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Estimating expected shortfall using a quantile function model

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  • Yuzhi Cai

Abstract

Distribution of financial returns defined by the existing GARCH models usually focus on the overall features such as the location, scale, skewness and kurtosis of the distribution. When using such GARCH models for expected shortfall (ES) estimation, it is difficult to consider specific information about the tails (such as the shape of the tails of the distribution), resulting in possible bias in ES estimation. We propose a quantile function threshold GARCH model to overcome some of the limitations of existing models. The model allows us to use the information including the skewness and tail shape of the distribution and the structure changes in the volatility of financial returns to obtain ES estimates. Our results show that the proposed model outperforms the benchmark models, confirming that tail shape of the distribution also plays an important role in ES estimation.

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  • Yuzhi Cai, 2021. "Estimating expected shortfall using a quantile function model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4332-4360, July.
    • Handle: RePEc:wly:ijfiec:v:26:y:2021:i:3:p:4332-4360
    DOI: 10.1002/ijfe.2017
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    Cited by:

    1. Yan Fang & Jian Li & Yinglin Liu & Yunfan Zhao, 2023. "Semiparametric estimation of expected shortfall and its application in finance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 835-851, July.

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