The VaR Modeling Handbook: Practical Applications in Alternative Investing, Banking, Insurance, and Portfolio Management (McGraw-Hill Finance & Investing) - Hardcover

GREGORIOU

Hardcover

ISBN 10: 0071625151 ISBN 13: 9780071625159

Verlag: McGraw Hill, 2009

Inhaltsangabe

Value-at-Risk (VaR) is a powerful tool for assessing market risk in real time- a critical insight when making trading and hedging decisions. The VaR Modeling Handbook is the most complete, up-to-date reference on the subject for today's savvy investors, traders, portfolio managers, and other asset and risk managers. Unlike market risk metrics such as the Greeks, or beta, which are applicable to only certain asset categories and sources of market risk, VaR is applicable to all liquid assets, making it a reliable indicator of total market risk. For this reason, among many others, VaR has become the dominant method for estimating precisely how much money is at risk each day in the financial markets. The VaR Modeling Handbook is a profound volume that delivers practical information on measuring and modeling risk specifically focused on alternative investments, banking, and the insurance sector. The perfect primer to The VaR Implementation Handbook (McGraw- Hill), this foundational resource features The experience of 40 internationally recognized experts Useful perspectives from a wide range of practitioners, researchers, and academics Coverage on applying VaR to hedge fund strategies, microcredit loan portfolios, and economic capital management approaches for insurance companies Each illuminating chapter in The VaR Modeling Handbook presents a specific topic, complete with an abstract and conclusion for quick reference, as well as numerous illustrations that exemplify covered material. Practitioners can gain in-depth, cornerstone knowledge of VaR by reading the handbook cover to cover or take advantage of its user-friendly format by using it as a go-to resource in the real world. Financial success in the markets requires confident decision making, and The VaR Modeling Handbook gives you the knowledge you need to use this state-of-the-art modeling method to successfully manage financial risk.

Über diesen Titel

Über die Autorin bzw. den Autor

Greg N. Gregoriou is professor of financein the School of Business and Economics atState University of New York (Plattsburgh).He has published 25 books and is coeditorfor the peer-reviewed Journal of Derivativesand Hedge Funds and editorial board memberfor the Journal of Wealth Management, Journal ofRisk Management in Financial Institutions, andBrazilian Business Review.

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The most complete guide to measuringand modeling risk in the real world

“The problems tackled in the papers collected here are both important and subtle, and they covera surprisingly broad range of issues.”
—Barry Schachter, Director of Quantitative Resources, Moore Capital Management

“The use of VaR as a risk metric was adopted globally under the 1996 Basel II amendment.Much interest and research in this broad field of risk management followed on its properties asa risk metric and portfolio optimizer. A ttention was focused on tail risk and CVaR as extensionsto the approach. The latest research on these issues is brilliantly captured in this volumeedited by Gregoriou.”
—Professor D.E. Allen, School of Accounting, Finance and Economics,Edith Cowan University

“I would highly recommend this book to everyone looking for a comprehensive and up-to-datesynthesis of research in risk management.”
—Dr. Bartosz Gebka, Professor of Finance, Newcastle University Business School

“This exquisitely edited volume shows a vast array of applications . . . ranging from alternativeinvestments to Solvency II , and also introduces advanced calculation models that go beyondthe standard value-at-risk approach and, hence, highlights how to deal with the caveatsof this measure.”
—Dr. Dieter Kaiser, Director of Hedge Funds, Feri Institutional Advisors GmbH

“This timely book contains new research in the vast area of value-at-risk, and will be invaluablefor sophisticated and institutional investors and money managers.”
—Fabrice Douglas Rouah, Vice President and Senior Quantitative Analyst,Enterprise Risk Management, State Street Corporation

Auszug. © Genehmigter Nachdruck. Alle Rechte vorbehalten.

THE VaR MODELING HANDBOOK

By GREG N. GREGORIOU

The McGraw-Hill Companies, Inc.

Copyright © 2009 The McGraw-Hill Companies, Inc.
All rights reserved.

ISBN: 978-0-07-162515-9

Contents

EDITOR
CONTRIBUTORS
PART ONE ALTERNATIVE INVESTMENTS AND OPTIMIZATION
Chapter 1 Asset Allocation for Hedge Fund Strategies: How to Better Manage 
Tail Risk Arjan Berkelaar, Adam Kobor, and Roy Kouwenberg
Chapter 2 Estimating Value at Risk of Institutional Portfolios with 
Alternative Asset Classes Roy Kouwenberg, Albert Mentink, Mark Schouten, 
and Robin Sonnenberg
Chapter 3 A Comparison between Optimal Allocations Based on the Modified 
VaR and Those Based on a Utility-Based Risk Measure Laurent Bodson, Alain 
Cöen, and Georges Hübner
Chapter 4 Using CVaR to Optimize and Hedge Portfolios Francesco Menoncin
PART TWO BANKING AND INSURANCE SECTOR APPLICATIONS
Chapter 5 Value at Risk, Capital Standards, and Risk Alignment in Banking 
Firms Guy Ford, Tyrone M. Carlin, and Nigel Finch
Chapter 6 The Asset–Liability Management Compound Option Model: A Public 
Debt Management Tool Jorge A. Chan-Lau and André O. Santos
Chapter 7 A Practitioner's Critique of Value-at-Risk Models Robert Dubil
Chapter 8 Value at Risk for a Microcredit Loan Portfolio: An African 
Microfinance Institution Case Study René Azokli, Emmanuel Fragnière, and 
Akimou Ossé
Chapter 9 Allocation of Economic Capital in Banking: A Simulation Approach 
 Hans-Peter Burghof and Jan Müller
Chapter 10 Using Tail Conditional Expectation for Capital Requirement 
Calculation of a General Insurance Undertaking João L. C. Duque, Alfredo 
D. Egídio dos Reis, and Ricardo Garcia
Chapter 11 Economic Capital Management for Insurance Companies Rossella 
Bisignani, Giovanni Masala, and Marco Micocci
Chapter 12 Solvency II: An Important Case in Applied VaR Alfredo D. 
Egídio dos Reis, Raquel M. Gaspar, and Ana T. Vicente
PART THREE PORTFOLIO MANAGEMENT
Chapter 13 Quantile-Based Tail Risk Estimation for Equity Portfolios John 
Cotter and Kevin Dowd
Chapter 14 Optimal Mixed-Asset Portfolios Juliane Proelss and Denis 
Schweizer
Chapter 15 Value-at-Risk-Adjusted Performance for Structured Portfolios 
Rosa Cocozza
INDEX

