DOCSLIB.ORG

Fat-Tailed Models for Risk Estimation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Fat-Tailed Models for Risk Estimation Fat-tailed models for risk estimation by Stoyan V. Stoyanov, Svetlozar T. Rachev, Boryana Racheva-Iotova, Frank J. Fabozzi No. 30 | MAY 2011 WORKING PAPER SERIES IN ECONOMICS KIT – University of the State of Baden-Wuerttemberg and National Laboratory of the Helmholtz Association econpapers.wiwi.kit.edu Impressum Karlsruher Institut für Technologie (KIT) Fakultät für Wirtschaftswissenschaften Institut für Wirtschaftspolitik und Wirtschaftsforschung (IWW) Institut für Wirtschaftstheorie und Statistik (ETS) Schlossbezirk 12 76131 Karlsruhe KIT – Universität des Landes Baden-Württemberg und nationales Forschungszentrum in der Helmholtz-Gemeinschaft Working Paper Series in Economics No. 30, May 2011 ISSN 2190-9806 econpapers.wiwi.kit.edu Fat-tailed models for risk estimation Stoyan V. Stoyanov EDHEC Business School EDHEC Risk Institute{Asia Svetlozar T. Rachev∗ Stony Brook University, USA Karlsruhe Institute of Technology, Germany, and FinAnalytica USA Boryana Racheva-Iotova FinAnalytica USA Frank J. Fabozzi Yale School of Management Abstract In the post-crisis era, financial institutions seem to be more aware of the risks posed by extreme events. Even though there are attempts to adapt methodologies drawing from the vast academic literature on the topic, there is also skepticism that fat-tailed models are needed. In this paper, we address the common criticism and discuss three popular methods for extreme risk modeling based on full distribution modeling and and extreme value theory. ∗Dr Rachev is Frey Family Foundation Chair-Professor at Department of Applied Math- ematics & Statistics, Stony Brook University. He gratefully acknowledges research support by grants from Division of Mathematical, Life and Physical Sciences, College of Letters and Science, University of California, Santa Barbara, the Deutschen Forschungsgemeinschaft and the Deutscher Akademischer Austausch Dienst. 1 This is the submitted version of the following article: Fat-tailed Mod- els for Risk Estimation, Stoyanov, S. V., Rachev, S., Racheva-Iotova, B., Fabozzi, F. J., Journal of Portfolio Management, 37/2, Copyright c 2011, Institutional Investor, Inc., which has been published in final form at: http: //www.iijournals.com/doi/abs/10.3905/jpm.2011.37.2.107 1 Introduction The extreme losses which occurred in the financial crisis of 2008 raised the question of whether existing models and practices, largely based on the Gaus- sian distribution represent an adequate and reliable framework for risk mea- surement and management. Following the financial crisis, different ideas for methodology improvements have been proffered in white papers such as Sheikh and Qiao (2009) and Dubikovsky et al. (2010), drawing from the existing literature on modeling of extreme events. Empirical studies on the properties of asset return distributions date back to the pioneering work of Mandelbrot (1963) and Fama (1963, 1965) who re- jected the assumption of normality and suggested the class of stable Paretian distributions as an extension to the Gaussian hypothesis. Their work led to the consolidation of the stable Paretian hypothesis which underwent em- pirical scrutiny in the 1970s and 1980s. It was reported that even though stock returns exhibit tails thicker than those of the Gaussian distribution, there are a few inconsistencies between observed financial return distribu- tions and those predicted by the stable Paretian distribution. The empirical inconsistencies are related mainly to a failure of the temporal aggregation property, that is, tail thickness does not change with return frequency, and the infinite-variance property of stable laws. Even though some of the statistical techniques used to derive these con- clusions are disputed and the class of stable laws has been suggested as a viable theoretical framework,1 alternative classes of distributions were sug- gested. Examples include the Student's t distribution considered by Blat- tberg and Gonedes (1974), the more general class of hyperbolic distributions considered by Eberlein and Keller (1995), and finite mixture of normals con- sidered by Kon (1984). Generalizing the notion of stability to other proba- bilistic schemes, Mittnik and Rachev (1993) considered geo-stable, Weibull, and other distributions. More recently, tempered stable distributions have 1See Rachev and Mittnik (2000) and the references therein. 2 been suggested as a model for asset returns because they retain some of the attractive theoretical properties of stable Paretian distributions but have a finite variance and can explain the temporal aggregation property.2 Clearly, there is no fundamental theory that can suggest a distributional model for financial returns and the problem remains largely a statistical one. Nevertheless, the extensive body of empirical research carried out since the 1950s leads to the following stylized facts: (1) clustering of volatility { large price changes tend to be followed by large price changes and small price changes tend to be followed by small price changes. (2) autoregressive behavior { price changes depend on price changes in the past, e.g. positive price changes tend to be followed by positive price changes. (3) skewness { there is an asymmetry in the upside and downside potential of price changes. (4) fat tails { the probability of extreme profits or losses is much larger than predicted by the normal distribution. Tail thickness varies from asset to asset. (5) temporal behavior of tail thickness { the probability of extreme profits or losses can change through time; it is smaller in regular markets and much larger in turbulent markets. (6) tail thickness varies across frequencies { high-frequency data tends to be more fat-tailed than lower-frequency data. A methodology for risk measurement can be based on any statistically ac- ceptable time-series model capable of capturing these stylized facts and an appropriate risk measure. Apart from the full distribution modeling, there exists another method for modeling extreme events. It is based on the extreme value theory (EVT) developed to model extreme events in nature, such as extreme winds, tem- peratures, and river levels, and provides a model only for the tails for the 2See Kim et al. (2008) and Samorodnitsky and Grabchak (2010). 