Estimation of Multivariate Asset Models with Jumps
提出一种两步估计法，用于估计基于Lévy过程的多维非高斯资产模型，可处理资产间依赖性和不同尾部行为，避免维度灾难，适用于大规模投资组合的风险管理，如计算在险价值。
We propose a consistent and computationally efficient 2-step methodology for the estimation of multidimensional non-Gaussian asset models built using Lévy processes. The proposed framework allows for dependence between assets and different tail behaviors and jump structures for each asset. Our procedure can be applied to portfolios with a large number of assets because it is immune to estimation dimensionality problems. Simulations show good finite sample properties and significant efficiency gains. This method is especially relevant for risk management purposes such as, for example, the computation of portfolio Value at Risk and intra-horizon Value at Risk, as we show in detail in an empirical illustration.