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 realized 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 ten of the top stocks composing the exchange traded fund linked with the investment return of the SP500 index and the model is shown to have a stable advantage in portfolio performance.
翻译:在多变每日股票回报率的随机波动模型中,发现参数的估计数随着回报率的提高而变得不稳定。为解决这一问题,我们侧重于多重回报的因数结构,并考虑另外两个信息来源:第一,与市场因素相关的已实现股票指数,第二,从高频数据计算得出的已实现的共变矩阵。拟议的具有杠杆效应和已实现措施的动态要素模型,适用于构成交易所交易基金的10个顶级股票,这些股票与SP500指数的投资回报挂钩,该模型在投资组合业绩方面显示出稳定优势。