3 distribution. Embrechts et al. (1997) provide applications in the field of insurance and finance. In the post-crisis era, while on the surface there seems to be universal agreement that financial assets are indeed fat-tailed and that investment managers must take extreme events into account as part of their everyday risk management processes, the attitude in the academic circles is not as uni- form. There are constructive efforts to identify factors explaining tail events which can, potentially, be hedged (see Bhansali (2008)), or verify whether the observed dynamics in correlations during market crashes are not artifacts of the implicit assumption of normality (see Campbell et al. (2008)). Yet, in stark contrast, there are papers such as Esch (2010) suggesting fat-tailed models should be ignored in favor of Gaussian-based models on the grounds of parsimony. While from a statistical viewpoint simpler models explaining all stylized facts of the data are preferred to more complex models, represen- tativity should not be sacrificed for simplicity. In other words, models should be as simple as possible but not any simpler. The paper is organized in the following way. Section 2 discusses whether non-Gaussian models in general are really needed. Sections 3 and 4 focus on the two common techniques for modeling extreme losses. Finally, Section 5 discusses general techniques for model selection. 2 Are non-Gaussian models needed? Since fat-tailed models challenge well-established classical theories, there has been a natural resistance towards them both in academia and in the practice of finance. The resistance is usually not rooted in doubts whether fat-tails exist in the real world; first, empirical research has firmly established this fact and, second, the fresh memories of the recent financial crisis are food for thought for those in denial. Rather, it is based on pragmatic reasoning which can be summarized in the following two statements: (i) the classical models are preferred because any attempt to employ a non-Gaussian model necessarily results in huge estimation errors which introduce additional un- certainty and outweigh the benefits and (ii) the observed fat tails can be explained through Gaussian-based classical models. In this section, we con- sider in detail each of these statements. 4 2.1 Estimation errors and non-Gaussian models The discussion of estimation errors and non-Gaussian models has two aspects and we draw a clear distinction between them. The first aspect concerns ap- plication of higher-order moments in order to account for a potential skewness and excess kurtosis which are two geometric characteristics of the return dis- tribution representing deviations from normality. The usual approach is to take advantage of the classical statistical measures of skewness and kurtosis and to use them directly in an extension of the classical mean-variance anal- ysis for portfolio construction (see Martellini and Ziemann (2010)) or to use them for the purpose of value-at-risk (VaR) estimation through a moment- based approximation known as the Cornish-Fisher expansion. It is important to note that this approach is not related to a fat-tailed distribution and is non-parametric in nature. Loosely speaking, the goal is to describe devia- tions from normality in
Load more

Recommended publications
  • Capturing Tail Risk in a Risk Budgeting Model
    DEGREE PROJECT IN MATHEMATICS, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Capturing Tail Risk in a Risk Budgeting Model FILIP LUNDIN MARKUS WAHLGREN KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES Capturing Tail Risk in a Risk Budgeting Model FILIP LUNDIN MARKUS WAHLGREN Degree Projects in Financial Mathematics (30 ECTS credits) Master's Programme in Industrial Engineering and Management KTH Royal Institute of Technology year 2020 Supervisor at Nordnet AB: Gustaf Haag Supervisor at KTH: Anja Janssen Examiner at KTH: Anja Janssen TRITA-SCI-GRU 2020:050 MAT-E 2020:016 Royal Institute of Technology School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci Abstract Risk budgeting, in contrast to conventional portfolio management strategies, is all about distributing the risk between holdings in a portfolio. The risk in risk budgeting is traditionally measured in terms of volatility and a Gaussian distribution is commonly utilized for modeling return data. In this thesis, these conventions are challenged by introducing different risk measures, focusing on tail risk, and other probability distributions for modeling returns. Two models for forming risk budgeting portfolios that acknowledge tail risk were chosen. Both these models were based on CVaR as a risk measure, in line with what previous researchers have used. The first model modeled re- turns with their empirical distribution and the second with a Gaussian mixture model. The performance of these models was thereafter evaluated. Here, a diverse set of asset classes, several risk budgets, and risk targets were used to form portfolios. Based on the performance, measured in risk-adjusted returns, it was clear that the models that took tail risk into account in general had su- perior performance in relation to the standard model.
