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|Title: ||Volatility forecasting and risk management|
|Other Titles: ||波動性預測與風險管理|
|Authors: ||劉洪鈞;Liu, Hung-chun|
|Keywords: ||波動性;風險值;厚尾;GARCH模型;新興市場;能源商品;Volatility;Value-at-Risk;Fat tails;GARCH model;Emerging markets;Energy commodities|
|Issue Date: ||2010-01-11 00:50:09 (UTC+8)|
The purpose of this dissertation is to contribute to the literature on volatility forecasting and its application to risk management (Value-at-Risk) which comprises three parts. The first part of the dissertation is entitled “Predicting the Volatility of Stock Indices in China using GARCH Models with Skewed-GED Distribution”, the second part is named “Modelling Risk for Energy Commodities via Fat-Tailed GARCH Models”, and the last one is “Daily Volatility Forecasts with Application to Risk Management: The Role of SGT Distribution in VaR Estimation”. A brief introduction of these three parts can be summarized as follows:
The first part investigates how specification of return distribution influences the out-of-sample volatility forecasting performance using GARCH-N and GARCH-SGED models. Illustrations of these techniques are presented for two main stock markets in China, the daily spot prices of Shanghai and Shenzhen composite stock indices, which are considered more interesting and attractive than that of general developed capital markets. Empirical results indicate that the GARCH-SGED model is superior to the GARCH-N model in forecasting China stock markets’ volatility, for alternative forecast horizons when model selection is based on MSE or MAE. Meanwhile, the DM-tests further confirm that volatility forecasts by the GARCH-SGED model are more accurate than those generated using the GARCH-N model in all cases, indicating the significance of both skewness and tail-thickness in the conditional distribution of returns, especially for the emerging financial markets.
In the second part, an analytical quantile-operator of the standard HT distribution (Politis, 2004) is derived which facilitates convenient in out-of-sample VaR estimation with HT distribution. In empirical application, we employ GARCH-N, GARCH-t and GARCH-HT models to estimate the one-day-ahead absolute VaR and compare their performance in risk management of competing models. Daily spot prices of WTI crude oil, Brent crude oil, heating oil #2, propane and Conventional Gasoline Regular are used as empirical data to compare the accuracy and efficiency of these VaR models. Empirical results suggest that for asset returns that exhibit leptokurtic and fat-tailed features, the VaR estimates generated by the GARCH-HT models have good accuracy at both low and high confidence levels. Moreover, MRSB measures indicate that the GARCH-HT model is more efficient than alternatives for most cases at high confidence levels. These findings suggest that the heavy-tailed distribution is more suitable for energy commodities, particularly VaR calculation.
The last part of my dissertation is to contribute to the literature by assessing market risk in the international crude oil market from the perspective of VaR analysis. A GARCH-SGT approach is thus proposed capable of coping with fat-tails, leptokurtosis and skewness using SGT returns innovations and catering for volatility clustering with the GARCH(1,1) model in modeling one-day-ahead VaR. This technique is illustrated using daily returns of West Texas Intermediate crude oil spot prices from December 2003 to December 2007. Empirical results indicate that the VaR forecast obtained by the GARCH-SGT model is superior to that of the GARCH-T and GARCH-GED models through a series of rigorous model selection criteria. Overall, the sophisticated SGT distributional assumption significantly benefits VaR forecasting for WTI crude oil returns at low and high confidence levels, indicating a need for VaR models that consider fat-tails, leptokurtosis and skewness behaviors. This makes the GARCH-SGT model be a robust forecasting approach which is practical to implement and regulate for VaR measurement.
|Appears in Collections:||[財務金融學系暨研究所] 學位論文|
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