Autoregressive Conditional Heteroskedasticity and Abrupt Changes in Regime

这项研究将开发和应用一种新的建模方法 经济和金融时间序列的可变性。的 建议将自回归条件下的模型联合收割机 异方差性在20世纪80年代早期随着时间序列而发展 模型涉及（马尔可夫）切换过程。组合这些 方法提供了更真实地捕捉 重大经济事件的时间序列特性，例如 股市崩盘、金融恐慌或货币政策的重大变化 或财政政策。能够以一种 连贯的时间序列框架可能被证明是关键， 了解金融市场的行为。

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.