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

Rong Xu and Xingye Li
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Rong Xu: University of Shanghai for Science and Technology, Shanghai, China
Xingye Li: University of Shanghai for Science and Technology, Shanghai, China

International Journal of Economics and Financial Issues, 2017, vol. 7, issue 6, 104-110

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.

Keywords: Minimum Value At Risk; Hedging Model; Decay Factor; Cornish-Fisher; Exponentially Weighted Moving Average -Generalized Autoregressive Conditional Heteroskedasticity (1; 1)-M Model (search for similar items in EconPapers)
JEL-codes: G11 (search for similar items in EconPapers)
Date: 2017
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