High-dimensional time-varying coefficient estimation in diffusion models
提出一种基于高维伊藤扩散过程的时变系数估计方法TED，通过局部Dantzig选择与去偏处理得到无偏估计，实证中比基准模型有更高样本外R²，并发现行业因子对资产收益解释力强。
In this article, we develop a novel high-dimensional time-varying coefficient estimation method based on high-dimensional Itô diffusion processes. To account for high-dimensional time-varying coefficients, we first estimate local (or instantaneous) coefficients using a time-localized Dantzig selection scheme under a sparsity condition, which results in biased local coefficient estimators due to the regularization. To handle the bias, we propose a debiasing scheme, which provides well-performing unbiased local coefficient estimators. With the unbiased local coefficient estimators, we estimate the integrated coefficient, and to further account for the sparsity of the coefficient process, we apply thresholding schemes. We call this Thresholding dEbiased Dantzig (TED). We establish asymptotic properties of the proposed TED estimator. In the empirical analysis, TED achieves a higher average out-of-sample R2 across assets than benchmark estimators in most periods. Industry-related factors play a central role in explaining asset returns. The estimated integrated coefficients show pronounced time variation associated with firm-specific events and seasonal patterns.