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Cross-sectional quantile regression for estimating conditional VaR of returns during periods of high volatility

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  • Vidal-Llana, Xenxo
  • Guillén, Montserrat

Abstract

Evaluating value at risk (VaR) for a firm’s returns during periods of financial turmoil is a challenging task because of the high volatility in the market. We propose estimating conditional VaR and expected shortfall (ES) for a given firm’s returns using quantile regression with cross-sectional (CSQR) data about other firms operating in the same market. An evaluation using US market data between 2000 and 2020 shows that our approach has certain advantages over a CAViaR model. Identification of low-risk firms and a reduction in computing times are additional advantages of the new method described.

Suggested Citation

  • Vidal-Llana, Xenxo & Guillén, Montserrat, 2022. "Cross-sectional quantile regression for estimating conditional VaR of returns during periods of high volatility," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    • Handle: RePEc:eee:ecofin:v:63:y:2022:i:c:s106294082200170x
    DOI: 10.1016/j.najef.2022.101835
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    More about this item

    Keywords

    Extreme values; Expected shortfall; Asset pricing; Risk management;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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