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- W3201834714 abstract "Under the framework of dynamic conditional score, we propose a parametric forecasting model for Value-at-Risk based on the normal inverse Gaussian distribution (Hereinafter NIG-DCS-VaR), which creatively incorporates intraday information into daily VaR forecast. NIG specifies an appropriate distribution to return and the semi-additivity of the NIG parameters makes it feasible to improve the estimation of daily return in light of intraday return, and thus the VaR can be explicitly obtained by calculating the quantile of the re-estimated distribution of daily return. We conducted an empirical analysis using two main indexes of the Chinese stock market, and a variety of backtesting approaches as well as the model confidence set approach prove that the VaR forecasts of NIG-DCS model generally gain an advantage over those of realized GARCH (RGARCH) models. Especially when the risk level is relatively high, NIG-DCS-VaR beats RGARCH-VaR in terms of coverage ability and independence." @default.
- W3201834714 created "2021-10-11" @default.
- W3201834714 creator A5001025785 @default.
- W3201834714 creator A5035466195 @default.
- W3201834714 date "2021-10-06" @default.
- W3201834714 modified "2023-09-27" @default.
- W3201834714 title "Value-at-Risk forecasting model based on normal inverse Gaussian distribution driven by dynamic conditional score" @default.
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- W3201834714 doi "https://doi.org/10.48550/arxiv.2110.02492" @default.
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