Autoregressive Conditional Heteroskedasticity and Abrupt Changes in Regime

这项研究将开发和应用一种新的方法来建模经济和金融时间序列的变异性。建议将20世纪80年代初发展起来的自回归条件异方差模型与涉及(马尔可夫)切换过程的时间序列模型结合起来。将这些方法结合起来，有望更真实地捕捉股市崩盘、金融恐慌或货币或财政政策重大变化等戏剧性经济事件的时间序列特性。能够在一个连贯的时间序列框架中描述这些事件，可能被证明是理解金融市场行为的关键。

This research will develop and apply a new approach to modeling the variability of economic and financial time series. The suggestion is to combine the models of autoregressive conditional heteroskedasticity developed in the early 1980s with time series models involving (Markov) switching processes. Combining these approaches offers promise of capturing more realistically the time series properties of dramatic economic events such as the stock market crash, financial panics or major changes in monetary or fiscal policy. Being able to characterize these events in a coherent time-series framework may prove to be critical in understanding the behavior of financial markets.