Abstract
Basing on the Heterogeneous Autoregressive with Continuous volatility and Jumps model (HAR-CJ), converting the realized Volatility (RV) into the adjusted realized volatility (ARV), and making use of the influence of momentum effect on the volatility, a new model called HAR-CJ-M is developed in this paper. At the same time, we also address, in great detail, another two models (HAR-ARV, HAR-CJ). The applications of these models to Chinese stock market show that each of the continuous sample path variation, momentum effect, and ARV has a good forecasting performance on the future ARV, while the discontinuous jump variation has a poor forecasting performance. Moreover, the HAR-CJ-M model shows obviously better forecasting performance than the other two models in forecasting the future volatility in Chinese stock market.
Highlights
Persistent volatility in financial markets is one of the most ubiquitous forms by which economic phenomena may be observed
By dealing with and calculating the above-mentioned 58751 data, we find that the overnight return variance rt2,n in Chinese stock market makes up 26.4% of the whole market volatility, namely, rt2,n/(RVt + rt2,n) equals 0.264
Combining the analyses in Sections 4.4.1 and 4.4.2, we can conclude that the forecasting performance of the above three volatility models of future volatility in Chinese stock market from the best to the weakest is in the following order: Heterogeneous Auto-Regressive with Continuous volatility and Jumps (HAR-CJ)-M model, HAR-adjusted realized volatility (ARV)-CJ model, and HARARV model
Summary
Persistent volatility in financial markets is one of the most ubiquitous forms by which economic phenomena may be observed. Since Andersen and Bollerslev [4] proposed RV, volatility models that take the high-frequency data as sample have developed rapidly and made great success in measuring and forecasting the volatility in financial markets. We propose in the perspective of Behavioral Finance Theory, add the momentum effect factor (the capital gain overhang) to the HAR-CJ model, consider the overnight return variance at the same time, convert RV into adjusted realized volatility (ARV), and set up the HAR-CJ-M model. We are to test the influence of momentum effect in Chinese stock market volatility; on the other hand, with the comparison of this new model with the HAR-ARV and HAR-CJ model on their volatility forecasting performance in Chinese stock market, it can help us find better models to measuring and forecasting volatility in Chinese stock market.
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