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基于SV-SGED模型的动态VaR测度研究

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Keywords: VaR,SV模型,有偏广义误差分布,有效重要性抽样,极大似然估计

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Abstract:

?本文针对金融资产收益展现出“有偏”及“厚尾”分布特征,引入有偏广义误差分布(SGED)来描述资产收益,继而提出SV-SGED模型对资产收益波动率建模,并以此来测度动态风险值(VaR),进而采用后验测试技术对风险测度模型的精确性进行检验。同时,为了估计SV模型的参数,提出基于有效重要性抽样(EIS)技巧的极大似然(ML)估计方法。最后,给出了基于上证综合指数的实证研究。结果表明,SV-SGED模型比正态分布假定下的SV(SV-N)和广义误差分布假定下的SV(SV-GED)模型具有更好的波动率描述能力,SV-SGED模型展现出比SV-N和SV-GED模型更优越的风险测度能力。

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