DETECTING CHANGES IN GARCH(1,1) PROCESSES WITHOUT ASSUMING STATIONARITY
提出一种无需假设平稳性的新检验方法，用于检测GARCH(1,1)过程参数是否发生改变，适用于平稳或爆炸性波动率过程，蒙特卡洛模拟显示检验效果良好，并应用于数千只美国股票。
This article develops a new test to detect changes in generalized autoregressive conditionally heteroscedastic (GARCH(1,1)) processes without imposing a stationary assumption. Specifically, the procedure tests the null hypothesis of a GARCH process with constant parameters, either in (strictly) stationary or explosive regimes, against the alternative hypothesis of parameter changes. We derive the limiting distribution of the test statistics and establish their asymptotic consistency. Monte Carlo simulations show that the proposed test has good size control and high power. We demonstrate a prototype application on a small group of stocks and report a further extensive application to more than ten thousand U.S. stocks.