Detecting multiple breaks in financial market volatility dynamics
评估了多种检测资产收益条件方差动态中结构断点的新检验方法，这些方法适用于ARCH和SV模型及高频数据波动率估计，并能在检验断点存在的同时识别其数量和位置。实证分析发现股票和外汇市场存在与亚洲和俄罗斯金融危机相关的多重断点。
Abstract The paper evaluates the performance of several recently proposed tests for structural breaks in the conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data‐driven volatility estimators using high‐frequency data. In addition to testing for the presence of breaks, the statistics identify the number and location of multiple breaks. We study the size and power of the new tests for detecting breaks in the conditional variance under various realistic univariate heteroscedastic models, change‐point hypotheses and sampling schemes. The paper concludes with an empirical analysis using data from the stock and FX markets for which we find multiple breaks associated with the Asian and Russian financial crises. These events resulted in changes in the dynamics of volatility of asset returns in the samples prior and post the breaks. Copyright © 2002 John Wiley & Sons, Ltd.