论文检索

随机波动率管理组合的灵活变分近似AI 解读

This paper investigates volatility-managed portfolios through a variational Bayes approach that smooths stochastic volatility forecasts. Our algorithm exhibits competitive performance relative to established methods both in terms of inferential accuracy and computational efficiency. Analyzing equity factors and characteristic-based portfolios, we show that smoothing reduces excess leverage and turnover, significantly improving risk-adjusted returns after transaction costs.…

振荡型非平稳时间序列的高效分段AI 解读

We propose a novel approach for change-point detection and parameter learning in multivariate non-stationary time series exhibiting oscillatory behaviour. We approximate the process through a piecewise function defined by a sum of sinusoidal functions with unknown frequencies and amplitudes plus noise. The inference for this model is non-trivial. However, discretising the parameter space allows us to recast this complex estimation problem into a more tractable linear model,…

大型贝叶斯向量自回归的变分推断AI 解读

We propose a novel variational Bayes approach to estimate high-dimensional vector autoregression (VAR) models with hierarchical shrinkage priors. Our approach does not rely on a conventional structural VAR representation of the parameter space for posterior inference. Instead, we elicit hierarchical shrinkage priors directly on the matrix of regression coefficients so that (1) the prior structure directly maps into posterior inference on the reduced-form transition matrix,…