IDEAS home Printed from https://ideas.repec.org/a/eco/journ1/2017-06-13.html
   My bibliography  Save this article

Study About the Minimum Value at Risk of Stock Index Futures Hedging Applying Exponentially Weighted Moving Average - Generalized Autoregressive Conditional Heteroskedasticity Model

Author

Listed:
  • Rong Xu

    (University of Shanghai for Science and Technology, Shanghai, China)

  • Xingye Li

    (University of Shanghai for Science and Technology, Shanghai, China)

Abstract

What investors often wish to insure is that the maximum possible loss of their portfolios falling below a certain value. Namely, the maximum possible loss that a portfolio will lose under normal market fluctuations, with a given confidence level, over a certain time horizon, it is known shortly as value at risk (VaR). However, when it comes to the hedging strategy taking in the derivative markets for the minimum VaR, many investors simply thinking it is a hedging ratio in one at beginning, then a lot of effective model came out from both academia and industry over the years.We pioneer deriving a combined and dynamic hedging model- exponentially weighted moving average-generalized autoregressive conditional heteroskedasticity (GARCH) (1,1)-M applicable to the real financial markets based on previous studies. The results in this paper turn out that the model we build is not only excellent for the pursuit for the minimum VaR but also practical for the actual situation where the variances of financial price data are time-varying.In this paper we calculate the optimal decay factor 0.93325 which is the best match to the Hu-Shen 300 stock index market, withdraw uniform 0.9400, and use the Cornish-Fisher function to correct the quantile of the normal distribution, get the final hedging ratios and the minimum VaR.

Suggested Citation

  • Rong Xu & Xingye Li, 2017. "Study About the Minimum Value at Risk of Stock Index Futures Hedging Applying Exponentially Weighted Moving Average - Generalized Autoregressive Conditional Heteroskedasticity Model," International Journal of Economics and Financial Issues, Econjournals, vol. 7(6), pages 104-110.
    • Handle: RePEc:eco:journ1:2017-06-13
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijefi/article/download/5630/pdf
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijefi/article/view/5630/pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Menelaos Karanasos, "undated". "Prediction in ARMA models with GARCH in Mean Effects," Discussion Papers 99/11, Department of Economics, University of York.
    2. Zhiguang Cao & Richard D.F. Harris & Jian Shen, 2010. "Hedging and value at risk: A semi‐parametric approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(8), pages 780-794, August.
    3. Menelaos Karanasos, 2001. "Prediction in ARMA Models with GARCH in Mean Effects," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(5), pages 555-576, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    2. Menelaos Karanasos & Alexandros Paraskevopoulos & Faek Menla Ali & Michail Karoglou & Stavroula Yfanti, 2014. "Modelling Returns and Volatilities During Financial Crises: a Time Varying Coefficient Approach," Papers 1403.7179, arXiv.org.
    3. M. Karanasos & J. Kim, 2003. "Moments of the ARMA--EGARCH model," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 146-166, June.
    4. Karanasos, Menelaos & Paraskevopoulos,Alexandros & Canepa, Alessandra, 2020. "Unified Theory for the Large Family of Time Varying Models with Arma Representations: One Solution Fits All," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202008, University of Turin.
    5. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    6. Hlouskova, Jaroslava & Schmidheiny, Kurt & Wagner, Martin, 2009. "Multistep predictions for multivariate GARCH models: Closed form solution and the value for portfolio management," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 330-336, March.
    7. Carol Alexander & Emese Lazar & Silvia Stanescu, 2010. "Analytic Moments for GARCH Processes," ICMA Centre Discussion Papers in Finance icma-dp2011-07, Henley Business School, University of Reading, revised Apr 2011.
    8. Jaroslava Hlouskova & Kurt Schmidheiny & Martin Wagner, 2002. "Multistep Predictions from Multivariate ARMA-GARCH: Models and their Value for Portfolio Management," Diskussionsschriften dp0212, Universitaet Bern, Departement Volkswirtschaft.
    9. Haiyan Zhao & Fred Huffer & Xu-Feng Niu, 2015. "Time-varying coefficient models with ARMA-GARCH structures for longitudinal data analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(2), pages 309-326, February.
    10. repec:awi:wpaper:0473 is not listed on IDEAS
    11. Neil Kellard & Denise Osborn & Jerry Coakley & Christian Conrad & Menelaos Karanasos, 2015. "On the Transmission of Memory in Garch-in-Mean Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 706-720, September.
    12. Menelaos Karanasos, "undated". "The Covariance Structure of Mixed ARMA Models," Discussion Papers 00/11, Department of Economics, University of York.
    13. Marie Brière & Jean-David Fermanian & Hassan Malongo & Ombretta Signori, 2012. "Volatility Strategies for Global and Country Specific European Investors," Post-Print hal-01494509, HAL.
    14. Zheng, Chengli & Su, Kuangxi & Yao, Yinhong, 2021. "Hedging futures performance with denoising and noise-assisted strategies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    15. Huang, Jinbo & Ding, Ashley & Li, Yong & Lu, Dong, 2020. "Increasing the risk management effectiveness from higher accuracy: A novel non-parametric method," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    16. Stilianos Fountas & Menelaos Karanasos & Marika Karanassou, "undated". "A GARCH Model of Inflation and Inflation Uncertainty with Simultaneous Feedback," Discussion Papers 00/24, Department of Economics, University of York.
    17. Antonis Demos, 2002. "Moments and dynamic structure of a time-varying parameter stochastic volatility in mean model," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 345-357, June.
    18. Kuang, Wei, 2023. "The equity-oil hedge: A comparison between volatility and alternative risk frameworks," Energy, Elsevier, vol. 271(C).
    19. Menelaos Karanasos, "undated". "Some Exact Formulae for the Constant Correlation and Diagonal M - Garch Models," Discussion Papers 00/14, Department of Economics, University of York.
    20. Karanasos, Menelaos & Paraskevopoulos, Alexandros & Magdalinos, Anastasios & Canepa, Alessandra, 2024. "A Unified Theory for Arma Models with Varying Coefficients: One Solution Fits All," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202413, University of Turin.
    21. Barbi, Massimiliano & Romagnoli, Silvia, 2018. "Skewness, basis risk, and optimal futures demand," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 14-29.

    More about this item

    Keywords

    Minimum Value At Risk; Hedging Model; Decay Factor; Cornish-Fisher; Exponentially Weighted Moving Average -Generalized Autoregressive Conditional Heteroskedasticity (1; 1)-M Model;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eco:journ1:2017-06-13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.