Portfolio Risk Analysis - Hardcover

Connor, Gregory; Goldberg, Lisa R.; Korajczyk, Robert A.

Hardcover

ISBN 10: 0691128286 ISBN 13: 9780691128283

Verlag: Princeton University Press, 2010

Inhaltsangabe

Portfolio risk forecasting has been and continues to be an active research field for both academics and practitioners. Almost all institutional investment management firms use quantitative models for their portfolio forecasting, and researchers have explored models' econometric foundations, relative performance, and implications for capital market behavior and asset pricing equilibrium. Portfolio Risk Analysis provides an insightful and thorough overview of financial risk modeling, with an emphasis on practical applications, empirical reality, and historical perspective.


Beginning with mean-variance analysis and the capital asset pricing model, the authors give a comprehensive and detailed account of factor models, which are the key to successful risk analysis in every economic climate. Topics range from the relative merits of fundamental, statistical, and macroeconomic models, to GARCH and other time series models, to the properties of the VIX volatility index. The book covers both mainstream and alternative asset classes, and includes in-depth treatments of model integration and evaluation. Credit and liquidity risk and the uncertainty of extreme events are examined in an intuitive and rigorous way. An extensive literature review accompanies each topic. The authors complement basic modeling techniques with references to applications, empirical studies, and advanced mathematical texts.


This book is essential for financial practitioners, researchers, scholars, and students who want to understand the nature of financial markets or work toward improving them.

Über diesen Titel

Über die Autorin bzw. den Autor

Gregory Connor is professor of finance at the National University of Ireland, Maynooth, and senior research associate at the London School of Economics and Political Science. Lisa R. Goldberg is executive director of analytic initiatives at MSCI Barra and adjunct professor of statistics at the University of California, Berkeley. Robert A. Korajczyk is professor of finance at Northwestern University.

Von der hinteren Coverseite

"Thorough and well-cited, this is a comprehensive treatment of techniques for portfolio risk management. It provides a unique perspective, from the fundamentals to practical applications. There are few books that cover this material in this particular way."--Christopher L. Culp, author of Structured Finance and Insurance

"The range of topics is wide and the coverage is deep. An impressive book."--Peter Christoffersen, McGill University

"The conceptual framework of this book is presented in a lucid and clear manner. The treatment is mathematically rigorous where it matters, without ever becoming pedantic and without cutting corners."--Riccardo Rebonato, Royal Bank of Scotland

"This book takes major steps forward in the crucially important area of portfolio risk measurement, making significant strides toward incorporating industry and country risk, as well as macroeconomic, FX, credit, transactions cost, and liquidity risks. It will be an essential reference text for academics, central bankers, and others in the financial services industry."--Francis X. Diebold, University of Pennsylvania

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Portfolio Risk Analysis

By Gregory Connor Lisa R. Goldberg Robert A. Korajczyk

Princeton University Press

Copyright © 2010 Princeton University Press
All right reserved.
ISBN: 978-0-691-12828-3

Contents

Acknowledgments...........................................................xiIntroduction..............................................................xiiiKey Notation..............................................................xix1 Measures of Risk and Return.............................................12 Unstructured Covariance Matrices........................................363 Industry and Country Risk...............................................614 Statistical Factor Analysis.............................................795 The Macroeconomy and Portfolio Risk.....................................1016 Security Characteristics and Pervasive Risk Factors.....................1177 Measuring and Hedging Foreign Exchange Risk.............................1348 Integrated Risk Models..................................................1559 Dynamic Volatilities and Correlations...................................16710 Portfolio Return Distributions.........................................19111 Credit Risk............................................................21212 Transaction Costs and Liquidity Risk...................................24113 Alternative Asset Classes..............................................27114 Performance Measurement................................................29915 Conclusion.............................................................319References................................................................323Index.....................................................................345

Chapter One

Measures of Risk and Return

Section 1.1 gives definitions of portfolio return. Section 1.2 introduces some key portfolio risk measures used throughout the book. Section 1.3 discusses risk–return preferences and portfolio optimization. Section 1.4 discusses the capital asset pricing model (CAPM). In that section we relate the CAPM to the more general state-space pricing model, and critically assess its applications to portfolio risk management. Section 1.5 takes a broader perspective, discussing the institutional environment and overall objectives of portfolio risk management, and its fundamental limitations.

