Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search
提出一种贝叶斯随机搜索方法，用于选择多变量回归模型中的约束条件，其中误差项具有确定性或随机条件波动性。通过数值模拟和外汇汇率数据验证了该方法在识别真实模型约束和提升预测性能方面的有效性。
We propose a Bayesian stochastic search approach to selecting restrictions on multivariate regression models where the errors exhibit deterministic or stochastic conditional volatilities. We develop a Markov chain Monte Carlo (MCMC) algorithm that generates posterior restrictions on the regression coefficients and Cholesky decompositions of the covariance matrix of the errors. Numerical simulations with artificially generated data show that the proposed method is effective in selecting the data-generating model restrictions and improving the forecasting performance of the model. Applying the method to daily foreign exchange rate data, we conduct stochastic search on a VAR model with stochastic conditional volatilities.