Idiosyncratic Volatility, Growth Options, and the Cross-Section of Returns


Book Description

The paper shows that the value effect and the idiosyncratic volatility (IVol) discount (Ang et al., 2006) arise because growth firms and high IVol firms beat the CAPM during the periods of increasing aggregate volatility, which makes their risk low. All else equal, growth options' value increases with volatility, and this effect is stronger for high IVol firms, for which growth options take a larger fraction of the firm value and firm volatility responds more to aggregate volatility changes. The empirical volatility factor model with the market factor, the market volatility risk factor (FVIX) and the average IVol factor (FIVol) explains the value effect and the IVol discount and why those anomalies are stronger for firms with high short sale constraints.







Idiosyncratic Volatility and the Cross-Section of Expected Returns


Book Description

This paper examines the cross-sectional relation between idiosyncratic volatility and expected stock returns. The results indicate that (i) data frequency used to estimate idiosyncratic volatility, (ii) weighting scheme used to compute average portfolio returns, (iii) breakpoints utilized to sort stocks into quintile portfolios, and (iv) using a screen for size, price and liquidity play a critical role in determining the existence and significance of a relation between idiosyncratic risk and the cross-section of expected returns. Portfolio-level analyses based on two different measures of idiosyncratic volatility (estimated using daily and monthly data), three weighting schemes (value-weighted, equal-weighted, inverse-volatility-weighted), three breakpoints (CRSP, NYSE, equal-market-share), and two different samples (NYSE/AMEX/NASDAQ and NYSE) indicate that there is no robust, significant relation between idiosyncratic volatility and expected returns.




Idiosyncratic Volatility, Its Expected Variation, and the Cross-Section of Stock Returns


Book Description

We show that the widely documented negative relation between idiosyncratic volatility (IVOL) and expected returns can be explained by the mean reversion of stocks' idiosyncratic volatilities. We use option-implied information to extract the mean reversion speed of IVOL in an almost model-free fashion. This allows us to identify stocks for which past IVOL is a bad proxy for expected IVOL. These stocks solely drive the negative relation, and a long--short portfolio earns a monthly risk-adjusted return of 2.74%, on average. In a horse race, the mean reversion speed is superior to prominent competing explanations of the IVOL puzzle.




Cointegration, Causality, and Forecasting


Book Description

A collection of essays in honour of Clive Granger. The chapters are by some of the world's leading econometricians, all of whom have collaborated with and/or studied with both) Clive Granger. Central themes of Granger's work are reflected in the book with attention to tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric econometric techniques, and overall, a careful blend of practical empirical work and strong theory. The book shows the scope of Granger's research and the range of the profession that has been influenced by his work.




Empirical Asset Pricing


Book Description

“Bali, Engle, and Murray have produced a highly accessible introduction to the techniques and evidence of modern empirical asset pricing. This book should be read and absorbed by every serious student of the field, academic and professional.” Eugene Fama, Robert R. McCormick Distinguished Service Professor of Finance, University of Chicago and 2013 Nobel Laureate in Economic Sciences “The empirical analysis of the cross-section of stock returns is a monumental achievement of half a century of finance research. Both the established facts and the methods used to discover them have subtle complexities that can mislead casual observers and novice researchers. Bali, Engle, and Murray’s clear and careful guide to these issues provides a firm foundation for future discoveries.” John Campbell, Morton L. and Carole S. Olshan Professor of Economics, Harvard University “Bali, Engle, and Murray provide clear and accessible descriptions of many of the most important empirical techniques and results in asset pricing.” Kenneth R. French, Roth Family Distinguished Professor of Finance, Tuck School of Business, Dartmouth College “This exciting new book presents a thorough review of what we know about the cross-section of stock returns. Given its comprehensive nature, systematic approach, and easy-to-understand language, the book is a valuable resource for any introductory PhD class in empirical asset pricing.” Lubos Pastor, Charles P. McQuaid Professor of Finance, University of Chicago Empirical Asset Pricing: The Cross Section of Stock Returns is a comprehensive overview of the most important findings of empirical asset pricing research. The book begins with thorough expositions of the most prevalent econometric techniques with in-depth discussions of the implementation and interpretation of results illustrated through detailed examples. The second half of the book applies these techniques to demonstrate the most salient patterns observed in stock returns. The phenomena documented form the basis for a range of investment strategies as well as the foundations of contemporary empirical asset pricing research. Empirical Asset Pricing: The Cross Section of Stock Returns also includes: Discussions on the driving forces behind the patterns observed in the stock market An extensive set of results that serve as a reference for practitioners and academics alike Numerous references to both contemporary and foundational research articles Empirical Asset Pricing: The Cross Section of Stock Returns is an ideal textbook for graduate-level courses in asset pricing and portfolio management. The book is also an indispensable reference for researchers and practitioners in finance and economics. Turan G. Bali, PhD, is the Robert Parker Chair Professor of Finance in the McDonough School of Business at Georgetown University. The recipient of the 2014 Jack Treynor prize, he is the coauthor of Mathematical Methods for Finance: Tools for Asset and Risk Management, also published by Wiley. Robert F. Engle, PhD, is the Michael Armellino Professor of Finance in the Stern School of Business at New York University. He is the 2003 Nobel Laureate in Economic Sciences, Director of the New York University Stern Volatility Institute, and co-founding President of the Society for Financial Econometrics. Scott Murray, PhD, is an Assistant Professor in the Department of Finance in the J. Mack Robinson College of Business at Georgia State University. He is the recipient of the 2014 Jack Treynor prize.




