高级搜索
ISSN 1008-2204
CN 11-3979/C

社保基金投资组合的动态风险测度研究

江红莉, 何建敏, 李超杰

江红莉, 何建敏, 李超杰. 社保基金投资组合的动态风险测度研究[J]. 北京航空航天大学学报社会科学版, 2012, 25(3): 90-94.
引用本文: 江红莉, 何建敏, 李超杰. 社保基金投资组合的动态风险测度研究[J]. 北京航空航天大学学报社会科学版, 2012, 25(3): 90-94.
shu
Jiang Hongli, He Jianmin, Li Chaojie. Dynamic Risk Measurement of the Portfolio of Social Security Fund[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2012, 25(3): 90-94.
Citation: Jiang Hongli, He Jianmin, Li Chaojie. Dynamic Risk Measurement of the Portfolio of Social Security Fund[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2012, 25(3): 90-94.
shu

社保基金投资组合的动态风险测度研究

  • 1.

    东南大学 经管学院, 江苏 南京 211189


  • 2. 河海大学 商学院, 江苏 南京 211189

基金项目: 国家自然科学基金资助项目(71071034)
详细信息
    作者简介:

    江红莉(1982-),女,湖北随州人,博士研究生,研究方向为风险管理.

  • 中图分类号: F840.32

Dynamic Risk Measurement of the Portfolio of Social Security Fund

  • 1.

    School of Economics and Management, Southeast University, Nanjing 211189, China


  • 2. Business School, Hohai University, Nanjing 211189, China

摘要

    摘要
  • 摘要: 考虑到现阶段社保基金主要投资于股票和债券,以沪深300指数和国债指数构建投资组合代表社保基金投资组合,测度社保基金投资组合的动态风险。首先,基于FIGARCH模型对边缘分布建模;然后,基于时变Copula研究资产间的动态相关性,发现沪深300指数和国债指数间的相关性具有较强的持续性;最后采用Monte Carlo预测投资组合的VaR进行Kupiec检验。比较研究发现,基于时变模型的VaR预测效果要优于非时变模型的预测效果,时变FIGARCH-Copula-T和DCC(Dynamic Conditional Correlation)-MFIGARC-T模型都能准确地测度动态风险,但前者预测的失败率要低于后者。
    Abstract: Taking into account the current social security fund primarily invested in stocks and bonds, we construct new portfolio on behalf of the portfolio of social security fund by using the CSI 300 Index and Bond Index, in order to measure the dynamic risk of social security fund. Firstly, we model the marginal distribution by FIGARCH model. Secondly, we research the dynamic correlation of the assets in the portfolio based on time-varying Copula, finding that the correlations of CSI 300 Index and Bond Index are heavily persistent. Thirdly, we predict VaR of the portfolio by using the method of Mont Carlo, and carry out the Kupiec test. By comparative study, we find that time-varying model is more effective than constant model on the aspect of VaR prediction; both time-varying FIGARCH-Copula-T model and DCC (Dynamic Conditional Correlation)-MFIGARC-T model can correctly measure the dynamic risk, but the former is better than the latter.
    • HTML全文
    • 参考文献(16)
  • [1] Embrechts P, Mcneil A, Straumann D. Risk management: value at risk and beyond[M]. Cambridge: Cambridge University Press, 1999:176-223.
    [2] Embrechts P, Hoeing A, Juri A. Using copula to bound the value-at-Risk for functions of dependent risks[J]. Finance and Stochastics,2003(7):145-167.
    [3] Wang Z R, Chen X H, Jin Y B, et al. Estimating risk of foreign exchange portfolio: using var and cvar based on garch-evt-copula model [J]. Physica A, 2010, 389(21):4918-4928.
    [4] Patton A J. Modeling time-varying exchange rate dependence using the conditional copula . California: Department of Economics, University of California, 2001.
    [5] Patton A J. Modeling asymmetric exchange rate dependence[J]. International Economic Review,2006,47(2):527-556.
    [6] Mendes B V M. Computing conditional var using time-varying copulas[J]. Brazilian Review of Finance, 2005,3(2):251-265.
    [7] Goorbergha R W J, Genest C, Werke B J M. Multivariate option pricing using dynamic copula models . .
    [8] Goorbergha R W J, Genest C, Werke B J M. Bivariate option pricing using dynamic copula models[J]Insurance: Mathematics and Economics,2005,37(1):101-114.
    [9] Zhang J, Guegan D. Pricing bivariate option under garch processes with time-varying copula[J]. Insurance: Mathematics and Economics, 2008,42(3):1095-1103.
    [10] 罗付岩,邓光明. 基于时变Copula的VaR估计[J]. 系统工程,2007,25(8):28-33.
    [11] 周好文,晏富贵. 基于时变Copula的基金、股票和国债动态尾部相关性分析[J]. 西安交通大学学报:社会科学版,2010,30(4):21-26.
    [12] 崔玉杰,李从珠. 风险管理技术(VaR)在养老保险基金管理中的运用[J]. 数理统计与管理,2003,22(4):18-23.
    [13] 刘子兰,严明. 全国社会保障基金投资风险管理研究[J]. 当代经济研究,2006(8):64-68.
    [14] Baillie R T, Bollerslev T, Mikkelsen H O. Fractionally integrated generalized autoregressive conditional heteroske dasticity[J]. Journal of Econometrics, 1996, 74(1): 3-30.
    [15] Bollerslev T. Modelling the coherence in short-run nominal exchange rates: a multivariate generalized arch model[J]. The Review of Economics and Statistics,1990,72(3):498-505.
    [16] Engle R. Dynamic conditional correlation-A simple class of multivariate GARCH models[J]. Journal of Business and Economic Statistics,2002, 20(3).
    • 相关文章
    • 施引文献
    • 资源附件(0)
计量
  • 文章访问数:  1037
  • HTML全文浏览量:  0
  • PDF下载量:  737
  • 被引次数: 0
出版历程
  • 收稿日期:  2011-03-03
  • 发布日期:  2012-05-24

目录

摘要
HTML全文
    参考文献(16)
    相关文章
    施引文献
    资源附件(0)

    /

    返回文章
    返回
    版权所有 © 《北京航空航天大学学报（社会科学版）》编辑部
    电话: 010-82338013E-mail: bhskxb@buaa.edu.cn邮编：100083

    本系统由 北京仁和汇智信息技术有限公司 开发  百度统计