We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. In addition, we find that stocks with high idiosyncratic volatility relative to the Fama and French (1993) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.

The Cross-Section of Volatility and Expected Returns

We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility. IT IS WELL KNOWN THAT THE VOLATILITY OF STOCK RETURNS varies over time. While considerable research has examined the time-series relation between the volatility of the market and the expected return on the market (see, among others, Campbell and Hentschel (1992) and Glosten, Jagannathan, and Runkle (1993)), the question of how aggregate volatility affects the cross-section of expected stock returns has received less attention. Time-varying market volatility induces changes in the investment opportunity set by changing the expectation of future market returns, or by changing the risk-return trade-off. If the volatility of the market return is a systematic risk factor, the arbitrage pricing theory or a factor model predicts that aggregate volatility should also be priced in the cross-section of stocks. Hence, stocks with different sensitivities to innovations in aggregate volatility should have different expected returns. The first goal of this paper is to provide a systematic investigation of how the stochastic volatility of the market is priced in the cross-section of expected stock returns. We want to both determine whether the volatility of the market

https://www0.gsb.columbia.edu/faculty/rhodrick/crosssection.pdf

In the setting of ‘‘affine’’ jump-diffusion state processes, this paper provides an analytical treatment of a class of transforms, including various Laplace and Fourier transforms as special cases, that allow an analytical treatment of a range of valuation and econometric problems. Example applications include fixed-income pricing models, with a role for intensity-based models of default, as well as a wide range of option-pricing applications. An illustrative example examines the implications of stochastic volatility and jumps for option valuation. This example highlights the impact on option ‘smirks’ of the joint distribution of jumps in volatility and jumps in the underlying asset price, through both jump amplitude as well as jump timing.

/pdf/transform-analysis-and-asset-pricing-for-affine-jump-nauz839ruj.pdf

Transform analysis and asset pricing for affine jump-diffusions

It is a common nancial practice to estimate volatility from the sum of frequently-sampled squared returns. However market microstructure poses challenge to this estimation approach, as evidenced by recent empirical studies in nance. This work attempts to lay out theoretical grounds that reconcile continuous-time modeling and discrete-time samples. We propose an estimation approach that takes advantage of the rich sources in tick-by-tick data while preserving the continuous-time assumption on the underlying returns. Under our framework, it becomes clear why and where the \usual" volatility estimator fails when the returns are sampled at the highest frequency.

/pdf/a-tale-of-two-time-scales-determining-integrated-volatility-314avtkr3k.pdf

A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data

Using high-frequency data on deutschemark and yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility and correlation that cover an entire decade. Our estimates, termed realized volatilities and correlations, are not only model-free, but also approximately free of measurement error under general conditions, which we discuss in detail. Hence, for practical purposes, we may treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normality-inducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and persistent dynamics in volatilities and correlations, evidence of long-memory dynamics in volatilities and correlations, and remarkably precise scaling laws under temporal aggregation.

/pdf/the-distribution-of-realized-exchange-rate-volatility-3eu34k7tio.pdf

The Distribution of Realized Exchange Rate Volatility

http://www.cirano.qc.ca/pdf/publication/2002s-58.pdf

Alternative models for stock price dynamics

This paper examines model specification issues and estimates diffusive and jump risk premia using S&P futures option prices from 1987 to 2003. We first develop a time series test to detect the presence of jumps in volatility, and find strong evidence in support of their presence. Next, using the cross section of option prices, we find strong evidence for jumps in prices and modest evidence for jumps in volatility based on model fit. The evidence points toward economically and statistically significant jump risk premia, which are important for understanding option returns.

/pdf/model-specification-and-risk-premia-evidence-from-futures-59xmgu3max.pdf

Model specification and risk premia: evidence from futures options

A study towards a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation

We use equity index options to quantify the distribution of consumption growth disasters. The challenge lies in connecting the risk-neutral distribution of equity returns implied by options to the true distribution of consumption growth estimated from macroeconomic data. We attack the problem from three perspectives. First, we compare pricing kernels constructed from macro-finance and option-pricing models. Second, we compare option prices derived from a macro-finance model to those we observe. Third, we compare the distribution of consumption growth derived from option prices using a macro-finance model to estimates based on macroeconomic data. All three perspectives suggest that options imply smaller probabilities of extreme outcomes than have been estimated from international macroeconomic data. The third comparison yields a viable alternative calibration of the distribution of consumption growth that matches the equity premium, option prices, and the sample moments of US consumption growth.

/pdf/disasters-implied-by-equity-index-options-1lt5sev01t.pdf

Disasters Implied by Equity Index Options

This paper studies the returns from investing in index options. Previous research documents significant average option returns, large CAPM alphas, and high Sharpe ratios, and concludes that put options are mispriced. We propose an alternative approach to evaluate the significance of option returns and obtain different conclusions. Instead of using these statistical metrics, we compare historical option returns to those generated by commonly used option pricing models. We find that the most puzzling finding in the existing literature, the large returns to writing out-of-the-money puts, is not even inconsistent with the Black-Scholes model. Moreover, simple stochastic volatility models with no risk premia generate put returns across all strikes that are not inconsistent with the observed data. At-the-money straddle returns are more challenging to understand, and we find that these returns are not inconsistent with explanations such as jump risk premia, Peso problems, and estimation risk.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1947051_code1671431.pdf?abstractid=1947051&mirid=2

Mikhail Chernov

Papers

Alternative models for stock price dynamics

Model specification and risk premia: evidence from futures options

A study towards a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation

Disasters Implied by Equity Index Options

Understanding Index Option Returns