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Peter Christoffersen - One of the best experts on this subject based on the ideXlab platform.
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Financial Risk measurement for Financial Risk Management
National Bureau of Economic Research, 2012Co-Authors: Torben G Andersen, Peter Christoffersen, Tim Bollerslev, Francis X DieboldAbstract:Current practice largely follows restrictive approaches to market Risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in Financial econometrics and are likely to produce more accurate Risk assessments, treating both portfolio-level and asset-level analysis. Asset-level analysis is particularly challenging because the demands of real-world Risk Management in Financial institutions - in particular, real-time Risk tracking in very high-dimensional situations - impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market Risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market Risk, but also cross-fertilize the academic and practitioner communities, promoting improved market Risk measurement technologies that draw on the best of both.
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estimation Risk in Financial Risk Management
2005Co-Authors: Peter Christoffersen, Silvia GoncalvesAbstract:Value-at-Risk (VaR) and Expected Shortfall (ES) are increasingly used in portfolio Risk measurement, Risk capital allocation and performance attribution. Financial Risk managers are therefore rightfully concerned with the precision of typical VaR and ES techniques. The purpose of this paper is exactly to assess the precision of common models and to quantify the magnitude of the estimation error by constructing confidence bands around the point VaR and ES forecasts. A key challenge in constructing proper confidence bands arises from the conditional variance dynamics typically found in speculative returns. Our paper suggests a resampling technique which accounts for parameter estimation error in dynamic models of portfolio variance. In a Monte Carlo study we find that commonly used practitioner methods such as Historical Simulation, which calculates the empirical quantile on a moving window of returns, implies 90% VaR confidence intervals that are too narrow and that contain as few as 20% of the true VaRs. Other methods which properly account for conditional variance dynamics, such as Filtered Historical Simulation instead imply 90% VaR confidence intervals that contain close to 90% of the true VaRs. ES measures are generally less accurate than VaR measures and the confidence bands around ES are also less reliable. La valeur-a-risque (VaR) et la mesure ES (Expected Shortfall) sont de plus en plus utilisees pour la mesure du risque d'un portefeuille, l'allocation de capital de risque et la determination des performances. Les gestionnaires de risques financiers sont donc legitimement interesses par la precision des techniques classiques de la valeur-a-risque et de la mesure ES. Le but de cet article est precisement d'evaluer la precision des modeles classiques et de mesurer l'importance de l'erreur d'estimation en construisant des intervalles de confiance autour des previsions de la valeur-a-risque et de la mesure ES. Un des problemes cles dans la construction d'intervalles de confiance appropries provient de la dynamique de la variance conditionnelle typiquement observee pour les rendements speculatifs. Notre article propose donc une technique de re-echantillonnage qui tient compte de l'erreur d'estimation des parametres des modeles dynamiques de la variance d'un portefeuille. Une analyse Monte Carlo nous montre que les methodes generalement utilisees par les praticiens, telles que la simulation historique qui calcule le quantile empirique a l'aide d'une fenetre mobile des rendements, generent des intervalles de confiance pour la valeur-a-risque a 90% qui sont trop etroits et qui contiennent seulement 20% des vraies valeurs-a-risque. D'autres methodes qui tiennent compte correctement de la dynamique conditionnelle de la variance, telles que la simulation historique filtree, generent quant a elles des intervalles de confiance de la valeur-a-risque a 90% qui contiennent pres de 90% des vraies valeurs-a-risque. Les mesures ES sont generalement moins precises que les mesures de valeur-a-risque et les intervalles de confiance autour de la mesure ES sont egalement moins fiables.
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estimation Risk in Financial Risk Management
Social Science Research Network, 2004Co-Authors: Peter Christoffersen, Silvia GoncalvesAbstract:Value-at-Risk (VAR) is increasingly used in portfolio Risk measurement, Risk capital allocation and performance attribution. Financial Risk managers are therefore rightfully concerned with the precision of typical VAR techniques. The purpose of this paper is to assess the precision of common dynamic models and to quantify the magnitude of the estimation error by constructing confidence intervals around the point VAR and expected shortfall (ES) forecasts. A key challenge in constructing proper confidence intervals arises from the conditional variance dynamics that are typically found in speculative returns. Our paper suggests a resampling technique that takes into account parameter estimation error in dynamic models of portfolio variance.
