Forecasting Volatility and Capturing Downside Risk in Financial Markets under the Subprime Mortgage Crisis

• Main Authors: Kao-Ying Chang, 張高瑩
• Other Authors: 邱建良
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/90258638164185318968

碩士 === 淡江大學 === 財務金融學系碩士班 === 98 === This thesis applies alternative GARCH-type models to daily volatility forecasting with Value-at-Risk (VaR) application to the Taiwanese stock index futures and Standard & Poor’s Depositary Receipts (SPDRs) that suffered the global financial tsunami that occurred during 2008. Instead of using squared returns as a proxy for true volatility, this thesis adopts four volatility proxy measures, the PK-range, GK-range, RS-range, and RV, for use in the empirical exercise. The volatility forecast evaluation is conducted with a variety of volatility proxies according to both symmetric and asymmetric types of loss functions regarding forecasting accuracy. These models are also evaluated in terms of their ability to provide adequate VaR estimates with the inclusion of realized-volatility-based VaR model. Moreover, the predictive performance of the RV-based VaR model is compared with various GARCH-based VaR models according to both unconditional coverage test (Kupiec,1995) and utility-based loss functions with respect to risk management practice. Empirical results indicate that the EGARCH model provides the most accurate daily volatility forecasts, whereas the performances of the standard GARCH model are relatively poor. Such evidence suggests that asymmetry in volatility dynamics should be taken into account for forecasting financial markets volatility. Moreover, I find a consistent result that the forecasting performance of models remains constant across various volatility proxies for both empirical data in most cases. In the area of risk management,the RV-VaR model tends to underestimate VaR and has been rejected for lacking correct unconditional coverage for the TAIFEX returns data, while the GARCH genre of models is capable of providing satisfactory and reliable daily VaR forecasts. In particular, the asymmetric EGARCH model is the most preferred. For SPDRs case, while all models have passed the back-test, the RV-VaR is considered the optimal VaR model both for a regulator and for a firm at alternative confidence levels during the whole year of 2008. The empirical findings presented here provide crucial implications for market practitioners, such as, policy makers, institutional risk managers, and common investors in risk management.