Most recent empirical option valuation studies build on the affine square root (SQR) stochastic volatility model. The SQR model is a convenient choice, because it yields closed-form solutions for option prices. We investigate alternatives to the SQR model, by comparing its empirical performance with that of five different but equally parsimonious stochastic volatility models. We provide empirical evidence from three different sources: realized volatilities, S&P500 returns, and an extensive panel of option data. The three sources of data all point to the same conclusion: the best volatility specification is one with linear rather than square root diffusion for variance. This model captures the stylized facts in realized volatilities, it performs well in fitting various samples of index returns, andit has the lowest option implied volatility mean squared error in and out of sample.

The cross-section of stock returns has substantial exposure to risk captured by higher moments in market returns. We estimate these moments from daily S&P 500 index option data. The resulting time series of factors are thus genuinely conditional and forward-looking. Stocks with high sensitivities to innovations in implied market volatility and skewness exhibit low returns on average, whereas those with high sensitivities to innovations in implied market kurtosis exhibit high returns on average. The results on market skewness risk are extremely robust to various permutations of the empirical setup. The estimated premium for bearing market skewness risk is between -6.00% and -8.40% annually. This market skewness risk premium is economically significant and cannot be explained by other common risk factors such as the market excess return or the size, book-to-market, momentum, and market volatility factors. Using ICAPM intuition, the negative price of market skewness risk indicates that it is a state variable that negatively affects the future investment opportunity set.

Under very general conditions, the total quadratic variation of a jump-diffusion process can be decomposed into diffusive volatility and squared jump variation. We use this result to develop a new option valuation model in which the underlying asset price exhibits volatility and jump intensity dynamics. The volatility and jump intensity dynamics in the model are directly driven by model-free empirical measures of diffusive volatility and jump variation. Because the empirical measures are observed in discrete intervals, our option valuation model is cast in discrete time, allowing for straightforward filtering and estimation of the model. Our model belongs to the affine class enabling us to derive the conditional characteristic function so that option values can be computed rapidly without simulation. When estimated on S&P500 index options and returns the new model performs well compared with standard benchmarks.

We use intraday data to compute weekly realized moments for equity returns and study their time-series and cross-sectional properties. Buying stocks in the lowest realized skewness decile and selling stocks in the highest realized skewness decile generates an average return of 19 basis points the following week with a t-statistic of 3.70. This result is robust across a wide variety of implementations and is not captured by the Fama-French and Carhart factors. The relation between realized kurtosis and next week׳s stock returns is positive but not always significant. We do not find a strong relation between realized volatility and next week׳s stock returns.

We show that the prices of risk for factors that are nonlinear in the market return can be obtained using index option prices. The price of coskewness risk corresponds to the market variance risk premium, and the price of cokurtosis risk corresponds to the market skewness risk premium. Option-based estimates of the prices of risk lead to reasonable values of the associated risk premia. An analysis of factor models with coskewness risk indicates that the new estimates of the price of risk improve the models’ performance compared with regression-based estimates.

Many studies have documented that daily realized volatility estimates based on intraday returns provide volatility forecasts that are superior to forecasts constructed from daily returns only. We investigate whether these forecasting improvements translate into economic value added. To do so, we develop a new class of affine discrete-time option valuation models that use daily returns as well as realized volatility. We derive convenient closed-form option valuation formulas, and we assess the option valuation properties using Standard & Poor’s (S&P) 500 return and option data. We find that realized volatility reduces the pricing errors of the benchmark model significantly across moneyness, maturity, and volatility levels.

We develop a conditional capital asset pricing model in continuous time that allows for stochastic beta exposure. When beta comoves with market variance and the stochastic discount factor (SDF), beta risk is priced, and the expected return on a stock deviates from the security market line. The model predicts that low-beta stocks earn high returns, because their beta positively comoves with market variance and the SDF. The opposite is true for high-beta stocks. Estimating the model on equity and option data, we find that beta risk explains expected returns on low- and high-beta stocks, resolving the “betting against beta” anomaly. Authors have furnished code and an Internet Appendix, which are available on the Oxford University Press Web site next to the link to the final published paper online.

Nous prsentons les rsultats d'une tude portant sur l'valuation de crances ventuelles de style europen pour une grande varit de caractristiques lies au rendement des actifs sousjacents. Les rsultats de notre valuation proposent en temps discret une formule tat-espace infinie, partir du principe de non-arbitrage et d'une mesure de martingale quivalente. Notre approche permet de tenir compte de formes gnrales d'htroscdasticit dans les rendements et d'obtenir, dans des cas spciaux, des rsultats d'valuation lis aux processus homoscdastiques. Elle permet aussi de considrer les innovations conditionnellement non normales en matire de rendement, ce qui reprsente un facteur critique, compte tenu du fait que l'htroscdasticit ne permet pas, elle seule, de saisir pleinement le caractre ironique de l'option. Nous analysons une catgorie de mesures de martingale quivalentes dont la dynamique du rendement risque-neutre obtenu est de la mme famille de distribution que la dynamique du rendement physique. Dans ce cas, notre cadre d'tude soutient les rsultats d'valuation obtenus par Nous tendons ces rsultats aux mesures de martingale quivalentes plus gnrales et aux modles de volatilit stochastique en temps discret et analysons aussi la relation entre nos rsultats et ceux obtenus dans le cas des modles en temps continu.

We estimate a continuous-time model for the stock market index where the stochastic volatility and crash probability depend on the realized spot variance and the stock market illiquidity. We find that market illiquidity is a useful economic covariate in the modeling of time-varying stock market crash risk embedded in index options. The relative contribution of spot variance in the time-varying crash risk is weakened once the market illiquidity variable is added to the model, and out-of-sample option pricing error also improves. Examining the relationship between market illiquidity and option-implied crash risk, we find that the availability of arbitrage capital and adverse selection facing liquidity providers are potential economic links. Our study highlights the benefits of adding a market illiquidity measure to index return models with time-varying crash risk.