothetical holdings, as well. The days of producing a single risk number, such as value at risk (VaR), are gone. The complexity of the underlying holdings and the interconnectedness of the markets mean that a variety of measures are needed to truly understand which positions would adversely impact profit and loss (P&L) when the markets move. Skaling: We often see buy-side firms implementing rigorous processes around creating daily forward-looking ex-ante risk measures. This is in addition to the ex-post measures that they may already be producing as part of their daily performance and reporting processes. Firms are introducing risk measures such as VaR, present value and dollar value of a basis point, key-rate durations, as well as numerous analyses based on correlations and covariances. The benefits of producing these risk measures are that asset managers can now get a better view of the risk present in the investment decisions that are being made. Since the financial crisis, moving to a daily operation and institutionalizing these risk measures alongside performance measurement and attribu-
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“There are few off-the-shelf offerings that suit the variations in strategy and security types away from equities, such as in multi-asset class portfolios. Those that do offer this, require modification, and therefore, additional cost to satisfy the needs of the strategy.� Garrett Nenner, Rosenblatt Securities tion analysis is now an important part of the risk and exposure analysis that firms are looking to institute as part of their operations. Clark: I would say they are looking at portfolio risk, where the investments fit, and whether there is drift in those investments. Nenner: This is a short question for a complicated and situational answer: It depends on the strategy, implementation of that strategy, metrics used for that implementation, and cues determined for risk removal. The business benefits, of course, are improved slippage, loss mitigation, and greatest opportunity for improved P&L.
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What are the technical challenges facing buy-side and sell-side firms when it comes to managing their risk on an intra-day basis? Skaling: The technical challenges start with ensuring a solid data management foundation, making sure that the inputs needed for risk measurement are available and of high quality.
Roundtable
Jeremy Skaling Head of Product Management Eagle Investment Systems Tel: +1 781 943 2200 Web: www.eagleinvsys.com
“Creating an enterprise solution that starts with high-quality investment data and ends with state-of-the-art scalable analytic processing, which are integrated with business intelligence and reporting, is a challenge that can be fulfilled today.” Jeremy Skaling, Eagle Investment Systems
To meet the requirements of comprehensive risk reporting capabilities, an advanced technical and software architecture is required. The architecture must be capable of dealing with the heavy quantitative processing nature of real-time risk analytics. Creating an enterprise solution that starts with high-quality investment data and ends with state-of-the-art scalable analytic processing, which are integrated with business intelligence and reporting, is a challenge that can be fulfilled today.
portfolio managers have not had on-demand access to complete and accurate portfolio information due to reliance on execution venues and brokers and separate back-office processes. Additionally, the buy side is less likely to have tech-savvy staff available in the front offices to program models, and create scenarios from scratch. The sell-side tools that had proliferated for risk just don’t fit well with the buy-side asset class mix, focus on scenario analysis, and staffing resources.
Williams: From a technical perspective, the sell side is relatively well prepared to calculate risk because real-time market data, as well as trades and net-position data, is already available. The big challenge here is getting the front office and the middle office— which has historically relied on overnight processes—on the same page. We see this as more of an operational process issue, rather than a technical one, as the two have historically relied on different risk methodologies, different levels of aggregation, and even different internal definitions. On the buy side, these challenges of concurrence also exist, but there are even more fundamental issues. Historically,
Clark: I see the main challenge being that you have to find the proper tools to view risk, and someone experienced to watch the risk. There are shrink-wrapped systems and platforms that do an excellent job. After the financial crisis, many brokers cut back on giving out risk reports. Nenner: There are few off-the-shelf offerings that suit the variations in strategy and security types away from equities, such as in multi-asset class portfolios. Those that do offer this, require modification, and therefore, additional cost to satisfy the needs of the strategy. Up until the last year or so, many risk manage-
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ment tools in this space were an in-house endeavor. Decisions made to do so are ruled by build versus buy and total cost of ownership. The data is available, so it’s not the information that is the issue—the challenges arise in the absorption of that data; how to utilize it; development and coding; latency incurred, and managed through the process; and the skilled personnel to design and implement. This can be a rather large and expensive endeavor in resources, time and money.
the flow of the analytical calculations with fine granularity. The calculations need to be transparent and should be based on recognized analytical platforms, such as Fincad or Matlab. It should also be flexible to invoke the firm’s risk models. The last ingredient required is dynamic reporting capacity to present the results of the valuable calculations.
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To what extent are modern risk management practices contingent on sound data management What are the ideal technical ingredients that realtechniques? time or intra-day risk platforms should include? Williams: All risk management is fundamentally dependent on Nenner: This is similar to question two as it again depends on data and, therefore, risk management cannot exist without data what is important to the strategy: solid low-latency data, highest management. In fact, risk measures that are implemented without quality hardware, superb programming and quantitative skills, data management are probably more dangerous than not having and everything in between risk measures at all. Since risk necessary to the specific management is inherently a decision-making framework, bad strategy at hand. data creates a level of misinfor“I think risk is contingent on sound management mation that almost certainly leads Williams: There are four key ingredients to any real- and good data. Poor management can lead to to bad investment decisions. time risk platform: outsized risk and not doing proper due diligence. • A clean, reliable source of I think there can be a problem of trying to eliminate Clark: I think risk is continfundamental market and gent on sound management risk by creating a portfolio that is completely instrument data and good data. Poor manageoffsetting and has difficulty making money.” • An easy-to-use interacment can lead to outsized risk tive reporting mechanism Tim Clark, Triad Futures and not doing proper due dilithat provides drill down gence. I think there can be a and multiple, customizproblem of trying to eliminate able views on exposures risk by creating a portfolio that • A scenario capability that doesn’t require a PhD or a week of is completely offsetting and has difficulty making money. programming time to define and run • A set of pre-defined metrics that runs the full gamut of market Skaling: It cannot be stressed enough that solid data manand credit risk measures, and the ability for users to create agement processes and architecture are crucial elements of custom measures, as needed. comprehensive, advanced risk reporting. Without these, every step further downstream in the network of analytical processing Clark: Again, I would mention platforms like AlphaMetrix. is questionable. The process must always begin with clean and AlphaMetrix has every conceivable real-time risk measure, they high-quality data to result in quality analytical results. watch for style drift—along with making sure managers stay within their stated parameters. It does not make economic sense Nenner: It’s all about the data, its timely receipt, and how it to replicate such platforms. They are also monitoring unconven- is “filleted” and disseminated. Issues occur, from latency and tional risks such as MF Global-type situations, as we are here. technical issues, and so on, and contingencies must be prepared for. Take, for instance, the data flow issues between Arca and the Skaling: Firms implementing real-time or intra-day risk Nasdaq-operated Securities Information Processor (SIP) issue platforms should start with a message-based, in-memory, and on August 22. Regardless of the cause, those without direct elastic application architecture that can execute ex-ante risk calconnection to the proprietary feeds were unable to retrieve full culations in parallel. Risk engines built using this architecture information from the consolidated book. Where you collect need to calculate risk measures at both the portfolio and security your data from and how it is managed are only two of the many level simultaneously and provide computing resources for cost possible issues to address in order to maintain effective risk efficiency. The platform should provide the ability to monitor management. ■
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