Stochastic Portfolio Theory


Book Description

Stochastic portfolio theory is a mathematical methodology for constructing stock portfolios and for analyzing the effects induced on the behavior of these portfolios by changes in the distribution of capital in the market. Stochastic portfolio theory has both theoretical and practical applications: as a theoretical tool it can be used to construct examples of theoretical portfolios with specified characteristics and to determine the distributional component of portfolio return. This book is an introduction to stochastic portfolio theory for investment professionals and for students of mathematical finance. Each chapter includes a number of problems of varying levels of difficulty and a brief summary of the principal results of the chapter, without proofs.




Idiosyncratic Volatility and Expected Returns at the Global Level


Book Description

We investigate the existence and significance of a cross-sectional relation between idiosyncratic volatility and expected returns at the global level by introducing a global idiosyncratic volatility measure and globally diversified test assets. We find that the portfolios with the highest and lowest global idiosyncratic volatility don't earn significantly different average returns, indicating the absence of a link between global idiosyncratic volatility and expected returns. This result is robust to three different samples utilized; two different asset pricing models, two different data frequencies, and an alternative idiosyncratic volatility definition used to estimate global idiosyncratic volatility; two different weighting schemes to calculate portfolio returns and after controlling for the size of the global idiosyncratic volatility sorted portfolios. It also holds for four different sub-periods and eight subsamples reflecting different states of the economy and stock markets. Our results show that global diversification is effective in stabilizing the returns of global test assets as global investors don't require a risk premium for bearing global idiosyncratic volatility and that benefits from global diversification can be gained by diversifying across countries or across industries.




The Time-Series Behavior and Pricing of Idiosyncratic Volatility


Book Description

Recent research on idiosyncratic volatility has documented three main empirical findings. First, Campbell, Lettau, Malkiel, and Xu (2001) show that idiosyncratic volatility exhibits an upward trend between 1962 and 1997. Second, Goyal and Santa-Clara (2003) find that aggregate measures of idiosyncratic volatility predict one-month-ahead excess market returns from 1962 to 1999. Third, Ang, Hodrick, Xing, and Zhang (2006) report a negative and significant relation between idiosyncratic volatility and cross-sectional stock returns from 1963 to 2000. We re-examine these three findings using a 37-year holdout sample of daily returns from 1926 to 1962. We find robust empirical evidence of (1) a statistically significant downward trend in idiosyncratic volatility, (2) an insignificant relation between average idiosyncratic volatility and one-month-ahead excess market returns, and (3) a highly significant inverse relation between idiosyncratic volatility and cross-sectional stock returns. These results shed new light on the time-series behavior and pricing of idiosyncratic volatility.




Idiosyncratic Volatility and the Pricing of Poorly-Diversified Portfolios


Book Description

This article examines the role of idiosyncratic volatility in explaining the cross-sectional variation of size- and value-sorted portfolio returns. We show that the premium for bearing idiosyncratic volatility varies inversely with the number of stocks included in the portfolios. This conclusion is robust within various multifactor models based on size, value, past performance, liquidity and total volatility and also holds within an ICAPM specification of the risk-return relationship. Our findings thus indicate that investors demand an additional return for bearing the idiosyncratic volatility of poorly-diversified portfolios.