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高维因子随机波动模型中的杠杆效应、非对称性与厚尾AI 解读

We develop a factor stochastic volatility model that incorporates leverage effects, return asymmetry, and heavy tails across all systematic and idiosyncratic model components. Our model leads to a flexible high-dimensional dependence structure that allows for time-varying correlations, tail dependence, and volatility response to both systematic and idiosyncratic return shocks. We develop an efficient Markov chain Monte Carlo algorithm for posterior estimation based on the…

粒子高效重要性抽样AI 解读

The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integrands to build variance minimising importance samplers. Despite a number of successful applications in high dimensions, it is well known that importance sampling strategies are subject to an exponential growth in variance as the dimension of the integration increases. We solve this problem by…

非线性非高斯状态空间模型的数值加速重要性抽样AI 解读

We propose a general likelihood evaluation method for nonlinear non-Gaussian state-space models using the simulation-based method of efficient importance sampling. We minimize the simulation effort by replacing some key steps of the likelihood estimation procedure by numerical integration. We refer to this method as numerically accelerated importance sampling. We show that the likelihood function for models with a high-dimensional state vector and a low-dimensional signal…