得分驱动指数加权移动平均线和风险价值预测

A. Lucas, Xin Zhang
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引用次数: 69

摘要

提出了一种简单的方法来建模波动率和其他高阶矩的时间变化,使用类似于熟悉的RiskMetrics方法的递归更新方案。我们使用预测分布的分数来更新参数。这允许参数动态自动适应任何非正态数据特征,并鲁棒后续估计。新方法包含了指数加权移动平均(EWMA)方案的几个早期扩展。此外,它可以很容易地扩展到更高的维度和替代预测分布。该方法适用于(偏斜)学生t分布和时变自由度和/或偏度参数的风险值预测。结果表明,该方法在预测个股收益波动率和汇率收益波动率方面优于或具有竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Score Driven Exponentially Weighted Moving Averages and Value-at-Risk Forecasting
A simple methodology is presented for modeling time variation in volatilities and other higher order moments using a recursive updating scheme similar to the familiar RiskMetrics approach. We update parameters using the score of the forecasting distribution. This allows the parameter dynamics to adapt automatically to any non-normal data features and robustifies the subsequent estimates. The new approach nests several of the earlier extensions to the exponentially weighted moving average (EWMA) scheme. In addition, it can easily be extended to higher dimensions and alternative forecasting distributions. The method is applied to Value-at-Risk forecasting with (skewed) Student's t distributions and a time-varying degrees of freedom and/or skewness parameter. We show that the new method is competitive to or better than earlier methods in forecasting volatility of individual stock returns and exchange rate returns.
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