A Nonparametric Test for Autoregressive Conditional Heteroscedasticity: A Markov-Chain Approach
提出一种基于有限状态马尔可夫链的非参数检验，用于检测自回归条件异方差。蒙特卡洛实验表明，在条件正态分布下该检验与拉格朗日乘子检验表现相当，而在t分布、对数正态分布和指数分布下更优。
In this article we propose a nonparametric test for autoregressive conditional heteroscedasticity based on finite-state Markov chains. A simple Monte Carlo experiment suggests that in finite samples it performs comparably to the Lagrange multiplier test under conditional normality and is superior for the t, lognormal, and exponential distributions. As an illustration, we apply both tests to Canadian/U.S. forward foreign exchange data.