ASS  >> Vol. 6 No. 4 (April 2017)

    基于MRS-GARCH模型的期货铜价格波动的预测
    Prediction of Futures Copper PriceFluctuation Based on MRS-GARCH Model

  • 全文下载: PDF(2362KB) HTML   XML   PP.361-370   DOI: 10.12677/ASS.2017.64049  
  • 下载量: 258  浏览量: 335   国家自然科学基金支持

作者:  

李恩来,费 宇,孙小军,胡梦婷,莫玉莲:云南财经大学,统计与数学学院,云南 昆明

关键词:
MRS-GARCH模型波动率预测损失函数风险MRS-GARCH Model Volatility Forecast Loss Function Risk

摘要:

随着铜被广泛的使用,铜在我国国民经济的发展中起着重要的作用,然而,铜的价格波动却是十分频繁,这不但给铜的生产和销售环节带来了巨大的风险,而且对我国经济的平稳运行产生了巨大的冲击。因此,本文提出基于MRS-GARCH模型对铜的收益率波动进行研究,利用损失函数来比较MRS-GARCH模型、GARCH模型、EGARCH模型以及GJR-GARCH模型对铜的收益率波动预测表现的好坏。结果表明MRS-GARCH模型的预测效果在总体上要优于GARCH模型、EGARCH模型和GJR-GARCH模型。其中MRS-GARCH-N模型是所有MRS-GARCH模型中预测效果最好的模型。

Since copper is widely used, copper plays an important role in the development of our national economy. However, fluctuations in the price of copper is very frequent, which not only brings huge risk to the production and sales of copper, but also has a huge impact on the stable operation of China’s economy. Therefore, this paper proposes the MRS-GARCH model of the return volatility of copper based on study, compared to the MRS-GARCH model, GARCH model, EGARCH model and GJR-GARCH model for copper volatility forecasting performance by the quality loss function. The results show that the prediction effect of MRS-GARCH model is better than GARCH model, EGARCH model and GJR-GARCH model. The MRS-GARCH-N model is the best prediction model in all MRS-GARCH models.

文章引用:
李恩来, 费宇, 孙小军, 胡梦婷, 莫玉莲. 基于MRS-GARCH模型的期货铜价格波动的预测[J]. 社会科学前沿, 2017, 6(4): 361-370. https://doi.org/10.12677/ASS.2017.64049

参考文献

[1] Engle, R. (1982) Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50, 987-1007.
https://doi.org/10.2307/1912773
[2] Bollerslev, T. (1986) Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 21, 307- 328.
[3] Nelson, D.B. (1991) Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59, 347-370.
https://doi.org/10.2307/2938260
[4] Hamilton, J.D. (1989) A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57, 357-384.
https://doi.org/10.2307/1912559
[5] Hamilton, J.D. and Susmel, R. (1994) Autoregressive Conditional Heteroskedasticity and Change in Regime. Journal of Econometrics, 64, 307-333.
[6] Gray, S. (1996) Modeling the Conditional Distribution of Interest Rates as a Regime-Switching Process. Journal of Financial Econometrics, 42, 27-62.
[7] 孙金丽, 张世英. 具有结构转换的GARCH模型及其在中国股市中的应用[J]. 系统工程, 2003, 21(6): 86-91.
[8] 江孝感, 万蔚. 马尔科夫状态转换GARCH模型的波动持续性研究——对估计方法的探讨[J]. 数理统计与管理, 2008, 28(4): 637-645.
[9] 赵华, 蔡建文. 基于MRS-GARCH模型的中国股市波动率估计与预测[J]. 数理统计与管理, 2011, 30(5): 912-921.
[10] Glosten, L.R., Jagannathan, R. and Runkle, D.E. (1993) On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, 48, 1779-1801.
[11] Klaassen, F. (2002) Improving GARCH Volatility Forecasts. Empirical Economics, 27, 363-394.
https://doi.org/10.1007/s001810100100
[12] Lopez, J.A. (2002) Evaluating the Predictive Accuracy of Volatility Models. Journal of Finance, 20, 87-109.