School of Business, Jiangnan University, Wuxi, CHINA
2
School of Economics and management, Changsha University of Science and Technology, Changsha, CHINA
3
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, CHINA
4
School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Xiamen, CHINA
5
School of Business, Central South University, Changsha, CHINA
Online publication date: 2017-11-25
Publication date: 2017-11-25
EURASIA J. Math., Sci Tech. Ed 2017;13(12):8367-8382
This paper uses the downside realized semi variance to measure the downside risk and then the HAR-DR, HAR-DR-V and HAR-DR-DV models on the basis of the HAR-RV model are built. Finally, by comparing the three models’ prediction ability for downside risk in the stock spot market and futures market, we test whether the trading volume and downside trading volume of the two markets can be used to predict the downside risk. And we also study the differences under different samples and different models. The results indicate that trading volume and downside trading volume have different prediction effects for the downside risk in different periods. The trading volume and downside trading volume exhibit much forecasting power in the futures market. However, they show little forecasting power in the spot market.
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