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Bayesian Assessment of Dynamic Quantile Forecasts

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  • Richard Gerlach
  • Cathy W. S. Chen
  • Edward M. H. Lin

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.
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Suggested Citation

  • Richard Gerlach & Cathy W. S. Chen & Edward M. H. Lin, 2016. "Bayesian Assessment of Dynamic Quantile Forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(8), pages 751-764, December.
    • Handle: RePEc:wly:jforec:v:35:y:2016:i:8:p:751-764
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    Cited by:

    1. Rangika Peiris & Minh-Ngoc Tran & Chao Wang & Richard Gerlach, 2024. "Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model," Papers 2408.13588, arXiv.org.
    2. Tomohiro Ando & Jushan Bai, 2020. "Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 266-279, January.

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