Forecasting Return Volatility of the CSI 300 Index Using the Stochastic Volatility Model with Continuous Volatility and Jumps
Joint Authors
Li, Pu
He, Zhifang
Zhu, Ning
Gong, Xu
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
The logarithmic realized volatility is divided into the logarithmic continuous sample path variation and the logarithmic discontinuous jump variation on the basis of the SV-RV model in this paper, which constructs the stochastic volatility model with continuous volatility (SV-CJ model).
Then, we use high-frequency transaction data for five minutes of the CSI 300 stock index as the study sample, which, respectively, make parameter estimation on the SV, SV-RV, and SV-CJ model.
We also comparatively analyze these three models' prediction accuracy by using the loss functions and SPA test.
The results indicate that the prior logarithmic realized volatility and the logarithmic continuous sample path variation can be used to predict the future return volatility in China's stock market, while the logarithmic discontinuous jump variation is poor at its prediction accuracy.
Besides, the SV-CJ model has an obvious advantage over the SV and SV-RV model as to the prediction accuracy of the return volatility, and it is more suitable for the research concerning the problems of financial practice such as the financial risk management.
American Psychological Association (APA)
Gong, Xu& He, Zhifang& Li, Pu& Zhu, Ning. 2014. Forecasting Return Volatility of the CSI 300 Index Using the Stochastic Volatility Model with Continuous Volatility and Jumps. Discrete Dynamics in Nature and Society،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-511969
Modern Language Association (MLA)
Gong, Xu…[et al.]. Forecasting Return Volatility of the CSI 300 Index Using the Stochastic Volatility Model with Continuous Volatility and Jumps. Discrete Dynamics in Nature and Society No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-511969
American Medical Association (AMA)
Gong, Xu& He, Zhifang& Li, Pu& Zhu, Ning. Forecasting Return Volatility of the CSI 300 Index Using the Stochastic Volatility Model with Continuous Volatility and Jumps. Discrete Dynamics in Nature and Society. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-511969
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-511969