Abstract
Abstract We investigate the realised volatility (RV) forecasts for the short, mid, and long term by developing the HAR models with Bayesian approaches and employing the high-frequency data of the China Stock Index 300 (CSI300) future for the period from 16 April 2010 to 21 May 2014. We also evaluate the performances of competing models for both in-sample forecasts and out-of-sample forecasts. We find that the proposed HAR-type models with Bayesian approaches capture the time-varying properties of parameters and predictor sets. We also find that the HAR-type models with Bayesian approaches have superior forecast performance for both in-sample forecasts and out-of-sample forecasts as compared with the benchmark HAR-type models.
Highlights
Accurate forecast of volatility is central for asset pricing, portfolio selection, and risk management
Patton and Sheppard (2015) construct the heterogeneous autoregressive regression (HAR)-ΔJ model by incorporating a signed jump variation in the HAR model, where the realised volatility (RV) is decomposed into a good volatility and a bad volatility so that the leverage effect in volatility forecasts can be taken into account
To capture the time-varying properties of parameters and predictor sets, we develop HAR-type models with the Bayesian approaches and use the proposed models to forecast the RV of stock index futures for one-step and multi-step ahead forecasts
Summary
Accurate forecast of volatility is central for asset pricing, portfolio selection, and risk management. Bandi and Renò (2012) provide a nonparametric estimation in the continuous-time stochastic volatility model with both jumps in returns and variance, making it feasible to identify the time-varying leverage effects in the HAR model. To capture the time-varying properties of parameters and predictor sets, we develop HAR-type models with the Bayesian approaches and use the proposed models to forecast the RV of stock index futures for one-step and multi-step ahead forecasts. The remainder of this paper is organised as follows: section II develops the HAR-type models with Bayesian approaches; section III describes the high-frequency data and statistics; section IV presents the in-sample forecast and out-of-sample forecast results; and section V concludes the paper
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