Excerpt

<h2>CHAPTER 1</h2><p><b>Asset Allocation for Hedge Fund Strategies: How to Better Manage Tail Risk</p><p>Arjan Berkelaar, Adam Kobor, and Roy Kouwenberg</p><br><p>ABSTRACT</b></p><p>Most approaches to risk budgeting are based on tracking error and value at risk(VaR). In addition, the return streams from any investment process are usuallyassumed to be serially uncorrelated and normally distributed. This assumption,however, does not necessarily hold in reality. In this chapter, we consider tworelatively new risk measures that are better suited to deal with nonnormal andserially correlated return streams and that are superior to tracking error (orvolatility) and value at risk. We show how these measures can be used indetermining an optimal risk allocation, allowing investors to better manage thetail and drawdown risks in their portfolios. By better managing these risks,investors can achieve superior risk-adjusted returns.</p><br><p><b>INTRODUCTION</b></p><p>Many institutional investors are searching for sources of diversification andreturn-enhancing strategies in order to improve the performance of theirportfolios. An area where many of them hope to achieve superior risk-adjustedreturns is hedge funds. Shifting asset allocations toward hedge funds is not aguarantee of success, however. Unlike equity and bond markets that compensateinvestors with a positive risk premium over the long term, returns fromselecting hedge fund managers are conditional on skill. To be successful inpicking hedge funds, a strong risk management process and a disciplinedinvestment approach are required.</p><p>Most approaches to risk and asset allocation, both in practice and in theacademic literature, are based on standard deviation and value at risk. Inaddition, the return streams are usually assumed to be normally distributed.This assumption is quite convenient, allowing investors to use the well-knownmean-variance workhorse to derive optimal allocations. We refer interestedreaders to Berkelaar et al. (2006) for a risk budgeting framework wheninvestment returns are normally distributed. In the case of hedge funds,however, the normal distribution fails to adequately describe the returndistribution. Basing risk allocations on mean-variance optimization may resultin a considerable misallocation of risk that could result in suboptimalportfolios and lower investment returns.</p><p>In this chapter, we consider conditional value at risk (CVaR)—a relativelynew risk measure that is better suited to deal with nonnormal return streams.The advantage of this risk measure is that it is easy to use and allows fornumerical tractability. In this chapter, we derive optimal portfolios for mean-CVaRinvestors and compare results with those of a mean-variance investor.Others have also studied the impact of skewness and fat tails on optimalportfolios. Krokhmal et al. (2003) consider a portfolio of individual hedgefunds and study the performance of various risk constraints, including CVaR andconditional drawdown at risk (CDaR), with in-sample and out-of-sample tests.Amin and Kat (2003) study the optimal allocation among stocks, bonds, and hedgefunds in a mean-variance-skewness optimization framework. Kouwenberg (2003)studies the added value of investment in individual hedge funds for investorswith passive stock and bond portfolios, taking into account the nonnormality ofthe return distribution.</p><p>We use the Hedge Fund Research, Inc. (HFRI) indexes for several hedge fundstrategies to determine optimal allocations. The period for the historical timeseries is January 1990 to December 2007. We show that investors who manage tailrisk by basing their portfolios on mean-CVaR optimization should be able toproduce superior returns on their hedge fund portfolio. Using historical returnsfor several hedge fund strategies, the incremental return could be as much as100 to 200 basis points (bps). We also present results based on a forward-lookingsimulation model for hedge fund returns with more modest assumptionsabout expected returns. In this case, the incremental return is about 40 to 60bps.</p><p>This chapter is organized as follows. The second section of this chapterdiscusses the CVaR measure. We also show to what extent risk could beunderestimated by assuming that returns are serially uncorrelated and normallydistributed. This chapter's third section discusses a framework that can beapplied to develop forward-looking return scenarios for a wide range of...

Details zur Ausgabe

• Verlag: McGraw Hill
• Erscheinungsdatum: 2009
• Sprache: Englisch
• ISBN 10: 0071625151
• ISBN 13: 9780071625159
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 416
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: gpsr@libri.de; gpsr@libri.de; ; Europaallee 1; Bad Hersfeld; 36244; Deutschland