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A Study on Feedback Trading in Stock Index Futures Markets Based on the Asymmetric GARCH Model

Author: ZhuZhen
Tutor: XieChi
School: Hunan University
Course: Management Science and Engineering
Keywords: Stock index futures market Feedback trading Behavioral finance Asymmetric GARCH model
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Type: Master's thesis
Year: 2013
Downloads: 33
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Abstract


As an irrational trading activity that making investment decisions only based onpast changes in asset prices’ movement, the feedback trading has always beenconsidered as one of the important research topics in the behavior finance field. Inrecent years, in order to improve the market construction and efficiency, Asian countrieshave introduced the stock index futures based on learning from the experience ofwestern mature markets. Meanwhile, the research on traders behavior in Asian stockindex futures markets has also caused scholars’ new attention.Firstly, an theoretical analysis is made for the connotation, forming reasons andcharacteristics of feedback trading in stock index futures markets. Then, this papercompares and selects the main test methods of feedback trading, and constructs theEGARCH(1,1) and TGARCH(1,1)-based feedback trading model. after that, this paperestimates the existence and asymmetrical feature of feedback trading in nine Asian stockindex futures markets with the feedback trading model, and investigates the yieldforecasting effect of the EGARCH(1,1) and TGARCH(1,1)-based feedback tradingmodel. Finally, the policy suggestions are proposed.The results show that, in addition to the Singapore market, there is strong evidenceof positive feedback trading based on short-term or medium-term price trends in Asianstock index futures markets, which induces negative return autocorrelation. At the sametime, the positive feedback trading activity is much more violent during periods ofmarket declines than periods of market advances, with a clear asymmetry. In addition,the yield forecasting effect of the EGARCH(1,1) and TGARCH(1,1)-based feedbacktrading model is significantly better than the normal EGARCH(1,1) and TGARCH(1,1)model, which indicates that feedback trading factors should be fully considered in thestock index futures yield forecasting.

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