Postmodern Portfolio Theory, 1st ed. 2016 Navigating Abnormal Markets and Investor Behavior Quantitative Perspectives on Behavioral Economics and Finance Series
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
Date de parution : 08-2016
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