Postmodern Portfolio Theory: Navigating Abnormal Markets and Investor Behavior

ISBN-13: 9781137544636 / Angielski / Twarda / 2016 / 339 str.

James Chen

Opis

This survey of portfolio theory, from its modern origins through more sophisticated, "postmodern" incarnations, evaluates portfolio risk according to the first four moments of any statistical distribution: mean, variance, skewness, and excess kurtosis.

Spis treści

Part I — Perpetual Possibility in a World of Speculation: Portfolio Theory in Its Modern and Postmodern Incarnations

 

Chapter 1 — Modern Portfolio Theory

            § 1.1 — Mathematically informed risk management

            § 1.2 — Measures of risk; the Sharpe ratio

            § 1.3 — Beta

            § 1.4 — The capital asset pricing model

            § 1.5 — The Treynor ratio

            § 1.6 — Alpha

            § 1.7 — The efficient markets hypothesis

            § 1.8 — The efficient frontier

 

Chapter 2 — Postmodern Portfolio Theory

            § 2.1 — A renovation project

            § 2.2 — An orderly walk

            § 2.3 — Roll’s critique

            § 2.4 — The echo of future footfalls

 

 

Part II — Bifurcating Beta in Financial and Behavioral Space

 

Chapter 3 — Seduced by Symmetry, Smarter by Half

            § 3.1 — Splitting the atom of systematic risk

            § 3.2 — The catastrophe of success

            § 3.3 — Reviving beta’s dead hand

            § 3.4 — Sinking, fast and slow

 

Chapter 4 —The Full Financial Toolkit of Partial Second Moments

§ 4.1 — A history of downside risk measures

§ 4.2 — Safety first

§ 4.3 — Semivariance, semideviation, and single-sided beta

§ 4.4 — Traditional CAPM specifications of volatility, variance, covariance, correlation, and beta

§ 4.5 — Deriving semideviation and semivariance from upper and lower partial moments

 

Chapter 5 — Sortino, Omega, Kappa: The Algebra of Financial Asymmetry

§ 5.1 — Extracting downside risk measures from lower partial moments

§ 5.2 — The Sortino ratio

§ 5.3 — Comparing the Treynor, Sharpe, and Sortino ratios

§ 5.4 — Pythagorean extensions of second-moment measures: Triangulating deviation about a target not equal to the mean

§ 5.5 — Further Pythagorean extensions: Triangulating semivariance and semideviation

§ 5.6 — Single-sided risk measures in popular financial reporting

§ 5.7 — The trigonometry of semideviation

§ 5.8 — Omega

§ 5.9 — Kappa

§ 5.10 — An overview of single-sided measures of risk based on lower partial moments

§ 5.11 — Noninteger exponents versus ordinary polynomial representations

 

Chapter 6 — Sinking, Fast and Slow: Relative Volatility Versus Correlation Tightening

§ 6.1 — The two behavioral faces of single-sided beta

§ 6.2 — Parameters indicating relative volatility and correlation tightening

§ 6.3 — Relative volatility and the beta quotient

§ 6.4 — The low-volatility anomaly (and Bowman’s paradox)

§ 6.5 — Correlation tightening

§ 6.6 — Correlation tightening in emerging markets

§ 6.7 — Isolating and pricing correlation risk

§ 6.8 — Low volatility revisited

§ 6.9 — Low volatility and banking’s “curse of quality”

§ 6.10 — Downside risk, upside reward

 

 

Part III — Τέσσερα, Τέσσερα : Four Dimensions, Four Moments

 

Chapter 7 — Time-Varying Beta: Autocorrelation and Autoregressive Time Series

§ 7.1 — Finding in motion what was lost in time

§ 7.2 — The conditional capital asset pricing model

§ 7.3 — Conditional beta

§ 7.4 — Conventional time series models

§ 7.5 — Asymmetrical time series models

 

Chapter 8 — Asymmetric Volatility and Volatility Spillovers

§ 8.1 — The origins of asymmetrical volatility; the leverage effect

§ 8.2 — Volatility feedback

§ 8.3 — Options pricing and implied volatility

§ 8.4 — Asymmetrical volatility and volatility spillover around the world

 

Chapter 9 — A Four-Moment Capital Asset Pricing Model

§ 9.1 — Harbingers of a four-moment capital asset pricing model

§ 9.2 — Four-moment CAPM as a response to the Fama-French-Carhart four-factor model

