Bayesian Time-Varying Quantile Forecasting for Value-at-Risk in Financial Markets

Richard Gerlach · Cathy W. S. Chen · Nancy Y. C. Chan

125 citations0/0 files storedJournal of Business and Economic Statistics

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Financial Risk and Volatility Modeling Forecasting Techniques and Applications Risk and Portfolio Optimization

Abstract

Recently, advances in time-varying quantile modeling have proven effective in financial Value-at-Risk forecasting. Some well-known dynamic conditional autoregressive quantile models are generalized to a fully nonlinear family. The Bayesian solution to the general quantile regression problem, via the Skewed-Laplace distribution, is adapted and designed for parameter estimation in this model family via an adaptive Markov chain Monte Carlo sampling scheme. A simulation study illustrates favorable precision in estimation, compared to the standard numerical optimization method. The proposed model family is clearly favored in an empirical study of 10 major stock markets. The results that show the proposed model is more accurate at Value-at-Risk forecasting over a two-year period, when compared to a range of existing alternative models and methods.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 1998Evaluating Interval Forecasts 1995Techniques for Verifying the Accuracy of Risk Measurement Models 1994Threshold heteroskedastic models 2004CAViaR

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