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elements of Financial Risk Management
2003Co-Authors: Peter ChristoffersenAbstract:The Second Edition of this best-selling book expands its advanced approach to Financial Risk models by covering market, credit, and integrated Risk. With new data that cover the recent Financial crisis, it combines Excel-based empirical exercises at the end of each chapter with online exercises so readers can use their own data. Its unified GARCH modeling approach, empirically sophisticated and relevant yet easy to implement, sets this book apart from others. Five new chapters and updated end-of-chapter questions and exercises, as well as Excel-solutions manual, support its step-by-step approach to choosing tools and solving problems. Examines market Risk, credit Risk, and operational Risk Provides exceptional coverage of GARCH models Features online Excel-based empirical exercises
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elements of Financial Risk Management
Elsevier Monographs, 2003Co-Authors: Peter ChristoffersenAbstract:Elements of Financial Risk Management offers an introduction to modern Risk Management. It focuses on implementation, especially recent techniques which facilitate bridging the gap between standard textbooks on Risk and real-life Risk Management systems. It identifies key features of Risk asset returns and captures them in tractable statistical models in the companion website. It presents step-by-step approaches as a means to solve problems. This book is intended for three types of readers with an interest in Financial Risk Management. First, Master's and Ph.D. students specializing in finance and economics. Second, market practitioners with a quantitative undergraduate or graduate degree. Third, a small group of advanced undergraduates majoring in either economics, engineering, finance, or another quantitative field. The book will also suit those in Financial engineering courses who have strong quantitative backgrounds and those in Ph.D. courses. *Pinpoints key features of Risk asset returns and captures them in tractable statistical models in the companion website *Presents step-by-step approaches as a means to solve problems *Visible patterns in the data motivate the choices of tools, and when tools fall short, it presents the next tool
Francis X Diebold - One of the best experts on this subject based on the ideXlab platform.
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Financial Risk measurement for Financial Risk Management
National Bureau of Economic Research, 2012Co-Authors: Torben G Andersen, Peter Christoffersen, Tim Bollerslev, Francis X DieboldAbstract:Current practice largely follows restrictive approaches to market Risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in Financial econometrics and are likely to produce more accurate Risk assessments, treating both portfolio-level and asset-level analysis. Asset-level analysis is particularly challenging because the demands of real-world Risk Management in Financial institutions - in particular, real-time Risk tracking in very high-dimensional situations - impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market Risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market Risk, but also cross-fertilize the academic and practitioner communities, promoting improved market Risk measurement technologies that draw on the best of both.