    [Show full text]
  • Tail Risk Premia and Return Predictability∗
    Tail Risk Premia and Return Predictability∗ Tim Bollerslev,y Viktor Todorov,z and Lai Xu x First Version: May 9, 2014 This Version: February 18, 2015 Abstract The variance risk premium, defined as the difference between the actual and risk- neutral expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may be attributed to time variation in the part of the variance risk premium associated with the special compensation demanded by investors for bearing jump tail risk, consistent with idea that market fears play an important role in understanding the return predictability. Keywords: Variance risk premium; time-varying jump tails; market sentiment and fears; return predictability. JEL classification: C13, C14, G10, G12. ∗The research was supported by a grant from the NSF to the NBER, and CREATES funded by the Danish National Research Foundation (Bollerslev). We are grateful to an anonymous referee for her/his very useful comments. We would also like to thank Caio Almeida, Reinhard Ellwanger and seminar participants at NYU Stern, the 2013 SETA Meetings in Seoul, South Korea, the 2013 Workshop on Financial Econometrics in Natal, Brazil, and the 2014 SCOR/IDEI conference on Extreme Events and Uncertainty in Insurance and Finance in Paris, France for their helpful comments and suggestions. yDepartment of Economics, Duke University, Durham, NC 27708, and NBER and CREATES; e-mail: [email protected]. zDepartment of Finance, Kellogg School of Management, Northwestern University, Evanston, IL 60208; e-mail: [email protected]. xDepartment of Finance, Whitman School of Management, Syracuse University, Syracuse, NY 13244- 2450; e-mail: [email protected].
    [Show full text]
  • Comparative Analyses of Expected Shortfall and Value-At-Risk Under Market Stress1
    Comparative analyses of expected shortfall and value-at-risk under market stress1 Yasuhiro Yamai and Toshinao Yoshiba, Bank of Japan Abstract In this paper, we compare value-at-risk (VaR) and expected shortfall under market stress. Assuming that the multivariate extreme value distribution represents asset returns under market stress, we simulate asset returns with this distribution. With these simulated asset returns, we examine whether market stress affects the properties of VaR and expected shortfall. Our findings are as follows. First, VaR and expected shortfall may underestimate the risk of securities with fat-tailed properties and a high potential for large losses. Second, VaR and expected shortfall may both disregard the tail dependence of asset returns. Third, expected shortfall has less of a problem in disregarding the fat tails and the tail dependence than VaR does. 1. Introduction It is a well known fact that value-at-risk2 (VaR) models do not work under market stress. VaR models are usually based on normal asset returns and do not work under extreme price fluctuations. The case in point is the financial market crisis of autumn 1998. Concerning this crisis, CGFS (1999) notes that “a large majority of interviewees admitted that last autumn’s events were in the “tails” of distributions and that VaR models were useless for measuring and monitoring market risk”. Our question is this: Is this a problem of the estimation methods, or of VaR as a risk measure? The estimation methods used for standard VaR models have problems for measuring extreme price movements. They assume that the asset returns follow a normal distribution.
