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Evaluation of Caviar Models Incorporated with Intraday Information ,the Case Study:Estimation Value at Risk of Gold

Karimi, Parvane | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44191 (44)
  4. University: Sharif University of Technology
  5. Department: Management and Economics
  6. Advisor(s): Barakchian, Mahdi
  7. Abstract:
  8. Value at risk (VaR) is the maximum loss of the asset portfolio at the specified confidence level and certain time horizon. This tool is used to measure market risk and also used as a basis in determining financial standards for international financial instituation. Conditional Autoregressive Value at Risk models or CAViaR models introduced by Engle and Manganelli (2004). This models calculate VaR base on quantile regession approach and show some promising performance properties.
    In order to propose a more accurate model for calcutating VaR , we develop CAViaR models by incorporating them with intraday information then we calculate VaR with this kind of models and CAViaR models that introduced by Engle and Manganelli and some common parametric and nonparametric models. We incorporate CAViaR models with intraday information by nonparametric estimators of daily price variability that exploi high frequency intraday informations , such as realised range , realised volatility, realised power variation, realised bipower variation. Our results show that CAViaR models incorporated with intraday information outperform the other models and are the most accurate model at 95 and 99 confidnce level
  9. Keywords:
  10. Quantile Regression ; High Frequency Data ; Value at Risk ; GAViaR Models ; Realized Volatility ; Realized Power Variation

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