Multi-asset risk modeling: techniques for a global economy in an electronic and algorithmic trading era PDF
Preview Multi-asset risk modeling: techniques for a global economy in an electronic and algorithmic trading era
Multi-Asset Risk Modeling Multi-Asset Risk Modeling Techniques for a Global Economy in an Electronic and Algorithmic Trading Era Morton Glantz Robert Kissell AMSTERDAM(cid:129)BOSTON(cid:129)HEIDELBERG(cid:129)LONDON NEWYORK(cid:129)OXFORD(cid:129)PARIS(cid:129)SANDIEGO SANFRANCISCO(cid:129)SINGAPORE(cid:129)SYDNEY(cid:129)TOKYO AcademicPressisanimprintofElsevier AcademicPressisanimprintofElsevier 525BStreet,Suite1800,SanDiegoCA92101,USA 225WymanStreet,Waltham,MA02451,USA TheBoulevard,LangfordLane,Kidlington,Oxford,OX51GB,UK r2014ElsevierInc.Allrightsreserved. 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ISBN:978-0-12-401690-3 ForinformationonallAcademicPresspublications visitourWebsiteatwww.elsevierdirect.com PrintedintheUnitedStates 14 15 16 17 18 10 9 8 7 6 5 4 3 2 1 To my wife Maryann,an endless sourceoflove,patience, and inspiration. To my wife Felise, anendless sourceof love andencouragement. Preface Multi-Asset Risk Modeling: Techniques for a Global Economy in an Electronic and Algorithmic Era is written to assist readers improve their understanding financial risk and risk management practices in a new and ever volatile environ- mentandacrossmultiple asset classes. Thetextfocusesontheapplicationofpropervolatilityandfactormodels,optimi- zation techniques and provides insight into the evaluation of traditional and non- traditional sources of risk (for example, extreme value events, and electronic and algorithmictradingrisk).Readerswilllearnhowtoproperlyevaluatenon-traditional sourcesofrisk,andmonetizefinancialinvestmentdecisionsinreal-time. We describe the latest and most advanced risk-modeling techniques for equi- ties, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management. With mathematics playing a prominent role, we present standard risk-management and asset allocation models and more advanced extensions, discuss the laws in stan- dard models that contributed to the 2008 financial crisis, and talk about current and future banking regulation. Importantly, we will also explore algorithmic trad- ing, which currently receives sparse attention in the literature. You will study extreme value functions, explore Basel III compliance modeling specifically in areas of specialized lending, develop concepts of sustainability management, and work with risk-hedging techniques. Emphasis throughout the text will focus on applying an financial risk management framework and proper utilization of statis- tical analysisrequired for fiduciary decisionmaking. Primary readership includes graduate students, industry practitioners, risk managers, money managers, bankers, regulators, corporate risk officers, accoun- tants, andCFOs. In order to address the essential aspects of Multi-Asset Risk Modeling, the book is arranged in fourteen chapters, each providing a specific focus. A descrip- tion ofthe chapters inthe book is as follows: In Chapter 1, Introduction to Multi-Asset Risk Modeling (cid:1) Lessons from the Debt Crisis, we review how statistical pricing and risk-forecasting models con- tributed to the debt crisis. For example, many of these models provided improper insight for risk analysis, and lead to mispriced collateralized debt obligations, mortgage backed securities andcredit derivatives, aswell as misguidealgorithmic trading instructions. The chapter shows how risk managers can incorporate views and information sets across different asset classes to improve risk forecasts in rapid changing mar- kets. We present an overview of different types of risk and risk management pro- ducts. In particular, we discuss details inherent in credit and equity risk: price risk (volatility), operational risk, country risk, default risk, company specific risk, liquidity risk, interest rate risk, operational risk, macro-economic factor risk; how xix xx Preface money managers, investors, and risk managers expand their understanding of risk and infer real-time information from derivatives and options, FX rates, debt mar- kets, and macro-economic changes. Finally, we discuss components of algorith- mictrading (cid:1)the industry’s lifeblood. In Chapter 2, A Primer on Risk Mathematics, we discuss applications of risk modeling, providing an overview of the mathematics, statistics, and probability required to measure, analyze, forecast, and measure risk. We discuss probability theory and statistical analysis, unbiased estimates, time series math, linear regres- sion, and non-linear estimation techniques including logit and probit models. The chapteralsoprovides anoverview ofsomeoftheimportantstatisticsused toeval- uate regression models such as T-test, F-test, and R2 goodness of fit. We provide an overview of the linear algebra technique used to estimate model parameters and corresponding parameter errors to develop confidence intervals surrounding the estimates. The chapter concludes with an in-depth discussion of probability distribution functions and their use in finance and risk management. This include continuous distributions, discrete distributions, and extreme value functions such as Gumbel (type I), Frechet (type II), and Weibul (type III). Extreme value func- tionshavegainedrecenttractioninthefinancialindustrysincethefinancial crisis, and have become essential risk-managementtools across all assetclasses. Chapter 3 preserves the quantitative focus as we learn to develop uncertainty forecast models. A Primer on Quantitative Risk Analysis provides the fundamen- tals of quantitative risk analysis that is required knowledge and a prerequisite for more advanced applications in financial services. The chapter begins with an interesting historical perspective of risk, followed by discussions of the basic characteristics of risk and the nature of risk versus returns. It then provides detailed hands-on applications of running