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Value at Risk Forecasting Based on Quantile Regression for GARCH Models
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 Title & Authors
Value at Risk Forecasting Based on Quantile Regression for GARCH Models
Lee, Sang-Yeol; Noh, Jung-Sik;
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 Abstract
Value-at-Risk(VaR) is an important part of risk management in the financial industry. This paper present a VaR forecasting for financial time series based on the quantile regression for GARCH models recently developed by Lee and Noh (2009). The proposed VaR forecasting features the direct conditional quantile estimation for GARCH models that is well connected with the model parameters. Empirical performance is measured by several backtesting procedures, and is reported in comparison with existing methods using sample quantiles.
 Keywords
Quantile regression;GARCH models;Value-at-Risk;
 Language
English
 Cited by
1.
Forecasting value-at-risk by encompassing CAViaR models via information criteria,;;

Journal of the Korean Data and Information Science Society, 2013. vol.24. 6, pp.1531-1541 crossref(new window)
1.
Forecasting value-at-risk by encompassing CAViaR models via information criteria, Journal of the Korean Data and Information Science Society, 2013, 24, 6, 1531  crossref(new windwow)
2.
Quantile Regression Estimator for GARCH Models, Scandinavian Journal of Statistics, 2013, 40, 1, 2  crossref(new windwow)
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