The statistical properties of stock returns have long been of interest to financial decision makers and academics alike. In particular, the great stock market crashes of the 20^{th} century pose particular challenges to economic and statistical models. In the past decades, there have been elaborate efforts by researchers to build models that explicitly allow for large market movements, or “fat tails” in return distributions. The literature has mainly focused on two approaches: (1) time-varying volatility models that allow for market extremes to be the outcome of normally distributed shocks that have a randomly changing variance, and (2) models that incorporate discontinuous jumps in the asset price.

Neither stochastic volatility models nor jump models have alone proven entirely empirically successful. For example, in the time-series literature, the models run into problems explaining large price movements such as the October 1987 crash. For stochastic volatility models, one problem is that a daily move of −22% requires an implausibly high-volatility level both prior to, and after the crash. Jump models on the other hand, can easily explain the crash of 1987 by a parameterization that allows for a sufficiently negative jump. However, jump models typically specify jumps to arrive with constant intensity. This assumption poses problems in explaining the tendency of large movements to cluster over time. In the case of the 1987 crash, for example, there were large movements both prior to, and following the crash. With respect to option prices, this modeling assumption implies that a jump has no impact on relative option prices. Hence, a price jump cannot explain the enormous increase in implied volatility following the crash of 1987. In response to these issues, researchers have proposed models that incorporate both stochastic volatility and jumps components.

In particular, recent work by Bates (2000) and Pan (2002) examines combined jump diffusion models. Their estimates are obtained from options data and joint returns/options data, respectively.^{1} They conclude that the jump diffusions in question do not adequately describe the systematic variations in option prices. Results in both papers point toward models that include jumps to the volatility process.

In response to these findings, Eraker, Johannes, and Polson (EJP) (2000) use returns data to investigate the performance of models with jumps in volatility as well as prices using the class of jump-in-volatility models proposed by Duffie, Pan, and Singleton (2000) (henceforth DPS). The DPS class of models generalizes the models in Merton (1976), Heston (1993), and Bates (1996). The results in EJP show that the jump-in-volatility models provide a significantly better fit to the returns data. EJP also provide decompositions of various large market movements which suggest that large returns, including the crash of 1987, are largely explained ex post by a jump to volatility. In a nutshell, the volatility-based explanation in EJP is driven by large subsequent moves in the stock market following the crash itself. This clustering of large returns is inconsistent with the assumption of independently arriving price jumps, but consistent with a temporarily high level of spot volatility caused by a jump to volatility. Overall, the results in EJP are encouraging with respect to the models that include jumps to volatility. In the current paper, therefore, we put the volatility jumping model to a more stringent test and ask whether it can explain price changes in both stock markets and option markets simultaneously.

The econometric technique in EJP is based on returns data only. By contrast, the empirical results presented in this paper are based on estimates obtained from joint returns and options data—an idea pursued in Chernov and Ghysels (2000) and Pan (2002). This is an interesting approach because even if option prices are not one's primary concern, their use in estimation, particularly in conjunction with returns, offers several advantages. A primary advantage is that risk premiums relating to volatility and jumps can be estimated. This stands in contrast to studies that focus exclusively on either source of data. Secondly, the one-to-one correspondence of options to the conditional returns distribution allows parameters governing the shape of this distribution to potentially be very accurately estimated from option prices.^{2} For example, EJP report fairly wide posterior standard deviations for parameters that determine the jump sizes and jump arrival intensity. Their analysis suggests that estimation from returns data alone requires fairly long samples to properly identify all parameters. Hopefully, the use of option prices can lead to very accurate estimates, even in short samples. Moreover, the use of option prices allows, and in fact requires, the estimation of the latent stochastic volatility process. Since volatility determines the time variation in relative option prices, there is also a strong potential for increased accuracy in the estimated volatility process. Finally, joint estimation also raises an interesting and important question: Are estimates of model parameters and volatility consistent across both markets? This is the essential question to be addressed in this paper.

Previously, papers by Chernov and Ghysels (2000), and Pan (2002) have proposed GMM-based estimators for joint options/returns data using models similar to the ones examined here, but without the jump to volatility component. In this paper we develop an approach based on Markov Chain Monte Carlo (MCMC) simulation. MCMC allows the investigator to estimate the posterior distributions of the parameters as well as the unobserved volatility and jump processes. Recent work by Jacquier and Jarrow (2000) points to the importance of accounting for estimation risk in model evaluation. This is potentially even more important in our setting because the parameter space is so highly dimensional. For example, one practical implication of MCMC is that the filtered volatility paths obtained by MCMC methods tend to be more erratic than estimates obtained by other methods (see Jacquier, Polson, and Rossi (1994)). This is important because previous studies find that estimates of the “volatility-of-volatility” parameter governing the diffusion term in the volatility process, is too high to be consistent with time-series estimates of the volatility process.

The empirical findings reported in this paper can be summarized as follows. Parameter estimates obtained for the (Heston) stochastic volatility model as well as the (Bates (1996)) jump diffusion with jumps in prices, are similar to those in Bakshi, Cao, and Chen (1997). In particular, our estimates imply a jump every other year on average, which compared to estimates in EJP from returns data alone, is very low. The volatility-of-volatility estimates are higher than those found from returns data alone in EJP. However, we do not conclude that they are inconsistent with the latent volatility series. Our posterior simulations of the latent volatility series have “sample volatility-of-volatility” that almost exactly matches those estimated from the joint returns and options data. This evidence contrasts with the findings of model violations reported elsewhere.

Evidence from an in-sample test reported in this paper shows surprisingly little support for the jump components in option prices. The overall improvement in in-sample option price fit for the general model with jumps in both prices and volatility, is less than 2 cents relative to the stochastic volatility model. There is some evidence to suggest that this rather surprising finding can be linked to the particular sample period used for estimation. In particular, we show that the models tend to overprice long dated options out-of-sample—a finding that can be linked to the high volatility embedded in options prices during the estimation period. If the mean volatility parameter is adjusted to match its historical average, out-of-sample pricing errors drop dramatically, and the jump models perform much better than the SV model. The option pricing models also seem to perform reasonably well whenever the calculations are based on parameter estimates obtained using only time-series data of returns on the underlying index.

The jump models, and particularly the jump-in-volatility model, are doing a far better job of describing the time-series dimension of the problem. In particular, the general model produces return residuals with a sample kurtosis of a little less than four (one unit in excess of the hypothesized value under the assumed normal distribution). However, all models in question produce too heavily tailed residuals in the volatility process for it to be consistent with its assumed square root, diffusive behavior. This obtains even when the volatility process is allowed to jump. This model violation is caused primarily by a sequence of large negative outliers following the crash of 1987, and corresponds to the fall in the implied spot volatility process following the huge increase ( jump) on the day of the crash. Since the jump-in-volatility model allows for positive jumps to volatility, it does explain the run-up in relative option prices on the day of the crash, but it has problems explaining the subsequent drop. In conclusion, the jump-in-volatility model does improve markedly on the simpler models, but its dynamics do not seem sufficiently general to capture variations in both returns and options markets simultaneously.

The rest of the paper is organized as follows: In the next section, we present the general model for the stock price dynamics as formulated in Duffie et al. (2000) special cases of this model, and discuss implications for option pricing. Section II discusses the econometric design and outlines a strategy for obtaining posterior samples by MCMC. Section III presents the data, while Section IV contains the empirical results. Section V summarizes the findings and suggests directions for further investigation.