Volatility specifications versus probability distributions in VaR forecasting

  1. Laura Garcia-Jorcano 1
  2. Alfonso Novales 2
  1. 1 Universidad de Castilla-La Mancha
    info

    Universidad de Castilla-La Mancha

    Ciudad Real, España

    ROR https://ror.org/05r78ng12

  2. 2 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Revista:
Documentos de Trabajo (ICAE)

ISSN: 2341-2356

Año de publicación: 2019

Número: 26

Páginas: 1-39

Tipo: Documento de Trabajo

DIALNET GOOGLE SCHOLAR lock_openDocta Complutense editor

Otras publicaciones en: Documentos de Trabajo (ICAE)

Resumen

We provide evidence suggesting that the assumption on the probability distribution for return innovations is more influential for Value at Risk (VaR) performance than the conditional volatility specification. We also show that some recently proposed asymmetric probability distributions and the APARCH and FGARCH volatility specifications beat more standard alternatives for VaR fore- casting, and they should be preferred when estimating tail risk. The flexibility of the free power parameter in conditional volatility in the APARCH and FGARCH models explains their better performance. Indeed, our estimates suggest that for a number of financial assets, the dynamics of volatility should be specified in terms of the conditional standarddeviation. Wedrawourresults on VaRforecastingperformance fromi) a variety of back testing approaches, ii) the Model Confidence Set approach, as well as iii) establishing a ranking among alternative VaR models using a precedence criterion that we introduceinthispaper.

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Información de financiación

The authors gratefully acknowledge financial support from the grants Ministerio de Economía y Competitividad ECO2015-67305-P, Generalitat Valenciana PrometeoII/2013/015, Programa de Ayudas a la Investigación en Macroeconomía, Economía Monetaria, Financiera y Bancaria e Historia Económica 2015-2016 from Banco de España.

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