Financial Volatility Forecasting by Nonlinear Support Vector Machine Heterogeneous Autoregressive Model: Evidence from Nikkei 225 Stock Index

Support vector machines (SVMs) are new semi-parametric tool for regression estimation. This paper introduced a new class of hybrid models, the nonlinear support vector machines heterogeneous autoregressive (SVM-HAR) models and aimed to compare the forecasting performance with the classical heterogeneous autoregressive (HAR) models to forecast financial volatilities. It was observed through empirical experiment that the newly proposed hybrid (SVM-HAR) models produced higher predicting ability than the classical HAR model.