| Summary: | 碩士 === 國立臺灣大學 === 統計碩士學位學程 === 107 === If stock volumes are defined by “event”, they may happen many times or not to happen in one single time point at the aspect of event. Also, when there are two or more event variables like that in the dataset, the reaction of delay will probably happen between them. Therefore, if we want to figure out the true correlation between each two of them, methods like using same time span to integrate data, using kernel density estimation (KDE) to gain optimal density, or multivariate time series analysis can be used to try and test.
Dataset of this study is the ten year (2009~2018) daily price and volume data of TSMC (Taiwan Semiconductor Manufacturing Company, Limited) and UMC (United Microelectronics Corporation), retrieved from the official website of Taiwan Stock Exchange. By using correlation bootstrap confidence intervals and T-tests, the study can select the appropriate bandwidths and time spans. After that, the study constructs different situations of data transformation by the former result, and fit VARIMA models in each situation. In conclusion, using KDE or time span can both make correlations of variables closer to the real, and the following VARIMA model can have better explanation. Meanwhile, VARIMA(1,1,2) model explained the best among all the situations in this empirical research, so it can be a reference of pairs trading strategies of the two companies.
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