Title: Volatility forecasting using the HAR and lasso-based models: An empirical investigation
Authors: Xingzhi Yao - Lancaster University (United Kingdom) [presenting]
Marwan Izzeldin - Lancaster University Management School (United Kingdom)
Abstract: The aim is to compare the performance of various least absolute shrinkage and selection operator (Lasso) based models in forecasting future log realized variance (RV) constructed from high-frequency returns. We conduct a comprehensive empirical study using the SPY and 10 individual stocks selected from 10 different sectors. In an in-sample analysis, we provide evidence for the invalidity of the lag structure implied by the heterogeneous autoregressive (HAR) model in volatility forecast. In our out-of-sample study, the best forecasting performance is usually provided by the Lasso-based model and the idea of forecast combination tends to improve the forecasting accuracy of the Lasso-based model. Among all models of interest, the ordered Lasso AR using the forecast combination serves as the top performer most frequently in forecasting RV and its improvements over the HAR model are, in most cases, significant over monthly horizons. In line with the existing study, the superiority of the Lasso-based models is more evident in a larger forecasting window size.