§ 9.3 — From asymmetric beta to coskewness and cokurtosis

§ 9.4 — Skewness and kurtosis

§ 9.5 — Higher-moment CAPM as a Taylor series expansion

§ 9.6 — Interpreting odd versus even moments

§ 9.7 — Approximating and truncating the Taylor series expansion

§ 9.8 — Profusion and confusion over measures of coskewness and cokurtosis

§ 9.9 — A possible cure for portfolio theory’s curse of dimensionality: Relative lower partial moments

 

Chapter 10 — The Practical Implications of a Spatially Bifurcated Four-Moment Capital Asset Pricing Model

§ 10.1 — Four-moment CAPM versus the four-factor model

§ 10.2 — Correlation asymmetry

§ 10.3 — Emerging markets

§ 10.4 — Size, value, and momentum

 

 

Part IV — Managing Kurtosis: Measures of Market Risk in Global Banking Regulation

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Chapter 11 — Going to Extremes: Leptokurtosis as an Epistemic Threat

§ 11.1 — Value-at-risk (VaR) and expected shortfall in global banking regulation

§ 11.2 — Leptokurtosis, fat tails, and non-Gaussian distributions

 

Chapter 12 — Parametric Value-at-Risk (VaR) Analysis

§ 12.1 — The Basel Committee on Bank Supervision and the Basel accords

§ 12.2 — The vulnerability of VaR analysis to model risk

§ 12.3 — Gaussian VaR

§ 12.4 — A simple worked example

 

Chapter 13 — Parametric VaR According to Student’s t-Distribution

§ 13.1 — Choosing among non-Gaussian distributions

§ 13.2 — Stable Paretian distributions

§ 13.3 — Student’s t-distribution

§ 13.4 — The probability density and cumulative distribution functions of Student’s t-distribution

§ 13.5 — Adjusting Student’s t-distribution according to observed levels of kurtosis

§ 13.6 — Performing Parametric VaR Analysis with Student’s t-distribution

 

Chapter 14 — Comparing Student’s t-Distribution with the Logistic Distribution

§ 14.1 — The logistic distribution

§ 14.2 — Equal kurtosis, unequal variance

 

Chapter 15 — Expected Shortfall as a Response to Model Risk

§ 15.1 — Value-at-risk versus expected shortfall

§ 15.2 — The incoherence of VaR

§ 15.3 — Extrapolating expected shortfall from VaR

§ 15.4 — A worked example

§ 15.5 — Formally calculating expected shortfall from VaR under Student’s t-distribution

§ 15.6 — Expected shortfall under a logistic model

 

Chapter 16 —Latent Perils: Stressed VaR, Elicitability, and Systemic Risk

§ 16.1 — Additional concerns

§ 16.2 — Stressed VaR

§ 16.3 — Expected shortfall and the elusive ideal of elicitability

§ 16.4 — Systemic risk

§ 16.5 — A dismal forecast

 

Conclusion: Finance as a Romance of Many Moments

O autorze

James Ming Chen holds the Justin Smith Morrill Chair in Law at Michigan State University, USA. He teaches, lectures, and writes widely on law, economics, and regulation. His books, Disaster Law and Policy and Postmodern Portfolio Theory, cover a broad range of issues concerning extreme events and risk management, from natural to financial disasters. He is of counsel to the Technology Law Group of Washington, D.C.; a public member of the Administrative Conference of the United States; and an elected member of the American Law Institute. A magna cum laude graduate of Harvard Law School and a former editor of the Harvard Law Review, Chen also served as a clerk to Justice Clarence Thomas of the Supreme Court of the United States.

Blurb

This survey of portfolio theory, from its modern origins through more sophisticated, “postmodern” incarnations, evaluates portfolio risk according to the first four moments of any statistical distribution: mean, variance, skewness, and excess kurtosis. In pursuit of financial models that more accurately describe abnormal markets and investor psychology, this book bifurcates beta on either side of mean returns. It then evaluates this traditional risk measure according to its relative volatility and correlation components. After specifying a four-moment capital asset pricing model, this book devotes special attention to measures of market risk in global banking regulation.

 

Despite the deficiencies of modern portfolio theory, contemporary finance continues to rest on mean-variance optimization and the two-moment capital asset pricing model. The term postmodern portfolio theory captures many of the advances in financial learning since the original articulation of modern portfolio theory. A comprehensive approach to financial risk management must address all aspects of portfolio theory, from the beautiful symmetries of modern portfolio theory to the disturbing behavioral insights and the vastly expanded mathematical arsenal of the postmodern critique. Mastery of postmodern portfolio theory’s quantitative tools and behavioral insights holds the key to the efficient frontier of risk management.