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Applying a Bayesian Network to VaR Calculations

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  • Emma Apps

Abstract

This paper focuses considers a methodology for deriving stock returns and VaR through the application of a Bayesian Network (BN). A network map is specified where the returns for three stocks are deemed to be conditionally dependent on two factors. The latter are defined having previously considered literature relating to the financial crisis and risk contagion. Subsequently, two factors are identified as influencing the individual stock returns – one relating to liquidity and the other relating to the market. Following application of the Gaussian Bayesian Network, regressions generate models for the said returns. The latter are then used to simulate time series of stock returns and those outcomes are compared to the original data series. The BN specification is found to be a satisfactory alternative for the modelling of stock returns. Furthermore, the resulting quantiles are shown to be more prudent estimates in relation to VaR calculations at the 5% level and, therefore, can result in increases in regulatory capital.

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  • Emma Apps, 2020. "Applying a Bayesian Network to VaR Calculations," Working Papers 202024, University of Liverpool, Department of Economics.
    • Handle: RePEc:liv:livedp:202024
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    References listed on IDEAS

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