因子波动及其相关矩阵的分离动态柔性观测因子模型
A flexible observed factor model with separate dynamics for the factor volatilities and their correlation matrix
摘要：
我们的文章考虑了一个具有观测因子的回归模型。观察到的因素有一个灵活的随机波动率结构，具有独立的动态波动率和相关矩阵。的相关矩阵的因素是随时间变化的，其演变是由一个逆Wishart过程。该模型灵活地描述了所观测到的波动率的演变，当观测的维数很高时，该模型特别有吸引力。提出了一种马尔可夫链蒙特卡罗算法来估计模型。利用该算法可以直接获得未来观测值的预测分布，并进行模型选择。该模型的说明和其他Wishart型因子多变量随机波动模型使用各种经验数据，包括每月的股票收益率和投资组合加权收益率进行比较。证据表明，我们的模型具有更好的预测性能。本文还允许特质误差遵循个体随机波动过程，以处理更易变的数据，如每日或每周的股票收益率。
英文摘要：
Our article considers a regression model with observed factors. The observed factors have a flexible stochastic volatility structure that has separate dynamics for the volatilities and the correlation matrix. The correlation matrix of the factors is time-varying and its evolution is described by an inverse Wishart process. The model specifies the evolution of the observed volatilities flexibly and is particularly attractive when the dimension of the observations is high. A Markov chain Monte Carlo algorithm is developed to estimate the model. It is straightforward to use this algorithm to obtain the predictive distributions of future observations and to carry out model selection. The model is illustrated and compared to other Wishart-type factor multivariate stochastic volatility models using various empirical data including monthly stock returns and portfolio weighted returns. The evidence suggests that our model has better predictive performance. The paper also allows the idiosyncratic errors to follow individual stochastic volatility processes in order to deal with more volatile data such as daily or weekly stock returns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