quantitative Monte Carlo risk simula- tions using Real Options Valuation’s Risk Simulator software. Other topics pre- sented include correlations and precision control, setting seed values, setting run preferences (simulation properties), and creating a simulation report and forecast statistics table. Finally, the chapter wraps up with two hands-on exercises for using the software on running risk simulations and understanding the diversifica- tion effects of correlations on risk. Readers are invited to reinforce concepts pre- sented in Chapter 3 by visiting www.realoptionsvaluation.com for free videos, case studies, and models,as well asdownloadabletrial versions ofthe software. Chapter 4 explores, in mathematical and practical terms, Price Volatility. We open with an overview of price-volatility forecasting models. We describe the mathematics behind these models and provide an in-depth analysis of scaling properties. We discuss various techniques employed in the industry to forecast volatility as well as appropriate methods to calibrate these models. We expand volatility concepts by providing readers with findings and empirical evidence across time period and asset classes. We will further develop scaling properties of volatility over time. This chapter also reiterates and expands on techniques pre- sented in the Price Volatility Chapter in The Science of Algorithmic Trading and Portfolio Management (Kissell,2013). Preface xxi Chapter 5 concentrates on defining the challenges and use of Factor Models. Factormodelshavegainedincreasedstatus.Muchofthisisduetocontinuedmar- ket turmoil and asset price uncertainty. Analysts are now turning to factor risk models to estimate asset returns and corresponding financial risk rather than rely- ing on their traditional approaches that all too often have proven unreliable. Our goal using factor risk models is to determine a set of factors (e.g., explanatory variables) that explain price movement. Analysts able to successfully forecast these factors or factor returns will be in a position to successfully forecast asset returns (the ultimate goal of financial management!). Factor models have also become increasingly widespread for estimating asset volatility, especially covari- ance and correlation across asset returns. This chapter also reiterates and expands on techniques presented in the Price Volatility Chapter in The Science of Algorithmic Trading andPortfolio Management (Kissell,2013). Moving on to Chapter 6, Equity Derivatives, we introduce readers to the equi- ties derivatives market and related financial instruments. We explore options, for- wards, futures, and swaps. We show how practitioners can infer risk from the different derivatives via implied volatilities and implied correlations. The chapter begins with a focus on the various options-pricing models (Black-Scholes, bino- mial trees, etc.), put-call parity, and pricing call and put options. We introduce the reader to the different derivative risks: delta, vega, theta, and gamma, as well as volatility smiles and volatility skew. We then expand on these models and show how the implied volatility and implied correlation measures derived from these instrumentscanleadtobetterreal-timeriskmetricsandrisk-monitoringsys- tems. We provide an in-depth analysis of the financial crisis period of 2008-2009, and show how these equity derivatives risk metrics provided practitioners with better metrics and more timely market insight. Readers will gain a thorough knowledge of the different risks and modeling approaches used in the equity deri- vatives asset class and proper methods to apply these approaches and build more efficient risk metrics. With increased globalization comes an increased need to understand the for- eign exchange markets. In Chapter 7, we present readers with an overview of the Foreign Exchange Market and Interest Rates, spot prices, forwards, futures, cross-currency interest rate swaps, and options. We explore the economics corre- sponding to exchange rates and international trades, differences between fixed and variable exchange rates, the gold standard, and provide an overview of the different currency risk metrics. We apply the mathematical models and approaches developed in Chapter 2 to develop best-in-class FX volatility and cor- relation risk models. The chapter concludes with practical examples and applications. We study Algorithmic Trading Risk in Chapter 8. Algorithmic trading repre- sents computerized executions of financial instruments. Currently, algorithms are being used to trade stocks, bonds, currencies, and a plethora of financial deriva- tives. The new era of algorithmic trading has provided investors with more effi- cient strategy implementation and lower transaction costs, resulting in improved xxii Preface portfolio performance. In addition to these advantages and savings comes a new set of algorithmic risks. Here we introduce readers to the algorithmic trading pro- cess and corresponding uncertainty. We follow the techniques presented in The Science of Algorithmic Trading and Portfolio Management (Kissell, 2013) and adapt these techniques for Risk Management. Readers interested in a more thor- oughexamination ofalgorithms can obtain(Kissell,2013). In Chapter 9, Risk Hedging Techniques, we provide mathematics behind some of the more advanced portfolio-hedging techniques. In particular, we discuss deri- vation of the hedge ratio andits usage in finance todetermine the optimal manner to hedge a held portfolio. We examine the hedge ratio in terms of various pricing models, such as CAPM and APT, and discuss the ratio in terms of portfolio dol- lars and weights. The chapter concludes with a general solution to the optimal hedging ratio problem thatmovesacrossassetclass and investment instruments. We move on to Chapter 10, Rating Credit Risk: Current Practices, Model Design, and Applications. The credit crisis of 2008(cid:1)2009 was in many ways a credit-rating crisis. The financial crisis might not have happened without