Zargar, F. N., & Kumar, D. (2020). Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis. International Review of Economics & Finance67, 25-41.

Abstract:

Based on the heterogeneous autoregressive (HAR) model, the study proposes the frameworks (HAR-AddRS and HAR-AddRS-J) to incorporate the impact of heterogeneity and volatility jumps in modeling the AddRS volatility estimator (Kumar & Maheswaran, 2014a, 2014b). The forecasting performance of the HAR-AddRS and HAR-AddRS-J models in comparison to the returns-based conditional volatility models is evaluated using the error statistic approach, Hansen (2005) superior predictive ability (SPA) test, and Hansen, Lunde et al. (2011) model confidence set (MCS) approach. The findings suggest that more accurate forecasts of daily volatility are obtained based on the HAR-AddRS and HAR-AddRS-J models than based on the returns-based conditional volatility models. The economic significance analysis results show that a substantial economic gain is achieved when the volatility forecasts based on the HAR-AddRS-J model are used to implement the trading strategies, however, the same is not true when the volatility forecasts are based on the returns-based conditional volatility models.


URL: https://doi.org/10.1016/j.iref.2019.12.011