In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is first predicted using the HAR model, and the...

Determining which variables afect price realized volatility has always been challenging. This paper proposes to explain how fnancial assets infuence realized volatility by developing an optimal day-to-day forecast. The methodological proposal is based on using the best econometric and machine...

This study classifies jumps into idiosyncratic jumps and co-jumps to quantitatively identify systematic risk and idiosyncratic risk by utilizing high-frequency data. We found that systematic risk occurs more frequently and has larger magnitudes than the idiosyncratic risk in an individual asset,...

This paper investigates the role of investor attention in forecasting realized volatility for fourteen international stock markets, by means of Google Trends data, over the sample period January 2004 through November 2021. We devise an augmented Empirical Similarity model that combines three...

This paper investigates the role of investor attention in forecasting realized volatility for fourteen international stock markets, by means of Google Trends data, over the sample period January 2004 through November 2021. We devise an augmented Empirical Similarity model that combines three...

This study classifies jumps into idiosyncratic jumps and co-jumps to quantitatively identify systematic risk and idiosyncratic risk by utilizing high-frequency data. We found that systematic risk occurs more frequently and has larger magnitudes than the idiosyncratic risk in an individual asset,...

This study analyzes the importance of the Tokyo Stock Exchange Co-Location dataset (TSE Co-Location dataset) to forecast the realized volatility (RV) of Tokyo stock price index futures. The heterogeneous autoregressive (HAR) model is a popular linear regression model used to forecast RV. This...

This study analyzes the importance of the Tokyo Stock Exchange Co-Location dataset (TSE Co-Location dataset) to forecast the realized volatility (RV) of Tokyo stock price index futures. The heterogeneous autoregressive (HAR) model is a popular linear regression model used to forecast RV. This...