The Causal and Dynamic Relationship Between Stock Returns and Trading Volume PDF Download
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Author:
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ISBN:
Category : Stocks
Languages : en
Pages : 21
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Author:
Publisher:
ISBN:
Category : Stocks
Languages : en
Pages : 21
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Author: Gong-meng Chen
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ISBN:
Category :
Languages : en
Pages :
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We examine the dynamic relation between returns, volume, and volatility of stock indexes. The data come from nine national markets and cover the period from 1973 to 2000. The results show a positive correlation between trading volume and the absolute value of the stock price change. Granger causality tests demonstrate that for some countries, returns cause volume and volume causes returns. Our results indicate that trading volume contributes some information to the returns process. The results also show persistence in volatility even after we incorporate contemporaneous and lagged volume effects. The results are robust across the nine national markets.
Author: Otavio Ribeiro de Medeiros
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Category :
Languages : en
Pages : 14
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We investigate the empirical relationship between stock returns, return volatility and trading volume using data from the Brazilian stock market (Bovespa). Our sample contains stock return and trading volume data from a theoretical portfolio including stocks participating in the Bovespa Index (Ibovespa) extending from 01/03/2000 through 12/29/2005. The empirical methods used include cross-correlation analysis, unit-root tests, bivariate simultaneous equations regression analysis, GARCH modeling, VAR modeling, and Granger causality tests. We find support for a contemporaneous as well as dynamic relationship between stock returns and trading volume, implying that forecasts of one of these variables can be only slightly improved by knowledge of the other. On the other hand, our results indicate that there is a contemporaneous and dynamic relationship between return volatility and trading volume. Additionally, by applying Granger's test for causality, we find that return volatility contains information about upcoming trading volume and vice versa.
Author: Manik Lakhani
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Category :
Languages : en
Pages :
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This project intends to study the relationship between stock returns and their trading volume and to test the causality effects. It focuses on the 50 stocks of CNX Nifty which is a value-weighted stock index of National Stock Exchange of India. Three proxies of trading volume namely, numbers of transactions, total traded quantity (volume) and total Rupee value of the traded quantity (turnover) have been taken and the asymmetry in the relationship of returns and volume is tested through regression. The study also tries to find the best proxy for volume through granger causality. The results indicate that there is asymmetry in the relation between returns and volume and the best proxy of the volume is the turnover or the value of shares traded.
Author: Rochelle L. Antoniewicz
Publisher:
ISBN:
Category : Rate of return
Languages : en
Pages : 66
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Author: Cristiana Tudor
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Category :
Languages : en
Pages :
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Abstract: This paper examines both the return-volume and volatility-volume movements on Bucharest Stock Exchange, in order to evaluate the impact of changes in stock market liquidity on stock returns and on volatility of returns. We employ linear Granger-causality tests to investigate the dynamic relation between trading volume, stock returns and returns volatility on the Romanian stock market, using daily logarithmic returns for the composite index BET-C, as a proxy for the market, and daily logarithmic change in trading volume during the period January 2004-July 2008. As a proxy for return volatility we employ absolute values of daily deviation of return from its mean value during the considered time period. We can report unidirectional linear causality from returns to volume and also from volume to volatility.
Author: Leon Zolotoy
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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: Rochelle L. Antoniewicz (Economiste.)
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ISBN:
Category :
Languages : en
Pages : 27
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Author: Gurmeet Singh
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ISBN:
Category :
Languages : en
Pages : 23
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This paper investigates the empirical relationship between return, volume and volatility dynamics of stock market by using data of the NIFTY index of NSE during the period from Jan 2007 to March 2014. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets. It is shown that ARCH family models outperform the conventional OLS models. We find that, the TARCH model is better fit, when we compare the GARCH, EGARCH and TARCH models, on the basis of AIC and SC criteria. Causality from volatility to volume can be seen as some evidence that new information arrival might follow a sequential rather than a simultaneous process. Moreover, in the GARCH model, ARCH and GARCH effects remain significant, which highlights the inefficiency in the market. In addition, EGARCH and TARCH models indicate the presence of leverage effect and positive impact of volatility on returns. Finally, the findings of granger causality test records the evidence of one way causality from volatility to trading volume and from return to volume.
Author: Robert Maria Margaretha Jozef Bauer
Publisher:
ISBN: 9789090107905
Category :
Languages : en
Pages : 191
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