论文标题
贝叶斯多元分位数回归，具有替代时变波动率规格
Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications
论文作者
论文摘要
本文提出了一种新型的贝叶斯多元分位数回归，以预测能量商品的尾部行为，在这种情况下，同性恋性假设放宽了，以允许时间变化。特别是，我们利用多元不对称拉式可能性的混合物表示以及量表矩阵的cholesky型分解来引入随机波动率和GARCH过程，然后提供有效的MCMC来估算它们。提出的模型的表现优于同性基准，主要是在预测分布的尾巴时。我们使用基于分位数分数的加权方案提供模型组合，这会导致改进的性能，尤其是当没有单个模型均匀地超过另一个模型跨分位数，时间或变量时。
This article proposes a novel Bayesian multivariate quantile regression to forecast the tail behavior of energy commodities, where the homoskedasticity assumption is relaxed to allow for time-varying volatility. In particular, we exploit the mixture representation of the multivariate asymmetric Laplace likelihood and the Cholesky-type decomposition of the scale matrix to introduce stochastic volatility and GARCH processes and then provide an efficient MCMC to estimate them. The proposed models outperform the homoskedastic benchmark mainly when predicting the distribution's tails. We provide a model combination using a quantile score-based weighting scheme, which leads to improved performances, notably when no single model uniformly outperforms the other across quantiles, time, or variables.
