Forecasting Implied Volatilities for Options on Index Futures: Time-Series and Cross-Sectional Analysis versus Constant Elasticity of Variance (CEV) Model

Abstract

In this chapter, we use two alternative approaches, time-series and cross-sectional analysis and constant elasticity of variance (CEV) model, to give different perspective of forecasting implied volatility. We use call options on the S&P 500 index futures expired within 2010 to 2013 to do the empirical work. The empirical results show that volatility changes are predictable by using cross-sectional time-series analysis and CEV model. The prediction power of these two methods can draw specific implications as to how Black model might be misspecified. The cross-sectional analysis can capture other trading behaviors such as week effect and in-/out- of the money effect. The abnormal returns in our trading strategy with the consideration of transaction costs indicate the market of options on index futures may be inefficient. The assumption of a noncentral χ2 distribution in CEV model can capture the skewness and kurtosis effects of index future options. According to the empirical studies of in-sample fitness and out-of-sample results, the CEV model performs better than Black model because it can generalize implied volatility surface as a function of asset price.