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Bayesian inference for CoVaR

Author

Listed:
  • Mauro Bernardi
  • Ghislaine Gayraud
  • Lea Petrella
Abstract
Recent financial disasters emphasised the need to investigate the consequence associated with the tail co-movements among institutions; episodes of contagion are frequently observed and increase the probability of large losses affecting market participants' risk capital. Commonly used risk management tools fail to account for potential spillover effects among institutions because they provide individual risk assessment. We contribute to analyse the interdependence effects of extreme events providing an estimation tool for evaluating the Conditional Value-at-Risk (CoVaR) defined as the Value-at-Risk of an institution conditioned on another institution being under distress. In particular, our approach relies on Bayesian quantile regression framework. We propose a Markov chain Monte Carlo algorithm exploiting the Asymmetric Laplace distribution and its representation as a location-scale mixture of Normals. Moreover, since risk measures are usually evaluated on time series data and returns typically change over time, we extend the CoVaR model to account for the dynamics of the tail behaviour. Application on U.S. companies belonging to different sectors of the Standard and Poor's Composite Index (S&P500) is considered to evaluate the marginal contribution to the overall systemic risk of each individual institution

Suggested Citation

  • Mauro Bernardi & Ghislaine Gayraud & Lea Petrella, 2013. "Bayesian inference for CoVaR," Papers 1306.2834, arXiv.org, revised Nov 2013.
    • Handle: RePEc:arx:papers:1306.2834
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Markus Brunnermeier & Simon Rother & Isabel Schnabel & Itay Goldstein, 2020. "Asset Price Bubbles and Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 33(9), pages 4272-4317.
    3. Ayoub Kyoud & Cherif El Msiyah & Jaouad Madkour, 2023. "Modelling Systemic Risk in Morocco’s Banking System," IJFS, MDPI, vol. 11(2), pages 1-16, May.
    4. Gabriela Anghelache & Dumitru-Cristian Oanea, 2014. "Main Romanian Commercial Banks’ Systemic Risk during Financial Crisis: a CoVar Approach," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 6(2), pages 069-080, December.
    5. Marina Resta, 2016. "VaRSOM: A Tool to Monitor Markets' Stability Based on Value at Risk and Self‐Organizing Maps," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 47-64, January.
    6. Bernardi, Mauro & Bignozzi, Valeria & Petrella, Lea, 2017. "On the Lp-quantiles for the Student t distribution," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 77-83.

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