Less Volatile Value-at-Risk Estimation Under a Semi-parametric Approach*

IF 1.8 4区 经济学 Q2 BUSINESS, FINANCE
Shih-Feng Huang, David K. Wang
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引用次数: 1

Abstract

In this study, we propose a two-step, less-volatile value-at-risk (LVaR) estimation using a generalized nearly isotonic regression (GNIR) model. In the proposed approach, a VaR sequence is first produced under the generalized autoregressive conditional heteroskedasticity (GARCH) framework. Then, the VaR sequence is adjusted by GNIR, and the generated estimate is denoted as LVaR. The results of an empirical investigation show that LVaR outperformed other VaR estimates under the classic equally weighted and exponentially weighted moving-average frameworks. Furthermore, we show not only that LVaR is less volatile, but also that it performed reasonably well in various backtests.

半参数方法下的低波动风险值估计*
在这项研究中,我们提出了一种使用广义近似等渗回归(GNIR)模型的两步、波动较小的风险值(LVaR)估计。在所提出的方法中,首先在广义自回归条件异方差(GARCH)框架下产生VaR序列。然后,通过GNIR调整VaR序列,生成的估计值表示为LVaR。实证调查结果表明,在经典的等加权和指数加权移动平均框架下,LVaR优于其他VaR估计。此外,我们不仅表明LVaR的挥发性较小,而且它在各种回溯测试中表现得相当好。
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来源期刊
CiteScore
2.60
自引率
20.00%
发文量
36
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