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the known the unknown and the unknowable in Financial Risk Management measurement and theory advancing practice
2010Co-Authors: Francis X Diebold, Neil A Doherty, Richard J HerringAbstract:Preface vii Chapter 1: Introduction by Francis X. Diebold, Neil A. Doherty, and Richard J. Herring 1 Chapter 2: Risk: A Decision Maker's Perspective by Sir Clive W. J. Granger 31 Chapter 3: Mild vs. Wild Randomness: Focusing on Those Risks That Matter by Benoit B. Mandelbrot and Nassim Nicholas Taleb 47 Chapter 4: The Term Structure of Risk, the Role of Known and Unknown Risks, and Nonstationary Distributions by Riccardo Colacito and Robert F. Engle 59 Chapter 5: Crisis and Noncrisis Risk in Financial Markets: A Unified Approach to Risk Management by Robert H. Litzenberger and David M. Modest 74 Chapter 6: What We Know, Don't Know, and Can't Know about Bank Risk: A View from the Trenches by Andrew Kuritzkes and Til Schuermann 103 Chapter 7: Real Estate through the Ages: The Known, the Unknown, and the Unknowable by Ashok Bardhan and Robert H. Edelstein 145 Chapter 8: Reflections on Decision-making under Uncertainty by Paul R. Kleindorfer 164 Chapter 9: O n the Role of Insurance Brokers in Resolving the Known, the Unknown, and the Unknowable by Neil A. Doherty and Alexander Muermann 194 Chapter 10: Insuring against Catastrophes by Howard Kunreuther and Mark V. Pauly 210 Chapter 11: Managing Increased Capital Markets Intensity: The Chief Financial Officer's Role in Navigating the Known, the Unknown, and the Unknowable by Charles N. Bralver and Daniel Borge 239 Chapter 12: The Role of Corporate Governance in Coping with Risk and Unknowns by Kenneth E. Scott 277 Chapter 13: Domestic Banking Problems by Charles A. E. Goodhart 286 Chapter 14: Crisis Management: The Known, The Unknown, and the Unknowable by Donald L. Kohn 296 Chapter 15: Investing in the Unknown and Unknowable by Richard J. Zeckhauser 304 List of Contributors 347 Index 359
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the known the unknown and the unknowable in Financial Risk Management measurement and theory advancing practice
Economics Books, 2010Co-Authors: Francis X Diebold, Neil A Doherty, Richard J HerringAbstract:A clear understanding of what we know, don't know, and can't know should guide any reasonable approach to managing Financial Risk, yet the most widely used measure in finance today--Value at Risk, or VaR--reduces these Risks to a single number, creating a false sense of security among Risk managers, executives, and regulators. This book introduces a more realistic and holistic framework called KuU --the K nown, the u nknown, and the U nknowable--that enables one to conceptualize the different kinds of Financial Risks and design effective strategies for managing them. Bringing together contributions by leaders in finance and economics, this book pushes toward robustifying policies, portfolios, contracts, and organizations to a wide variety of KuU Risks. Along the way, the strengths and limitations of "quantitative" Risk Management are revealed. In addition to the editors, the contributors are Ashok Bardhan, Dan Borge, Charles N. Bralver, Riccardo Colacito, Robert H. Edelstein, Robert F. Engle, Charles A. E. Goodhart, Clive W. J. Granger, Paul R. Kleindorfer, Donald L. Kohn, Howard Kunreuther, Andrew Kuritzkes, Robert H. Litzenberger, Benoit B. Mandelbrot, David M. Modest, Alex Muermann, Mark V. Pauly, Til Schuermann, Kenneth E. Scott, Nassim Nicholas Taleb, and Richard J. Zeckhauser.
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how relevant is volatility forecasting for Financial Risk Management
The Review of Economics and Statistics, 2000Co-Authors: Peter Christoffersen, Francis X DieboldAbstract:It depends. If volatility fluctuates in a forecastable way, volatility forecasts are useful for Risk Management (hence the interest in volatility forecastability in the Risk Management literature). Volatility forecastability, however, varies with horizon, and different horizons are relevant in different applications. Moreover, existing assessments of volatility forecastability are plagued by the fact that they are joint assessments of volatility forecastability and an assumed model, and the results can vary not only with the horizon but also with the assumed model. To address this problem, we develop a model-free procedure for assessing volatility forecastability across horizons. Perhaps surprisingly, we find that volatility forecastability decays quickly with horizon. Volatility forecastability—although clearly of relevance for Risk Management at the short horizons relevant for, say, trading desk Management—may be much less important at longer horizons.
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pitfalls and opportunities in the use of extreme value theory in Risk Management
The Journal of Risk Finance, 2000Co-Authors: Francis X Diebold, Til Schuermann, John D StroughairAbstract:Recent literature has trumpeted the claim that extreme value theory (EVT) holds promise for accurate estimation of extreme quantiles and tail probabilities of Financial asset returns, and hence hold promise for advances in the Management of extreme Financial Risks. Our view, based on a disinterested assessment of EVT from the vantage point of Financial Risk Management, is that the recent optimism is partly appropriate but also partly exaggerated, and that at any rate much of the potential of EVT remains latent. We substantiate this claim by sketching a number of pitfalls associate with use of EVT techniques. More constructively, we show how certain of the pitfalls can be avoided, and we sketch a number of explicit research directions that will help the potential of EVT to be realized.