    [Show full text]
  • Modeling Financial Sector Joint Tail Risk in the Euro Area
    Modeling financial sector joint tail risk in the euro area∗ Andr´eLucas,(a) Bernd Schwaab,(b) Xin Zhang(c) (a) VU University Amsterdam and Tinbergen Institute (b) European Central Bank, Financial Research Division (c) Sveriges Riksbank, Research Division First version: May 2013 This version: October 2014 ∗Author information: Andr´eLucas, VU University Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands, Email: [email protected]. Bernd Schwaab, European Central Bank, Kaiserstrasse 29, 60311 Frankfurt, Germany, Email: [email protected]. Xin Zhang, Research Division, Sveriges Riksbank, SE 103 37 Stockholm, Sweden, Email: [email protected]. We thank conference participants at the Banque de France & SoFiE conference on \Systemic risk and financial regulation", the Cleveland Fed & Office for Financial Research conference on \Financial stability analysis", the European Central Bank, the FEBS 2013 conference on \Financial regulation and systemic risk", LMU Munich, the 2014 SoFiE conference in Cambridge, the 2014 workshop on \The mathematics and economics of systemic risk" at UBC Vancouver, and the Tinbergen Institute Amsterdam. Andr´eLucas thanks the Dutch Science Foundation (NWO, grant VICI453-09-005) and the European Union Seventh Framework Programme (FP7-SSH/2007- 2013, grant agreement 320270 - SYRTO) for financial support. The views expressed in this paper are those of the authors and they do not necessarily reflect the views or policies of the European Central Bank or the Sveriges Riksbank. Modeling financial sector joint tail risk in the euro area Abstract We develop a novel high-dimensional non-Gaussian modeling framework to infer con- ditional and joint risk measures for many financial sector firms.
    [Show full text]
  • Hedging and Evaluating Tail Risks Via Two Novel Options Based on Type II Extreme Value Distribution
    S S symmetry Article Hedging and Evaluating Tail Risks via Two Novel Options Based on Type II Extreme Value Distribution Hang Lin 1,2,* , Lixin Liu 1 and Zhengjun Zhang 2 1 School of Statistics, University of International Business and Economics, Beijing 100029, China; [email protected] 2 Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA; [email protected] * Correspondence: [email protected] Abstract: Tail risk is an important financial issue today, but directly hedging tail risks with an ad hoc option is still an unresolved problem since it is not easy to specify a suitable and asymmetric pricing kernel. By defining two ad hoc underlying “assets”, this paper designs two novel tail risk options (TROs) for hedging and evaluating short-term tail risks. Under the Fréchet distribution assumption for maximum losses, the closed-form TRO pricing formulas are obtained. Simulation examples demonstrate the accuracy of the pricing formulas. Furthermore, they show that, no matter whether at scale level (symmetric “normal” risk, with greater volatility) or shape level (asymmetric tail risk, with a smaller value in tail index), the greater the risk, the more expensive the TRO calls, and the cheaper the TRO puts. Using calibration, one can obtain the TRO-implied volatility and the TRO-implied tail index. The former is analogous to the Black-Scholes implied volatility, which can measure the overall symmetric market volatility. The latter measures the asymmetry in underlying losses, mirrors market sentiment, and provides financial crisis warnings. Regarding the newly proposed TRO Citation: Lin, H.; Liu, L.; Zhang, Z.
    [Show full text]
  • Tail Risk of Equity Market Indices: an Extreme Value Theory Approach
    An EDHEC-Risk Institute Publication Tail Risk of Equity Market Indices: An Extreme Value Theory Approach February 2014 Institute 2 Printed in France, February 2014. Copyright EDHEC 2014. The opinions expressed in this survey are those of the authors and do not necessarily reflect those of EDHEC Business School. Tail Risk of Equity Market Indices: An Extreme Value Theory Approach — February 2014 Table of Contents Executive Summary .................................................................................................5 1. Introduction ............................................................................................................9 2. Extreme Value Theory .......................................................................................13 3. A Conditional EVT Model .................................................................................19 4. Risk Estimation with EVT .................................................................................23 5. Back-testing and Statistical Tests .................................................................27 6. Data and Empirical Results .............................................................................31 7. Conclusions .........................................................................................................43 Appendices ..............................................................................................................47 References ...............................................................................................................55
    [Show full text]
  • Portfolio Tail Risk: a Multivariate Extreme Value Theory Approach
    entropy Article Portfolio Tail Risk: A Multivariate Extreme Value Theory Approach Miloš Božovi´c Faculty of Economics, University of Belgrade, Kameniˇcka6, 11000 Belgrade, Serbia; [email protected] Received: 31 October 2020; Accepted: 15 December 2020; Published: 17 December 2020 Abstract: This paper develops a method for assessing portfolio tail risk based on extreme value theory. The technique applies separate estimations of univariate series and allows for closed-form expressions for Value at Risk and Expected Shortfall. Its forecasting ability is tested on a portfolio of U.S. stocks. The in-sample goodness-of-fit tests indicate that the proposed approach is better suited for portfolio risk modeling under extreme market movements than comparable multivariate parametric methods. Backtesting across multiple quantiles demonstrates that the model cannot be rejected at any reasonable level of significance, even when periods of stress are included. Numerical simulations corroborate the empirical results. Keywords: tail risk; extreme value theory; principal component analysis; value at risk; expected shortfall JEL Classification: G17; G32; C52; C53 1. Introduction Events of the Global Financial Crisis, the Eurozone sovereign debt crisis, and the ongoing COVID-19 pandemics increase the investors’ awareness of the need to assess the risk of extreme losses and the necessity of protection against future market meltdowns. To quantify financial risks, investors and regulators commonly resort to risk measures based on quantiles of distributions of losses or returns, such as Value at Risk (VaR) or Expected Shortfall (ES). However, the most significant problem with conventional approaches to assessing the quantile-based risk measures is their poor performance in characterizing the “tail” behavior of the distribution of returns.
