Option moneyness classification using support vector machine

Determining the theoretical price for an option, or option pricing, is regarded as one of the most important issues in financial research. In recent years, linear and non-linear GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) models were used to estimate volatility. However, the empirical analysis of various different volatility model estimations has not achieved consistent results. This study construct an Taiwan's existing tech index options price classification with various a values to determine the moneyness (at-the-money, in-the-money, out-the-money) of option price. This study tested 140 models, the combinations included 4 types of the kernel function in multi-SVM (Linear, Polynomial, RBF, Sigmoid), 7 types of volatility estimation (historical volatility, implied volatility, GARCH, IGARCH, GJR-CARCH, EGARCH, TBGARCH) and 5 types of α (2%, 4%,5%,6%,8%). Finally, the classification result shows that using α=2%, polynomial function multi-SVM with the three types of volatility estimation methods of TBGARCH, EGARCH and GJR-GARCH would yield better classification performance.