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A Regime Switching Inter-trade Duration Model with Empirical Analysis to the Nasdaq Limit Order Market

来源:湖南大学经济与管理研究中心 日期:2018-09-26 作者:

Sep. 28th (Friday), 16:15-17:30


地 点Room 206, Shuishang Teaching Building

题:A Regime Switching Inter-trade Duration Model with Empirical Analysis to the Nasdaq Limit Order Market


主讲人Zhicheng Li

 Assistant Professor in the Finance Institute,the Center for Economics, Finance and Management Studies at Hunan University.  


要:We identify a common bimodal distribution for inter-trade durations in recent data from the Nasdaq limit order market. This novel finding, in addition to an observed pattern of clustering in inter-trade durations, have led us to build a parsimonious regime switching model in which the inter-trade durations move between a high and a low duration regime. The high regime with long intertrade durations probably result from high-frequency traders’passive trading period in which the flow of market orders is determined by slow human traders.While in the low regime, the high-frequency traders aggressively initiate trades and the inter-trade durations are very short. Besides that, in order to analyze which variables in the limit order market influence the high-frequency traders’decision of switching their trading strategies, we incorporate factor analysis into our model by logistic regression. Finally, we implement an empirical study for the Nasdaq stocks. The analysis of a single stock over several days has found consistent significant factors and the out-of-sample test shows a good predictive power. The analysis of many stocks reveals some common factors which would impact the regime switching probability in general.



报告人简介 Dr. Zhicheng Li joined the center for Economics, Finance and Management Studies at Hunan University in Fall 2018. Now he is a tenure track Assistant Professor in the Finance Institute. Zhicheng earned his Ph.D. from Stony Brook University. His research interests include financial economics, financial econometrics, time series modeling and computational finance.


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