Autoregressive Conditional Heteroskedasticity and Abrupt Changes in Regime

自回归条件异方差和政权突变

• 批准号: 8920752
• 负责人: James Hamilton
• 金额: $ 4.97万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-06-01 至 1992-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8920752&HistoricalAwards=false
• 关键词: Autoregressive; Conditional; Heteroskedasticity; Abrupt; Changes

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.

本研究将开发并应用一种新的方法来模拟经济和金融时间序列的可变性。建议将20世纪80年代初发展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，有望更真实地捕捉戏剧性经济事件（如股市崩盘、金融恐慌或货币或财政政策的重大变化）的时间序列属性。能够在一个连贯的时间序列框架中描述这些事件的特征，可能对理解金融市场的行为至关重要。