Til Schuermann - One of the best experts on this subject based on the ideXlab platform.
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pitfalls and opportunities in the use of extreme value theory in Risk Management
The Journal of Risk Finance, 2000Co-Authors: Francis X Diebold, Til Schuermann, John D StroughairAbstract:Recent literature has trumpeted the claim that extreme value theory (EVT) holds promise for accurate estimation of extreme quantiles and tail probabilities of Financial asset returns, and hence hold promise for advances in the Management of extreme Financial Risks. Our view, based on a disinterested assessment of EVT from the vantage point of Financial Risk Management, is that the recent optimism is partly appropriate but also partly exaggerated, and that at any rate much of the potential of EVT remains latent. We substantiate this claim by sketching a number of pitfalls associate with use of EVT techniques. More constructively, we show how certain of the pitfalls can be avoided, and we sketch a number of explicit research directions that will help the potential of EVT to be realized.
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horizon problems and extreme events in Financial Risk Management
Federal Reserve Bank of New York Economic policy review, 1998Co-Authors: Peter Christoffersen, Francis X Diebold, Til SchuermannAbstract:I. INTRODUCTION There is no one "magic" relevant horizon for Risk Management. Instead, the relevant horizon will generally vary by asset class (for example, equity versus bonds), industry (banking versus insurance), position in the firm (trading desk versus chief Financial officer), and motivation (private versus regulatory), among other things, and thought must be given to the relevant horizon on an application-by-application basis. But one thing is clear: in many Risk Management situations, the relevant horizons are long--certainly longer than just a few days--an insight incorporated, for example, in Bankers Trust's RAROC system, for which the horizon is one year. Simultaneously, it is well known that short-horizon asset return volatility fluctuates and is highly forecastable, a phenomenon that is very much at the center of modern Risk Management paradigms. Much less is known, however, about the forecastability of long-horizon volatility, and the speed and pattern with which forecastability decays as the horizon lengthens. A key question arises: Is volatility forecastability important for long-horizon Risk Management, or is a traditional constant-volatility assumption adequate? In this paper, we address this question, exploring the interface between long-horizon Financial Risk Management and long-horizon volatility forecastability and, in particular, whether long-horizon volatility is forecastable enough such that volatility models are useful for long-horizon Risk Management. In particular, we report on recent relevant work by Diebold, Hickman, Inoue, and Schuermann (1998); Christoffersen and Diebold (1997); and Diebold, Schuermann, and Stroughair (forthcoming). To assess long-horizon volatility forecastability, it is necessary to have a measure of long-horizon volatility, which can be obtained in a number of ways. We proceed in Section II by considering two ways of converting short-horizon volatility into long-horizon volatility: scaling and formal model-based aggregation. The defects of those procedures lead us to take a different approach in Section III, estimating volatility forecastability directly at the horizons of interest, without making assumptions about the nature of the volatility process, and arriving at a surprising conclusion: Volatility forecastability seems to decline quickly with horizon, and seems to have largely vanished beyond horizons of ten or fifteen trading days. If volatility forecastability is not important for Risk Management beyond horizons of ten or fifteen trading days, then what is important? The really big movements such as the U.S. crash of 1987 are still poorly understood, and ultimately the really big movements are the most important for Risk Management. This suggests the desirability of directly modeling the extreme tails of return densities, a task potentially facilitated by recent advances in extreme value theory. We explore that idea in Section IV, and we conclude in Section V. II. OBTAINING LONG-HORIZON VOLATILITIES FROM SHORT-HORIZON VOLATILITIES SCALING AND FORMAL AGGREGATION(1) Operationally, Risk is often assessed at a short horizon, such as one day, and then converted to other horizons, such as ten days or thirty days, by scaling by the square root of horizon [for instance, as in Smithson and Minton (1996a, 1996b) or J.P. Morgan (1996)]. For example, to obtain a ten-day volatility, we multiply the one-day volatility by [square root of 10]. Moreover, the horizon conversion is often significantly longer than ten days. Many banks, for example, link trading volatility measurement to internal capital allocation and Risk-adjusted performance measurement schemes, which rely on annual volatility estimates. The temptation is to scale one-day volatility by [square root of 252]. It turns out, however, that scaling is both inappropriate and misleading. SCALING WORKS IN lid ENVIRONMENTS Here we describe the restrictive environment in which scaling is appropriate. …
