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The impact of liquidity on portfolio value-at-risk forecasts

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  • Jui-Cheng Hung
  • Jung-Bin Su
  • Matthew C. Chang
  • Yi-Hsien Wang

Abstract

Historical crisis events have highlighted the insufficiency of Value-at-Risk (VaR) as a measure of market risk because such metric does not take liquidity into account. Unlike previous studies analyzing with only a single asset, we examine the impact of liquidity on computing VaR forecasts from a portfolio level. To this end, we use multivariate GARCH-t and GJR-GARCH-t models, as compared with univariate models, to seize the liquidity property embedded in individual stock returns and evaluate their accuracy and efficiency in computing VaR forecasts for portfolios with different liquidity levels.The empirical results indicate that computing portfolio VaR forecasts with multivariate models outperform the univariate models for full and subsample periods in terms of accuracy and efficiency evaluations, in particular for less-liquid portfolios. These results suggest the importance of liquidity in computing portfolio VaR forecasts. Ignorance of the impact of liquidity in computing portfolio VaR forecasts might result in inadequate coverage and insufficient market risk capital requirements.

Suggested Citation

  • Jui-Cheng Hung & Jung-Bin Su & Matthew C. Chang & Yi-Hsien Wang, 2020. "The impact of liquidity on portfolio value-at-risk forecasts," Applied Economics, Taylor & Francis Journals, vol. 52(3), pages 242-259, January.
    • Handle: RePEc:taf:applec:v:52:y:2020:i:3:p:242-259
    DOI: 10.1080/00036846.2019.1644442
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    Cited by:

    1. Qi Deng & Zhong-guo Zhou, 2024. "Liquidity Jump, Liquidity Diffusion, and Portfolio of Assets with Extreme Liquidity," Papers 2407.00813, arXiv.org.
    2. Hanxiao Wang & Huizi Ma, 2022. "Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China," Mathematics, MDPI, vol. 10(16), pages 1-18, August.

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