TY - JOUR T1 - Forecasting Options Prices Using Discrete Time Volatility Models Estimated at Mixed Timescales JF - The Journal of Derivatives DO - 10.3905/jod.2019.1.094 SP - jod.2019.1.094 AU - Giovanni Calice AU - Jing Chen AU - Julian Williams Y1 - 2019/12/13 UR - https://pm-research.com/content/early/2019/12/13/jod.2019.1.094.abstract N2 - Option pricing models traditionally have utilized continuous-time frameworks to derive solutions or Monte Carlo schemes to price the contingent claim. Typically these models were calibrated to discrete-time data using a variety of approaches. Recent work on GARCH based option pricing models have introduced a set of models that easily can be estimated via MLE or GMM directly from discrete time spot data. This paper provides a series of extensions to the standard discrete-time options pricing setup and then implements a set of various pricing approaches for a very large cross-section of equity and index options against the forward-looking traded market price of these options, out-of-sample. Our analysis provides two significant findings. First, we provide clear evidence that including autoregressive jumps in the options model is critical in determining the correct price of heavily out-of-the money and in-the-money options relatively close to maturity. Second, for longer maturity options, we show that the anticipated performance of the popular component GARCH models, which exhibit long persistence in volatility, does not materialize. We ascribe this result in part to the inherent instability of the numerical solution to the option price in the presence of component volatility. Taken together, our results suggest that when pricing options, the first best approach is to include jumps directly in the model, preferably using jumps calibrated from intraday data.TOPICS: Options, volatility measuresKey Findings• In this paper, we present a new method for estimating the parameters for a jump GARCH model. We provide a series of empirical tests of the efficacy of the GARCH type option models. We analyse the S&P 500 index and for a sample of 20 individual equities sampled from the Dow Jones 30. Our out-of-sample test covers over a third of a million individually equity traded prices.• We find three primary empirical results. First, pre-filtering for jumps improves the accuracy of options models based on GARCH processes. Second, for certain stocks, models that explicitly incorporate jumps substantially outperform all other models. Third, for the S&P500, the GARCH model estimated on jump-filtered returns appears to dominate. ER -