High‐dimensional sparse multivariate stochastic volatility models
针对多元随机波动率模型在高维情形下的维度诅咒问题，提出一种基于惩罚OLS的两步估计方法，并证明了渐近性质，通过模拟和金融数据验证了有效性。
Although multivariate stochastic volatility models usually produce more accurate forecasts compared with the MGARCH models, their estimation techniques such as Bayesian MCMC typically suffer from the curse of dimensionality. We propose a fast and efficient estimation approach for MSV based on a penalized OLS framework. Specifying the MSV model as a multivariate state‐space model, we carry out a two‐step penalized procedure. We provide the asymptotic properties of the two‐step estimator and the oracle property of the first‐step estimator when the number of parameters diverges. The performances of our method are illustrated through simulations and financial data. Supplementary Material presenting technical proofs is available online.