| Summary: | 碩士 === 國立臺北大學 === 統計學系 === 100 === The fast growing of the theory of financial engineering and well developing of variety financial products in the financial market cause the evaluation of asset positions tends to diversification and complex, and the risk of access is increased as well. The major function of VaR (value at risk) is to measure the maximum loss of investment positions; therefore, how to assess VaR effectively is an important issue concerned by investors. It is already shown on many finance literatures that the distribution of assets return have the properties of autocorrelation and volatility clustering. Hence, models of GARCH (autocorrelation conditional heteroskedasticity) family, such as: ARMA (p, q)-GARCH (m, n) models are suggested to estimate the risk of assets return.
The purpose of this study is to establish the VaR of assets return of financial products. The major idea of this study is applying time-varying autoregressive model by Tesheng Hsiao (2008). This model differs from general autoregressive model on assuming that the constant term and lag coefficients are fixed and not change over time. This assumption seems inadequate for modeling the VaR of assets return of financial products. The technique of this study obtains time varying coefficients by using rolling estimation to estimate the coefficient of AR-GARCH model and then obtains a time varying model.
Finally, the Dow Jones and NASDAQ indices are illustrated for empirical analysis. And the penetration rate of 1%, 5% and 10% confidence level are calculated to assess the predictive ability.
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