Journal of Risk Model Validation

A study of China’s financial market risks in the context of Covid-19, based on a rolling generalized autoregressive score model using the asymmetric Laplace distribution

Guanghui Han, Panpan Liu, Yueqiang Zhang and Xiaobo Li

  • We employ asymmetric Laplacian distribution and GAS model to capture risk in financial markets under the COVID-19 pandemic.
  • Empirical analysis using industry representative index data from the Shanghai Stock Exchange is undertaken.
  • The advantages of VaR and ES based on GAS-ALD model in predicting volatility risk are demonstrated, with ES being more accurate in extreme cases.

In order to fully capture the degree of risk in the financial market, we use the asymmetric Laplace distribution (ALD) to describe the distribution characteristics of the financial rate of return in combination with a generalized autoregressive score (GAS) model. We apply the GAS-ALD model to time-varying-parameter rolling estimation of the risk measures value-at-risk (VaR) and expected shortfall (ES), with the aim of building a dynamic risk measurement model for the financial market during the Covid-19 pandemic. Data for representative industry indexes of the Shanghai Stock Exchange are selected for empirical analysis, and parametric and nonparametric methods are used for comparison and backtesting. The results show, first, that the GAS-ALD model has obvious advantages over other tested methods in estimating VaR and ES and predicting the volatility risk of the rate of return, with ES being more accurate at predicting risk in extreme cases than VaR. Second, the pandemic had a big impact on the raw materials and energy industries, but a smaller impact on the financial industry. Based on the research conclusions, recommendations are put forward for three areas: government, regulators and enterprises.

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