论文标题
预测具有基于Copula的模型的实现挥发性矩阵
Forecasting Realized Volatility Matrix With Copula-Based Models
论文作者
论文摘要
多元波动性建模和预测在金融经济学中至关重要。本文开发了一种基于Copula的方法，用于模型和预测实现的波动率矩阵。提出的基于COPULA的时间序列模型可以捕获已实现挥发性矩阵的隐藏依赖性结构。同样，这种方法可以通过cholesky分解或矩阵对数转换自动保证预测的正确定性。在本文中，我们考虑了多变量和双变量copulas。 Copulas的类型包括Student's T，Clayton和Gumbel Copulas。在经验应用中，我们发现，对于为期一日的波动矩阵预测，这些基于Copula的模型都可以在统计精度以及创造经济上含义的有效效率投资组合方面取得重要的性能。在我们考虑的Copulas中，多元T型副物在统计精度方面的表现更好，而双变量T型副物具有更好的经济性能。
Multivariate volatility modeling and forecasting are crucial in financial economics. This paper develops a copula-based approach to model and forecast realized volatility matrices. The proposed copula-based time series models can capture the hidden dependence structure of realized volatility matrices. Also, this approach can automatically guarantee the positive definiteness of the forecasts through either Cholesky decomposition or matrix logarithm transformation. In this paper we consider both multivariate and bivariate copulas; the types of copulas include Student's t, Clayton and Gumbel copulas. In an empirical application, we find that for one-day ahead volatility matrix forecasting, these copula-based models can achieve significant performance both in terms of statistical precision as well as creating economically mean-variance efficient portfolio. Among the copulas we considered, the multivariate-t copula performs better in statistical precision, while bivariate-t copula has better economical performance.