    [Show full text]
  • Introduction to Tail Risk Parity
    An Introduction to Tail Risk Parity Balancing Risk to Achieve Downside Protection Ashwin Alankar, Senior Portfolio Manager, AllianceBernstein Michael DePalma, CIO Quantitative Strategies, AllianceBernstein Myron Scholes, Nobel Laureate, Frank E. Buck Professor of Finance, Emeritus, Stanford University Much of the real world is controlled as much by the “tails” of distributions as by means or averages: by the exceptional, not the mean; by the catastrophe, not the steady drip; by the very rich, not the “middle class.” We need to free ourselves from “average” thinking. Philip W. Anderson, Nobel Prize Recipient, Physics For institutional investor or home office use only. Not for inspection by, distribution or quotation to, the general public. Executive Summary . Tail Risk Parity (TRP) adapts the risk-balancing techniques of Risk Parity in an attempt to protect the portfolio at times of economic crisis and reduce the cost of the protection in the absence of a crisis. In measuring expected tail loss we use a proprietary “implied expected tail loss (ETL)” measure distilled from options-market information. Whereas Risk Parity focuses on volatility, Tail Risk Parity defines risk as expected tail loss—something that hurts investors more than volatility. Risk Parity is a subset of Tail Risk Parity when asset returns are normally distributed and/or volatility adequately captures tail-loss risk. Hence, when the risk of tail events is negligible, Tail Risk Parity allocations will resemble Risk Parity allocations. Tail Risk Parity seeks to reduce tail losses significantly while retaining more upside than Risk Parity or other mean-variance optimization techniques. It is very difficult to construct portfolios under symmetric risk measures (such as volatility as used in Risk Parity) that don’t penalize both large losses and, unfortunately, large gains.
    [Show full text]
  • An Introduction to Tail Risk Parity
    An Introduction to Tail Risk Parity Balancing Risk to Achieve Downside Protection Ashwin Alankar—Senior Portfolio Manager, AllianceBernstein Michael DePalma—CIO of Quantitative Strategies, AllianceBernstein Myron Scholes, Nobel Laureate, Frank E. Buck Professor of Finance, Emeritus, Stanford University Much of the real world is controlled as much by the “tails” of distributions as by means or averages: by the exceptional, not the mean; by the catastrophe, not the steady drip; by the very rich, not the “middle class.” We need to free ourselves from “average” thinking. Philip W. Anderson, Nobel Prize Recipient, Physics For institutional investor or home office use only. Not for inspection by, distribution or quotation to, the general public. Executive Summary Tail Risk Parity adapts the risk-balancing techniques of Risk Parity in an attempt to protect the portfolio at times of economic crisis when correlations spike and reduce the cost of the protection in the absence of a crisis. In measuring expected tail loss, we use a proprietary “implied expected tail loss (ETL)” measure distilled from options-market information. Whereas Risk Parity focuses on volatility, Tail Risk Parity defines risk as expected tail loss—the risk of suffering large losses. Risk Parity is a special case of Tail Risk Parity when asset returns are normally distributed and/or volatility adequately captures tail loss risk. By focusing on the drawdowns themselves, the Tail Risk Parity approach subsumes the costly drawdowns or jumps to which the Risk Parity approach is susceptible. Tail Risk Parity seeks to reduce tail losses significantly while retaining more upside than Risk Parity or other mean-variance optimization techniques.
