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近几年来,风险价值(VaR)已成为金融市场风险度量及风险管理的标准工具.文章用周期广义自回归条件异方差(GARCH)模型拟合金融市场数据,并应用分位回归方法得到此模型参数及条件VaR的估计,在一定条件下估计具有强相合性及渐近正态性,蒙特卡罗模拟结果表明此方法具有稳健性,且对于条件VaR的预测具有很高的准确性,沪深300指数的实证分析结果表明此方法关于VaR的预测具有非常好的效果.
In recent years, VaR has become the standard instrument of risk measurement and risk management in financial markets.This paper uses the generalized autoregressive conditional heteroskedasticity (GARCH) model to fit the financial market data and uses the quantile regression method to get this model The estimation of parameters and condition VaR under certain conditions has strong consistency and asymptotic normality. The results of Monte Carlo simulation show that the proposed method is robust and the prediction of conditional VaR is of high accuracy. The empirical analysis of the 300 index shows that this method has a very good effect on the prediction of VaR.