REWEIGHTED FUNCTIONAL ESTIMATION OF DIFFUSION MODELS
针对非参数连续时间扩散模型中扩散函数可能为负的问题，提出一种基于重加权的函数估计方法，保证有限样本下非负，并建立极限理论，适用于非平稳数据。
The local linear method is popular in estimating nonparametric continuous-time diffusion models, but it may produce negative results for the diffusion (or volatility) functions and thus lead to insensible inference. We demonstrate this using U.S. interest rate data. We propose a new functional estimation method of the diffusion coefficient based on reweighting the conventional Nadaraya–Watson estimator. It preserves the appealing bias properties of the local linear estimator and is guaranteed to be nonnegative in finite samples. A limit theory is developed under mild requirements (recurrence) of the data generating mechanism without assuming stationarity or ergodicity.