Forecasting the realized volatility in the Chinese stock market: further evidence

ABSTRACT

In this study, the impact of noise and jump on the forecasting ability of volatility models with high-frequency data is investigated. A signed jump variation is added as an additional explanatory variable in the volatility equation according to the sign of return. These forecasting performances of models with jumps are compared with those without jumps. Being applied to the Chinese stock market, we find that the jump variation has a significant in-sample predictive power to volatility and the predictive power of the negative one is greater than the positive one. Furthermore, out-of-sample evidence based on the fresh model confidence set (MCS) test indicates that the incorporation of singed jumps in volatility models can significantly improve their forecasting ability. In particular, among the realized variance (RV)-based volatility models and generalized autoregressive conditional heteroscedasticity (GARCH) class models, the heterogeneous autoregressive model of realized volatility (HAR-RV) model with the jump test and a decomposed signed jump variation have better out-of-sample forecasting performance. Finally, the use of the decomposed signed jump variations in predictive regressions can improve the economic value of realized volatility forecasts.

KEYWORDS:

• High-frequency data
• noise
• jump test
• signed jump variation
• MCS test

JEL CLASSIFICATION:

• C22
• C52
• C53

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ See http://data.worldbank.org

² Liu, Patton, and Sheppard (2012) point out that ‘it is necessary to use a lead (or a lag) of the proxy to “break” the dependence between the estimation error in the realized measure under analysis and the estimation error in the proxy. We use a one-day lead’.

³ This result also can be found in references such as Andersen, Bollerslev, and Diebold (2007) and Corsi, Pirino, and Renò (2010).

⁴ For more details about the bootstrap methods see Hansen, Lunde, and Nason (2011).

⁵ See more details about the realized kernel in Barndorff-Nielsen et al. (2008).

⁶ Some papers such as Fleming, Kirby, and Ostdiek (2001, 2003), Guidolin and Timmermann (2005, 2007) also provide some other settings on how to evaluate the economic value of volatility forecasts. They all use volatility forecasts as the key determinant of portfolio optimization by maximizing investor utility and then assess the performances of portfolios formed by volatility forecasts. Therefore, our strategy is intrinsically similar to their settings.

Additional information

Funding

The authors are grateful for the financial support from the National Natural Science Foundation of China [grant numbers 71371157, 71372109 and 71401077], the Humanities and Social Science Fund of Ministry of Education [grant number 14YJC790073] and the Young Scholar Fund of Science and Technology Department of Sichuan province [grant number 15QNJJ0032].