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Peter Reinhard Hansen, University of North Carolina at Chapel Hill:Volatility During the Financial Crisis Through the Lens of High Frequency Data: A Realized GARCH Approach

([西财新闻] 发布于 :2018-06-20 )

光华讲坛——社会名流与企业家论坛第5014

 

主题Volatility During the Financial Crisis Through the Lens of High Frequency Data: A Realized GARCH Approach

主讲人Peter Reinhard Hansen, University of North Carolina at Chapel Hill

主持人金融学院  罗荣华教授

时间2018年6月22日(周五)上午10:00-11:30

地点www.bte365.com柳林校区格致楼411会议室

主办单位:金融学院  科研处

 

主讲人简介:

Peter Hansen is the Henry A. Latané Distinguished Professor in Economics at the University of North Carolina, Chapel Hill. He holds a M.Sc in Mathematics and Economics from University of Copenhagen and a Ph.D. in Economics from University of California, San Diego. He is the current director of the (EC)^2 conference series. Professor Hansen is a leading researcher on forecasting and volatility modeling. His research spans the topics: forecasting, volatility, cointegration, and multiple testing, and his main contributions to econometrics include the Test for Superior Predictability; the Model Confidence Set, and the Realized Kernel Estimator.

内容提要:

We study financial volatility during the Global Financial Crisis and use the largest volatility shocks to identify major events during the crisis. Our analysis makes extensive use of high-frequency financial data to model volatility and to determine the timing within the day when the largest volatility shocks occurred. The latter helps us identify the events that may be associated with each of these shocks, and serves to illustrate the benefits of using high-frequency data. Some of the largest volatility shocks coincide, not surprisingly, with the bankruptcy of Lehman Brothers on September 15, 2008 and Congress’s failure to pass the Emergency Economic Stabilization Act on September 29, 2008. Yet, the largest volatility shock was on February 27, 2007, the date when Freddie Mac announced a stricter policy for underwriting subprime loans and a date that was marked by a crash on the Chinese stock market. However, the intraday high-frequency data shows that the main culprit was a computer glitch in the trading system. The days with the largest drops in volatility can in most cases be related to interventions by governments and central banks.


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