Online Kernel estimation of stationary stochastic diffusion models
提出一种在线核回归方法，降低平稳随机扩散模型非参数估计的计算成本，并给出渐近性质、数值例子及美国3个月国库券利率的实证分析。
Nonparametric regression has recently become important in quantitative finance due to its distribution-free property. However, this advantage does not come without any cost. As large sample sizes are always required to adequately estimate local structures, nonparametric regression is computationally intensive in real applications. This paper proposes an online method to decrease the computational cost of nonparametric regression for estimating stationary stochastic diffusion models. We establish asymptotic behaviours of the proposed estimators under appropriate conditions. Numerical examples and an empirical study of US 3-month treasury bill rates are illustrated. The application to financial risk management is also taken into consideration.