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A Comparative Performance of Conventional Methods for Estimating Market Risk Using Value at Risk

机译：使用风险值估算市场风险的传统方法的比较性能

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This paper presents a comparative evaluation of the predictive performance of conventional univariate VaR models including unconditional normal distribution model, exponentially weighted moving average (EWMA/RiskMetrics), Historical Simulation, Filtered Historical Simulation, GARCH-normal and GARCH Students t models in terms of their forecasting accuracy. The paper empirically determines the extent to which the aforementioned methods are reliable in estimating one-day ahead Value at Risk (VaR). The analysis is based on daily closing prices of the USD/KES exchange rates over the period starting January 03, 2003 to December 31, 2016. In order to assess the performance of the models, the rolling window of approximately four years (n=1000 days) is used for backtesting purposes. The backtesting analysis covers the sub-period from November 2008 to December 2016, consequently including the most volatile periods of the Kenyan shilling and the historical all-time high in September 2015. The empirical results demonstrate that GJR-GARCH-t approach and Filtered Historical Simulation method with GARCH volatility specification perform competitively accurate in estimating VaR forecasts for both standard and more extreme quantiles thereby generally out-performing all the other models under consideration.
机译：本文对常规单变量VaR模型的预测性能进行了比较评估，包括无条件正态分布模型，指数加权移动平均值（EWMA / RiskMetrics），历史模拟，滤波历史模拟，GARCH-normal和GARCH Students t模型预测准确性。本文根据经验确定上述方法在估计提前一天的风险价值（VaR）方面的可靠性。该分析基于2003年1月3日至2016年12月31日这段时间内USD / KES汇率的每日收盘价。为了评估模型的性能，滚动窗口大约为四年（n = 1000）天）用于回测。回溯分析涵盖了从2008年11月到2016年12月的子期间，因此包括肯尼亚先令的最不稳定时期和2015年9月的历史最高点。经验结果表明，GJR-GARCH-t方法和过滤的历史数据具有GARCH波动率指标的模拟方法在估计标准和更极端分位数的VaR预测方面具有竞争性的准确性，因此通常胜过所考虑的所有其他模型。

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