A Less Volatile Value-At-Risk Estimation Under A Semi-Parametric Approach

Asia-Pacific Journal of Financial Studies (2022)

36 Pages Posted: 3 May 2022

See all articles by Shih-Feng Huang

Shih-Feng Huang

affiliation not provided to SSRN

David K. Wang

National University of Kaohsiung

Abstract

In this paper, we propose a two-step less volatile value-at-risk (LVaR) estimation using the generalized nearly-isotonic regression (GNIR) model. The first step of our LVaR estimation is to produce a VaR sequence under the generalized autoregressive conditional heteroskedasticity (GARCH) framework. The second step is to adjust the VaR sequence by GNIR, and the generated estimate is denoted as LVaR. Our empirical tests based on the daily indices of 20 global stock markets suggest that LVaR outperforms other VaR estimates under the classic equally-weighted and exponentially-weighted moving-average frameworks. Furthermore, we show that LVaR is not only less volatile, but also performs reasonably well in various backtests.

Keywords: Data sequence; Fluctuation reduction; Generalized nearly-isotonic regression; Value-at-risk

JEL Classification: C14; C32; C53

Suggested Citation

Huang, Shih-Feng and Wang, David K., A Less Volatile Value-At-Risk Estimation Under A Semi-Parametric Approach. Asia-Pacific Journal of Financial Studies (2022), Available at SSRN: https://ssrn.com/abstract=4095878

Shih-Feng Huang

affiliation not provided to SSRN

David K. Wang (Contact Author)

National University of Kaohsiung ( email )

Kaohsiung, 811
Taiwan

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