Local-Linear Estimation of Time-Varying-Parameter GARCH Models and Associated Risk Measures
提出一种非参数方法估计时变参数的GARCH(1,1)模型，使用局部线性估计器，证明其一致性和渐近正态性，蒙特卡洛模拟显示优于滚动窗口估计，并应用于股票指数日收益率以检验参数稳定性及风险度量。
Abstract In this article, we propose a nonparametric approach to estimating generalized autoregressive conditional heteroskedasticity (1,1) models with time-varying parameters. We model the time-varying parameters as a smooth function of time and estimate them using a local linear estimator. We show that our estimator is consistent and is asymptotically normal and that the proposed estimator outperforms a rolling window estimator in Monte Carlo simulation experiments. We present strong evidence of parameter instabilities using daily returns of stock indices and explore implications to risk management measures, such as value-at-risk and expected shortfall, through backtesting.