Nonlinearities and Nonstationarities in Stock Returns
研究高频金融时间序列中的非线性发现是否受到数据分布变化的干扰，通过递归BDS统计量分析1980-1990年两个股票市场指数的日数据，发现1987年10月对应分布变化，且简单线性过程能复制测试行为，而自回归条件异方差模型不能。
Abstract This article addresses the question of whether recent findings of nonlinearities in high-frequency financial time series have been contaminated by possible shifts in the distribution of the data. It applies a recursive version of the Brock–Dechert–Scheinkman statistic to daily data on two stock-market indexes between January 1980 and December 1990. It is shown that October 1987 is highly influential in the characterization of the stock-market dynamics and appears to correspond to a shift in the distribution of stock returns. Sampling experiments show that simple linear processes with shifts in variance can replicate the behavior of the tests, but autoregressive conditional heteroscedastic filters are unable to do so. KEY WORDS: BDS testNonlinearityNonstationarity