1.1 Measuring Return

Except where noted, throughout the book we work in terms of a unit investment, that is, an investment with an initial value of $1. The analysis can be scaled up to cover an investment of any size.

1.1.1 Arithmetic and Logarithmic Return

We define the investment universe as the set of assets that are current or potential holdings in the portfolio. In many applications, the investment universe is very large since it includes individual stocks, bonds, commodities, and other assets from around the world. We use n to denote the number of assets in the investment universe.

We usually measure the random per-period payoff on an asset in terms of its arithmetic return, denoted by r. This is defined as the end-of-period price plus any end-of-period cash flow, divided by the beginning-of-period price, minus one. Intermediate cash flows can be artificially shifted to the end of the period using an appropriate reinvestment rate. The arithmetic return on a portfolio is defined analogously as the end-of-period value plus cash flow divided by the beginning-of-period value, minus one. Note that return is a random variable; its expected value is called the expected return.

The arithmetic portfolio return equals the weighted sum of constituent asset returns. We use r to denote the n-vector of asset returns on all the assets in the investment universe, and w the n-vector of portfolio weights. These weights give the proportion invested in each asset, and therefore sum to one. (In a typical application, the vast majority of portfolio weights are zero, since the number of assets in the investment universe tends to be much larger than the number held in nonzero amounts in the portfolio.) The portfolio return can be expressed as

r_w = w' r. (1.1)

Arithmetic return supports subportfolio analysis: the arithmetic return on the portfolio equals the value-weighted sum of the returns on any complete collection of nonoverlapping subportfolios. We can apply (1.1) to nested subsets, first using it to find the returns to subportfolios, where w is the vector of asset weights within a subportfolio and r is the vector of assets in the subportfolio, and then to the aggregate portfolio, where w is the vector of weights in the subportfolios and r is the vector of subportfolio returns. At each step, the investment universe is defined appropriately. So, for example, consider a large diverse portfolio of common stocks, government bonds, corporate bonds, and real estate. Defining the investment universe as the collection of all individual assets in all the categories, the total portfolio return is the weighted sum of all the individual returns. Alternatively, if desired, we can first calculate the return on the common stock portfolio using the universe of common stocks as the set of assets and the total amount invested in common stocks as total value. From this we get a common stock portfolio return. Repeating this procedure for government bonds, then corporate bonds, and then real estate gives analogously a government bond portfolio return, a corporate bond portfolio return, and a real estate portfolio return. Now we redefine the investment universe as consisting of these four "assets" (our computed subportfolio returns) so that in the second step n = 4, and we can compute the total portfolio return as the weighted sum of these four asset returns.

Return is typically measured at daily, weekly, monthly, quarterly, and annual frequencies. However, the T single-period arithmetic returns do not add up to the arithmetic return over T periods. Instead, the T-period arithmetic return on an asset or portfolio is the compound product of the constituent one-period returns:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.2)

When written in this form, the T-period return is called the compound return to highlight the fact that it is a product of returns at higher frequency. If the intervening single-period returns are uncorrelated, then the expected compound return equals the product of the returns (each augmented by one) and then minus one:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

If, in addition, the single-period returns are independent through time with constant mean and variance, then the variance of the compound return can be expressed in terms of the single-period mean and variance:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.3)

However, this relatively simple formula for variance (1.3) does not apply if the single-period mean or variance is time dependent.

Temporal analysis of return is simpler in the continuously compounded or logarithmic framework. The log return of an asset or portfolio is defined as the log of the end-of-period value plus cash flow minus the log of the beginning-of-period value. Mathematically, this is written

r_l = log(1 + r).

Log returns add up over time. Letting rl0,T denote the log return over T periods,

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Details zur Ausgabe

• Verlag: Princeton University Press
• Erscheinungsdatum: 2010
• Sprache: Englisch
• ISBN 10: 0691128286
• ISBN 13: 9780691128283
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 354
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: Buchbär; info@buchbaer.de; ; Weberstr. 67; Osnabrück; 49084; Deutschland