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horizon problems and extreme events in Financial Risk Management
Social Science Research Network, 1998Co-Authors: Peter Christoffersen, Francis X Diebold, Til SchuermannAbstract:Is volatility forecastability important for long-horizon Risk Management, or is a traditional constant-volatility assumption adequate? In this paper, the authors address this question, exploring the interface between long-horizon Financial Risk Management and long-horizon volatility forecastability and, in particular, whether long-horizon volatility is forecastable enough such that volatility models are useful for long-horizon Risk Management.
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horizon problems and extreme events in Financial Risk Management
Research Papers in Economics, 1998Co-Authors: Peter Christoffersen, Francis X Diebold, Til SchuermannAbstract:Central to the ongoing development of practical Financial Risk Management methods is recognition of the fact that asset return volatility is often forecastable. Although there is no single horizon relevant for Financial Risk Management, most would agree that in many situations the relevant horizon is quite long, certainly longer than a few days. This fact creates some tension, because although short-horizon asset return volatility is clearly highly forecastable, much less is known about long-horizon volatility forecastability, which we examine in this paper. We begin by assessing some common model-based methods for converting short-horizon volatility into long-horizon volatility; we argue that such conversions are problematic even when done properly. Hence we develop and apply a new model-free methodology to assess the forecastability of volatility across horizons and find, surprisingly, that forecastability decays rapidly as the horizon lengthens. We conclude that for managing Risk at horizons longer than a few weeks, attention given to direct estimation of extreme event probabilities may be more productive than attention given to modeling volatility dynamics, and we proceed to assess the potential of extreme value theory for estimating extreme event probabilities.
Silvia Goncalves - One of the best experts on this subject based on the ideXlab platform.
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estimation Risk in Financial Risk Management
2005Co-Authors: Peter Christoffersen, Silvia GoncalvesAbstract:Value-at-Risk (VaR) and Expected Shortfall (ES) are increasingly used in portfolio Risk measurement, Risk capital allocation and performance attribution. Financial Risk managers are therefore rightfully concerned with the precision of typical VaR and ES techniques. The purpose of this paper is exactly to assess the precision of common models and to quantify the magnitude of the estimation error by constructing confidence bands around the point VaR and ES forecasts. A key challenge in constructing proper confidence bands arises from the conditional variance dynamics typically found in speculative returns. Our paper suggests a resampling technique which accounts for parameter estimation error in dynamic models of portfolio variance. In a Monte Carlo study we find that commonly used practitioner methods such as Historical Simulation, which calculates the empirical quantile on a moving window of returns, implies 90% VaR confidence intervals that are too narrow and that contain as few as 20% of the true VaRs. Other methods which properly account for conditional variance dynamics, such as Filtered Historical Simulation instead imply 90% VaR confidence intervals that contain close to 90% of the true VaRs. ES measures are generally less accurate than VaR measures and the confidence bands around ES are also less reliable. La valeur-a-risque (VaR) et la mesure ES (Expected Shortfall) sont de plus en plus utilisees pour la mesure du risque d'un portefeuille, l'allocation de capital de risque et la determination des performances. Les gestionnaires de risques financiers sont donc legitimement interesses par la precision des techniques classiques de la valeur-a-risque et de la mesure ES. Le but de cet article est precisement d'evaluer la precision des modeles classiques et de mesurer l'importance de