    [Show full text]
  • Investment Behavior Analysis Based on Tail Risk Management
    INVESTMENT BEHAVIOR ANALYSIS BASED ON TAIL RISK MANAGEMENT A Paper Submitted to the Graduate Faculty of the North Dakota State University of Agriculture and Applied Science By Yu Sun In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE Major Department: Statistics May 2018 Fargo, North Dakota NORTH DAKOTA STATE UNIVERSITY Graduate School Title INVESTMENT BEHAVIOR ANALYSIS BASED ON TAIL RISK MANAGEMENT By Yu Sun The supervisory committee certifies that this paper complies with North Dakota State University’s regula- tions and meets the accepted standards for the degree of MASTER OF SCIENCE SUPERVISORY COMMITTEE: Dr. Rhonda Magel Chair Dr. Ruilin Tian Co-Chair Dr. Simone Ludwig Approved: 11 May 2018 Dr. Rhonda Magel Date Department Chair ABSTRACT As behavioral finance is becoming more prevalent in academic area, a study is worth conducting to pinpoint investors’ preference through managing tail risk of asset portfolios. This study investigates investors’ investment behaviors by modeling their investment personalities based on tail risk management. We incorporate CVaR approach to model traditional and non-traditional investment behaviors by reshaping the tails of portfolio return. To be specific, we build model to maximize left-tail CVaR, minimize right-tail CVaR, minimize left-tail CVaR models, and a mixed model that maximize left-tail CVaR and minimize right- tail CVaR simultaneously based on various group of rational and irrational investors. Our work incorporates empirical historical data and Monte Carlo simulation to compare these models with the classical Markowitz approach via different dimensions. We make contributions to fill the gap by making a more comprehensively study that incorporates investors’ psychological factors and exploring economic information regarding asset pricing puzzle and long-run risk.
    [Show full text]
  • Fundamental Review of the Trading Book: a Revised Market Risk Framework
    Basel Committee on Banking Supervision Consultative Document Fundamental review of the trading book: A revised market risk framework Issued for comment by 31 January 2014 October 2013 This publication is available on the BIS website (www.bis.org). © Bank for International Settlements 2013. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated. ISBN 92-9131-971-6 (print) ISBN 92-9197-971-6 (online) Contents Executive summary ........................................................................................................................................................................... 1 Section 1: Overall revisions to the market risk framework ............................................................................................... 7 1.1 The trading book/banking book boundary .......................................................................................................... 7 1.2 Treatment of credit ...................................................................................................................................................... 10 1.3 Factoring in market liquidity .................................................................................................................................... 13 1.4 Choice of market risk metric and calibration to stress conditions ............................................................ 18 1.5 Treatment of hedging and diversification..........................................................................................................
    [Show full text]
  • Tail Risk Adjusted Sharpe Ratio
    Working Paper I Cross asset Investment Strategy WP-71-2018 Tail Risk Adjusted Sharpe Ratio RESEARCH STRATEGY & ANALYSIS Document for the exclusive attention of professional clients, investment services providers and any other professional of the financial industry Tail Risk Adjusted Sharpe Ratio Abstract Didier Maillard The Sharpe Ratio has become a standard measure of Professor Emeritus at portfolio management performance, taking into account CNAM, Senior Advisor the risk side. In that framework, the consideration of risk on Research – Amundi is reduced to returns volatility. The Sharpe Ratio does not encompass extreme risk, especially on the downside. didier.maillard-ext@amundi. com In this paper, we provide four methods for constructing Tail Risk Adjusted Sharpe Ratios (TRASR), using asymmetric measures of risk: semi variance, Value-at-Risk, Conditional Value-at-Risk (or Expected Shortfall) and expected utility derived measures of risks. We also have an exploration of the Aumann & Serrano’s Economic Index of Riskiness. The adjustment translates into an adjusted volatility, which allows us to keep the Sharpe Ratio format and to compare the impacts of the adjustments. For that, we use the Cornish-Fisher transformation to model tail risk, duly controlling for skewness and kurtosis. Keywords: Risk, variance, volatility, skewness, kurtosis, portfolio performance JEL Classification: C02, C51, G11, G32 Acknowledgement: The author thanks Fabrice Barthélémy and Charles-Olivier Amédée-Manesme for valuable insights and discussions. He retains sole responsibility for the content. 3 About the author Didier Maillard Didier Maillard is Senior Advisor to Amundi on Research. He his Professor Emeritus at Conservatoire national des arts et metiers (CNAM), where he held a Chair of Banking.
    [Show full text]