Bayesian Time-Varying Quantile Forecasting for Value-at-Risk in Financial Markets
将动态条件自回归分位数模型推广到非线性族，用贝叶斯方法结合自适应MCMC估计参数，在10个主要股票市场实证中比现有模型更准确地预测了两年期的风险价值。
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.