Bayesian Assessment of Dynamic Quantile Forecasts

Cathy W. S. Chen (), Richard Gerlach and Edward Lin ()

No 2014-04, Working Papers from University of Sydney Business School, Discipline of Business Analytics

Abstract: Methods for Bayesian testing and assessment of dynamic quantile forecasts are proposed. Specifically, Bayes factor analogues of popular frequentist tests for independence of violations from, and for correct coverage of a time series of, quantile forecasts are developed. To evaluate the relevant marginal likelihoods involved, analytic integration methods are utilised when possible, otherwise multivariate adaptive quadrature methods are employed to estimate the required quantities. The usual Bayesian interval estimate for a proportion is also examined in this context. The size and power properties of the proposed methods are examined via a simulation study, illustrating favourable comparisons both overall and with their frequentist counterparts. An empirical study employs the proposed methods, in comparison with standard tests, to assess the adequacy of a range of forecasting models for Value at Risk (VaR) in several financial market data series.

Keywords: Bayesian Hypothesis testing; Bayes factor; asymmetric-Laplace distribution; Value-at-Risk; quantile regression (search for similar items in EconPapers)
Date: 2014-09-10
New Economics Papers: this item is included in nep-ecm, nep-for, nep-ger and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:syb:wpbsba:2123/11816

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