credit- ratings agencies issuing stellar ratings on toxic mortgage securities. Doubts about credit-rating agencies arose due to alleged conflicts of interest and the alleged backward-looking nature of the analytical process. Structured finance products, such as mortgage-backed securities, accounted for over 11 trillion dollars of out- standing U.S. debt. The lion’s share of these securities was highly rated. For example,morethanhalfofthestructuredfinancesecuritiesrated byMoody’scar- ried AAA ratings, the highest credit rating, typically reserved for near-riskless securities. Important point: Financial institutions should develop industry- and deal-specific internal risk models. Internal ratings, because they are founded on “know thy customer,” provide a potent framework for assessing multi-asset port- folios. The internal risk models discussedunderstand client fundamentals. We offer specialized lending risk models proposed by the Bank for International Settlements including project finance, commodity finance, real estate, and object finance. In addition, the chapter includes an industry-specific, deal- specific corporate system and the CAMELS rating, the United States supervisory rating of a bank’soverall condition. Risk rating is developed further in Appendix 1 toChapter10,CorporateRiskRating:ObligorandFacilityGradeRequisites. Chapter 11, A Basic Credit Default Swap Model, provides readers with an introduction to credit derivatives. The credit derivative market has emerged as one of the most dynamic and innovative sectors of the global financial system. Creditderivativecontractsarefinancial instrumentsthattransferbetweentwopar- ties the risk and return characteristics of a credit-risky reference asset. As such, they have become an integral part of the risk management and investment strate- gies of global investors and intermediaries. We explore the credit derivative mar- ketplace, examine the instruments from various different angles, including applications, valuation, and control, and demonstrate how each provides perspec- tive on the essential elements of the marketplace. Finally, readers review the “bad” side of these assets, so-called toxic credit derivatives and their role in the Preface xxiii recent debt crisis. The term “toxic asset” was coined during the recent financial crisis to refer to mortgage-backed securities, collateralized debt obligations, and credit-default swaps, none of which could be sold after they exposed their holders tomassive losses. Chapter 12, Multi-Asset Corporate Restructurings and Valuations, discusses techniques for decision making in the presence of risk, such as valuating corpo- rate restructuring strategies under uncertainty. In particular, we discuss asset allocation and restructuring, risk budgeting, and stress testing. We present techniques based on real-world data to construct multi-asset class portfolios, and provide techniques to minimize risk, manage factor exposure, and maximize alpha. This chapter relies heavily on techniques presented above, and incorporates Monte Carlo simulations and stochastic optimization as a means to uncover potential extreme movements. An appendix to Chapter 12, A Banker’s Guide: Valuation Appraisal of Business Clients, was developed for readers who operate as financial consultants and advisors. Recent history tells us that it is unwise to discount the possibility of extreme events. Fat tail risk is real, and the real world does not fit neatly into a bell curve. Chapter 13, Extreme Value Theory and Application to Market Shocks for Stress Testing and Extreme Value at Risk, deals with extreme value functions and the role these functions play in the financial services. If credit portfolio losses were bell shaped, we could specify the likelihood of large losses by defining portfolio expected and unexpected loss. The problem is that individual debt assets have very “skewed” loss probabilities. For instance, AAA debt assets enjoy a near-zero standard deviation, while a B-rated debt asset may have a five standard deviation within its distribution. In most cases, the obligor does not default, and the loss is zero, but when default occurs, the loss is substantial. Given the positive correla- tion between defaults, this unevenness of loss never fully resolves. There is always a large probability of relatively small losses, and a small probability of rather large losses. We explore value at risk and extreme value functions and show derivations. The chapter concludes with a comprehensive extreme value modelingcase authored by Dr. JohnathanMun. Tail risk is the bane of a financial institution’s revenue model, and always has been. Chapter 14, Ensuring Sustainability of an Institution as a Going Concern: An Approach to Dealing with Black Swan or Tail Risk by Karamjeet Paul explains that despite rigorous models and risk management controls, financial institution exposure from tail risk can accumulate. For highly leveraged financial institutions, cumulative exposure from tail risk can threaten survival in a stressed environment. While traditional models normally work well in relation to portfo- lios, they do not address certain critical issues related to policies, governance, limits, strategies, and guidelines to manage the total risk of an institution. What should be the goal of tail risk management? How much tail risk does the institu- tion have? How do you manage tail risk proactively? As we begin our journey through the chapters, it might be helpful to remem- ber that quantitative methods, such as the use of advanced models or the xxiv Preface employment of math, do not alarm sharp professionals. Modeling tools that work are not black boxes that ignore or inhibit wisdom or that mechanize the decision- making process. The traumatic experience of the debt crisis taught us that finan- cial institutions require fresh multi-asset risk models controlled by risk managers with,above all, good common sense. Morton Glantz Robert Kissell
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