Welcome to Journal of Beijing Institute of Technology
Advance Search
Volume 18Issue 3
.
Turn off MathJax
Article Contents
Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2009> 18(3): 365-369
ZHOU Ying, ZHANG Hong-xi, WU Hui-shuo. Market Risk Evaluation on Single Futures Contract: SV-CVaR Model and Its Application on Cu00 Data[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2009, 18(3): 365-369.
Citation: ZHOU Ying, ZHANG Hong-xi, WU Hui-shuo. Market Risk Evaluation on Single Futures Contract: SV-CVaR Model and Its Application on Cu00 Data[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2009, 18(3): 365-369.
shu

Market Risk Evaluation on Single Futures Contract: SV-CVaR Model and Its Application on Cu00 Data

  • School of Management, Dalian University of Technology, Dalian 116024, China

  • Received Date:2008-07-20
    Abstract
  • A new stochastic volatility (SV) method to estimate the conditional value at risk (CVaR) is put forward. Firstly, it makes use of SV model to forecast the volatility of return. Secondly, the Markov chain Monte Carlo (MCMC) simulation and Gibbs sampling have been used to estimate the parameters in the SV model. Thirdly, in this model, CVaR calculation is immediate. In this way, the SV-CVaR model overcomes the drawbacks of the generalized autoregressive conditional heteroscedasticity value at risk (GARCH-VaR) model. Empirical study suggests that this model is better than GARCH-VaR model in this field.
    • FullText(HTML)
  • loading
    • References (9)
  • [1]
    Li Y Z, Zhou P, Xiao Z Y, et al. Application of VaR in futures market risk evaluation[J]. Journal of Central South University: Social Science, 2001,7(4):325-327. (in Chinese)
    [2]
    Engle R F. Autoregressive conditional heteroscedasticity with estimates of the variance of U.K[J]. Econometrics, 1982, 50: 987-1008.
    [3]
    Bollerslev T. Generalized autoregressive conditional heteroscedasticity[J]. Journal of Econometrics, 1986, 31: 307-327.
    [4]
    David J, Semper C, Clemente I M. Value at risk calculation through ARCH factor methodology: Proposal and comparative analysis[J]. European Journal of Operational Research, 2003, 150(3):516-528.
    [5]
    Barndorff-Nielsen O, Shephard N. Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics[J]. Journal of the Royal Statistical Society, 2001, Series B 63: 167-241.
    [6]
    Chi G T, Wang Y G, Yu F P, et al. Research on single futures contract market risk evaluation VaR-GARCH model and its application[J]. Journal of Dalian University of Technology, 2006, 46(1): 127-134. (in Chinese)
    [7]
    Eberlein E, Kallsen J, Kristen J. Risk management based on stochastic volatility[J]. Journal of Risk, 2003, 5(2):19-44.
    [8]
    Ai C, Chatrath A, Song F. A semi-parametric estimation of the optimal hedge ratio[J]. The Quarterly Review of Economics and Finance, 2007, 47(2):366-381.
    [9]
    Gerlach R, Tuyl F. MCMC methods for comparing stochastic volatility and GARCH models[J]. International Journal of Forecasting, 2006, 22: 91-107. (Edited by
    • Relative Articles
  • Supplements (0)
  • Cited By
  • 加载中

Catalog

    通讯作者:陈斌, bchen63@163.com
    • 1.

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (298) PDF downloads(46) Cited by()
    Proportional views
    Related

    Address:5 Zhongguancun South Street, Haidian District, Beijing

    Tel:86-10-68914374

    Email:blgywb@bit.edu.cn

    Copy Right © Journal of Beijing Institute of Technology

    Supported by:Beijing Renhe Information Technology Co. Ltd

    /

      Return
      Return
        Baidu
        map