臺灣博碩士論文加值系統

許多實務上的研究都顯示，股票波動與其交易量都具有長相關，且可用分數差分過程來描述。這個研究主要目的就是去探討它們之間的關係。這篇文章中我們採用四種估計波動的方法，包含對數報酬平方法、歷史資料估計法、疊代t的估計法以及GARCH模型估計法。結果顯示在四種估計波動方法中，對數報酬平方法通常具有最大的分數差分階次，以及最高比例的分數共整合現象。分數差分的階次有聯合估計與分別估計，而共整合參數分別可用簡單線性迴歸、頻譜方面的迴歸以及半參數估計法來得到。最後對無分數共整合的序列也會對它們建立模型關係。

Many empirical studies show that both equity volatility and its trading volume have long range dependence and can be modeled as fractional integrated processes. The objective of this study is to investigate relationship between volatility and volume.We adopt four estimators of volatility, which includes the squared log returns, historical volatility, iterative t estimators and $GARCH$ estimators. The results show that among the four estimators squared log returns usually have the largest integration orders and produce hightest ratios of fractional cointegration. The fractional integrated orders are estimated separately and jointly, and the cointegration parameters are estimated by ordinary least squares, a narrow band frequency domain least squares method and a semiparametric estimator of Whittle likelihood. Models are also established when volatility and volume are not fractional cointegrated.

1. Introduction 1
2. Literature review 5
3. Cross spectrum analysis 8
4. Semiparametric estimation in long memory models 11
5. Results 20
6. Conclusion 24

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