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首页> 外文期刊>Journal of industrial and management optimization >MODEL SELECTION BASED ON VALUE-AT-RISK BACKTESTING APPROACH FOR GARCH-TYPE MODELS
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MODEL SELECTION BASED ON VALUE-AT-RISK BACKTESTING APPROACH FOR GARCH-TYPE MODELS

机译：基于价值 - 风险反击型方法的模型选择GARCH型模型

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摘要

This paper aims to investigate the efficiency of the value-at-risk (VaR) backtests in the model selection from different types of generalised autoregressive conditional heteroskedasticity (GARCH) models with skewed and non-skewed innovation distributions. Extensive simulation is carried out to compare the model selection based on VaR backtests and Akaike Information Criteria (AIC). When the model is given but the innovation distribution is one of the six selected distributions which may be skewed or non-skewed, the simulation results show that both AIC and the VaR backtests succeed in selecting the correct innovation distribution from the set of six distributions under consideration. This indicates that both AIC and the VaR backtests are able to distinguish between skewed and non-skewed distributions when the innovation distribution is misspecified. Using an empirical data from NASDAQ index, we observe that the selected combination of model and innovation distribution based on the smallest AIC does not agree with that selected by using the in-sample VaR backtests. Examination of confidence limits for VaR and the expected shortfall forecasts under various loss functions provides evidence that the selected combination of model and innovation distribution using the VaR backtests tends to possess smaller mean absolute percentage error and logarithmic loss.
机译：本文旨在探讨来自不同类型的广义自回归条件异质痉挛（GARCH）模型的模型选择中的价值 - 风险（VAR）反馈的效率，具有偏斜和非偏斜的创新分布。进行广泛的仿真以比较基于VAR反馈和Akaike信息标准（AIC）的模型选择。当给出模型但创新分布是可能是偏斜或不倾斜的六个选定分布之一，仿真结果表明，AIC和VAR反馈都成功选择了从六个分布的组中选择正确的创新分布考虑。这表明AIC和VAR反馈能够在遗漏创新分布时区分偏斜和非偏斜分布。使用来自NASDAQ索引的经验数据，我们观察到基于最小AIC的模型和创新分布的所选组合不同意通过使用IN-SAMPLE REAKTEST选择的。在各种损失职能下对VAR的置信范围和预期短缺预测的验证提供了证据表明，使用VAR反馈的模型和创新分布所选择的组合往往具有较小的平均绝对百分比误差和对数损耗。

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