英文文献：Unstable volatility functions: the break preserving local linear estimator-不稳定波动函数:保破局部线性估计
英文文献作者：Isabel Casas,Irene Gijbels
英文文献摘要：
The objective of this paper is to introduce the break preserving local linear (BPLL) estimator for the estimation of unstable volatility functions. Breaks in the structure of the conditional mean and/or the volatility functions are common in Finance. Markov switching models (Hamilton, 1989) and threshold models (Lin and Ter?svirta, 1994) are amongst the most popular models to describe the behaviour of data with structural breaks. The local linear (LL) estimator is not consistent at points where the volatility function has a break and it may even report negative values for finite samples. The estimator presented in this paper generalises the classical LL. The BPLL maintains the desirable properties of the LL with regard to the bias and the boundary estimation, it estimates the breaks consistently and it ensures that the volatility estimates are always positive.

摘要提出了一种不稳定波动函数的保断局部线性估计方法。条件均值和/或波动函数结构的中断在金融中很常见。马尔可夫切换模型(Hamilton, 1989)和阈值模型(Lin和Terasvirta, 1994)是描述具有结构中断的数据行为的最流行的模型之一。局部线性(LL)估计器在波动率函数有断点的点上是不一致的，它甚至可能报告有限样本的负值。本文给出的估计量推广了经典LL估计。BPLL在偏差和边界估计方面保持了LL的理想特性，它一致地估计断点，并确保波动估计总是正的。