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Author: Megan Yuan Sun
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 702
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Author: Megan Yuan Sun
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 702
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Author: Xiaojun He
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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This paper develops a common-factor model to investigate relationships between security returns/return volatility and trading volume. The model generalizes Tauchen and Pitts' (1983) MDH model by capturing possible interactions among securities. In our model, both price changes and trading volume are governed by three kinds of mutually independent variables: common factor variables, latent information variables and idiosyncratic variables. Despite its similarity to Hasbrouck and Seppi's (2001) model in terms of the form, the model extraordinarily allows us to identify the cause of interactions among securities by decomposing factor loadings into constant and random components. Three key implications are reached from our model. First, common factor structures in returns and trading volume stem from information flows. Second, returns' common factors are not related to trading volume's common factors. This implication directly opposes Hasbrouck and Seppi's (2001) assumption. Finally, cross-firm variations of returns and volume respectively rely on underlying latent information flows. The positive relation between return volatility and volume also results only from underlying latent information flows. Thus, common factor structures in returns and trading volume have no additional explanatory power in cross-firm variations and the positive return volatility-volume relationship. We fit the model for intraday data of Dow Jones 30 stocks using the EM algorithm. The results support specifications of our model. The empirical results demonstrate 3-factor structures in returns and trading volume, respectively. All 30 stocks in our sample are governed by at least one common factor. This fact implies that our model outperforms Tauchen and Pitts' (1983) model because their model is a special case of our model without the presence of common factors. We also show that after controlling the effect of information flows, persistence in return variance disappears.
Author: Timothy J. Brailsford
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 32
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Author: Leon Zolotoy
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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In this paper we study the dynamic relationship between trading volume, volatility, and stock returns at the international stock markets. First, we examine the role of volume and volatility in the individual stock market dynamics using a sample of ten major developed stock markets. Next, we extend our analysis to a multiple market framework, based on a large sample of cross-listed firms. Our analysis is based on both semi-nonparametric (Flexible Fourier Form) and parametric techniques. Our major findings are as follows. First, we find no evidence of the trading volume affecting the serial correlation of stock market returns, as predicted by Campbell et.al (1993) and Wang (1994). Second, the stock market volatility has a negative and statistically significant impact on the serial correlation of the stock market returns, consistent with the positive feedback trading model of Sentana and Wadhwani (1992). Third, the lagged trading volume is positively related to the stock market volatility, supporting the information flow theory. Fourth, we find the trading volume to have both an economically and statistically significant impact on the price discovery process and the co-movement between the international stock markets. Overall, these findings suggest the importance of the trading volume as an information variable.
Author: Hayan Omran
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Marcus Davidsson
Publisher:
ISBN:
Category :
Languages : en
Pages : 13
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In this paper we will discuss the relationship among volume, volatility and return momentum in global financial markets. It turns out that when the volatility is large i.e. the difference between the daily high price and the daily low price is large then the trading volume is also large. We also found that a momentum strategy on volume perform on par with a momentum return investment strategy. A significant amount of positive serial correlation was also found in the volatility and volume.
Author: Rochelle L. Antoniewicz
Publisher:
ISBN:
Category : Rate of return
Languages : en
Pages : 66
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Author: Sang-Hak Lee
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Bramantyo Djohanputro
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ISBN:
Category :
Languages : en
Pages :
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Author: Sui Choi Billy Mak
Publisher:
ISBN:
Category :
Languages : en
Pages :
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