Dynamic factor, leverage and realized covariances in multivariate stochastic volatility
针对多元日股票收益随机波动率模型中参数估计随维度增加而不稳定的问题，引入市场因子对应的股指和基于高频数据的已实现协方差矩阵，构建含杠杆效应的动态因子模型，应用于标普500指数ETF的10只成分股，在投资组合绩效上表现出稳定优势。
In the stochastic volatility models for multivariate daily stock returns, it has been found that the estimates of parameters become unstable as the dimension of returns increases. To solve this problem, we focus on the factor structure of multiple returns and consider two additional sources of information: first, the stock index associated with the market factor and, second, the realized covariance matrix calculated from high-frequency data. The proposed dynamic factor model with the leverage effect and realized measures is applied to 10 top stocks composing the exchange traded fund linked with the investment return of the S&P 500 index and the model is shown to have a stable advantage in portfolio performance.