Application of Quantile Regression to Estimating Value at Risk of Foreign Exchange Portfolio
| Autor: | Zhong-Wei Ke, 柯中偉 |
|---|---|
| Rok vydání: | 2010 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 98 We applied quantile regression proposed by Koenker and Bassett (1978) to value at risk model in this study. After selecting the best foreign exchange portfolio by Markowitz''s portfolio theory, the JPY, USD, SGD and SAR was selected. We compared GARCH, tGARCH, EGARCH and multivariate CCC-GARCH in traditional variance-covariance method with quantile regression to estimating value at risk. And using two kinds of back-testing which includes Kupeic and Christofferson test the performance of value at risk models. Empirical results, VaR.tGARCH (1, 1) model worst estimated the daily foreign return of value at risk. Combining quantile regression with GARCH-type to calculate the value at risk of individual foreign exchange is very close, where the average is higher than VaR.GARCH-type. After back-testing, adding quantile regression indeed is able to make accurate estimation. It fully shows that without any assumption of distribution surely capture fat-tail, kurtosis and correlation of financial assets characteristics. The back-testing results of portfolio''s value at risk models show that the appropriate portfolio can actually reduce risk, while VaR.QR.CCC-GARCH model perform better than VaR.CCC-GARCH model in this foreign exchange portfolio. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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