l'erreur d'estimation en construisant des intervalles de confiance autour des previsions de la valeur-a-risque et de la mesure ES. Un des problemes cles dans la construction d'intervalles de confiance appropries provient de la dynamique de la variance conditionnelle typiquement observee pour les rendements speculatifs. Notre article propose donc une technique de re-echantillonnage qui tient compte de l'erreur d'estimation des parametres des modeles dynamiques de la variance d'un portefeuille. Une analyse Monte Carlo nous montre que les methodes generalement utilisees par les praticiens, telles que la simulation historique qui calcule le quantile empirique a l'aide d'une fenetre mobile des rendements, generent des intervalles de confiance pour la valeur-a-risque a 90% qui sont trop etroits et qui contiennent seulement 20% des vraies valeurs-a-risque. D'autres methodes qui tiennent compte correctement de la dynamique conditionnelle de la variance, telles que la simulation historique filtree, generent quant a elles des intervalles de confiance de la valeur-a-risque a 90% qui contiennent pres de 90% des vraies valeurs-a-risque. Les mesures ES sont generalement moins precises que les mesures de valeur-a-risque et les intervalles de confiance autour de la mesure ES sont egalement moins fiables.
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estimation Risk in Financial Risk Management
Social Science Research Network, 2004Co-Authors: Peter Christoffersen, Silvia GoncalvesAbstract:Value-at-Risk (VAR) is increasingly used in portfolio Risk measurement, Risk capital allocation and performance attribution. Financial Risk managers are therefore rightfully concerned with the precision of typical VAR techniques. The purpose of this paper is to assess the precision of common dynamic models and to quantify the magnitude of the estimation error by constructing confidence intervals around the point VAR and expected shortfall (ES) forecasts. A key challenge in constructing proper confidence intervals arises from the conditional variance dynamics that are typically found in speculative returns. Our paper suggests a resampling technique that takes into account parameter estimation error in dynamic models of portfolio variance.
Anthony S Tay - One of the best experts on this subject based on the ideXlab platform.
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multivariate density forecast evaluation and calibration in Financial Risk Management high frequency returns on foreign exchange
The Review of Economics and Statistics, 1999Co-Authors: Francis X Diebold, Jinyong Hahn, Anthony S TayAbstract:We provide a framework for evaluating and improving multivariate density forecasts. Among other things, the multivariate framework lets us evaluate the adequacy of density forecasts involving cross-variable interactions, such as time-varying conditional correlations. We also provide conditions under which a technique of density forecast “calibration” can be used to improve deficient density forecasts, and we show how the calibration method can be used to generate good density forecasts from econometric models, even when the conditional density is unknown. Finally, motivated by recent advances in Financial Risk Management, we provide a detailed application to multivariate high-frequency exchange rate density forecasts.
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evaluating density forecasts with applications to Financial Risk Management
Social Science Research Network, 1998Co-Authors: Francis X Diebold, Todd A Gunther, Anthony S TayAbstract:Density forecasting is increasingly more important and commonplace, forexample in Financial Risk Management, yet little attention has been given to theevaluation of density forecasts. We develop a simple and operational frameworkfor density forecast evaluation. We illustrate the framework with adetailed application to density forecasting of asset returns in environments withtime-varying volatility. Finally, we discuss several extensions.
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evaluating density forecasts with applications to Financial Risk Management
International Economic Review, 1998Co-Authors: Francis X Diebold, Todd A Gunther, Anthony S TayAbstract:Density forecasting is increasingly more important and commonplace, for example in Financial Risk Management, yet little attention has been given to the evaluation of density forecasts. The authors develop a simple and operational framework for density forecast evaluation. They illustrate the framework with a detailed application to density forecasting of asset returns in environments with time-varying volatility. Finally, the authors discuss several extensions. Copyright 1998 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.