Author: Timothy J. Brailsford
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 32

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Author: Robert Maria Margaretha Jozef Bauer
Publisher:
ISBN: 9789090107905
Category :
Languages : en
Pages : 191

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Author: Otavio Ribeiro de Medeiros
Publisher:
ISBN:
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: Mr.Charles Frederick Kramer
Publisher: International Monetary Fund
ISBN: 1451854870
Category : Business & Economics
Languages : en
Pages : 36

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The relationship of stock returns and trading volume is the focus of much recent interest. I examine an economic model of a rational trader who operates in a market with transactions costs and noise trading. The level of trading affects the rational trader’s marginal cost of transacting; as a result, trading volume is a source of risk. This engenders an equilibrium relationship between returns and volume. The model also provides a simple way to scrutinize this relationship empirically. Empirical evidence supports the implications of the model.

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: 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: Gurmeet Singh
Publisher:
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: Dr.Lakshmi P.
Publisher:
ISBN:
Category :
Languages : en
Pages : 13

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This paper empirically examines the relationship between trading volume of FII flows and volatility of stock returns. The contemporaneous correlation and asymmetry between NIFTY returns and FII trading volume is studied through OLS. There is evidence for positive contemporaneous correlation between returns and volume. The relationship between conditional volatility and volume is investigated through GARCH model by introducing volume as an explanatory variable in the GARCH equation. The results indicate that GARCH effect is reduced only to a negligible level by the inclusion of trading volume of FIIs as an explanatory variable. This implies that FIIs influence towards persistence of volatility is very low and there may be other factors responsible for the same.

Author: Wen-Cheng Lu
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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This paper examines the dynamic relationship of volatility and trading volume using a bivariate vector autoregressive methodology. This study found bidirectional causal relations between trading volume and volatility, which is in accordance with sequential information arrival hypothesis that suggests lagged values of trading volume provide the predictability component of current volatility. Findings also reveal that trading volume shocks significantly contribute to the variability of volatility and then volatility shocks partly account for the variability of trading volume.

Author: Hanfeng Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 39

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We find that trading volume not only contributes positively to the contemporaneous volatility, as indicated in previous literature, but also contributes negatively to the subsequent volatility. And this pattern between trading volume and volatility is consistently held among individual stocks, volume-based portfolios, size-based portfolios, and market index, and among daily data and weekly data. These empirical findings tend to support that the Information-Driven-Trade (IDT) hypothesis is more pervasive and powerful in explaining trading activities in the stock market than the Liquidity-Driven-Trade (LDT) hypothesis. Our additional tests obtain three interesting findings, 1) liquidity and the degree of information asymmetry influence the relation between volume and subsequent volatility, 2) the effect of volume on subsequent volatility and volume size have a non-linear relationship, which is consistent with Barclay and Warner (1993, JFE)'s finding, 3) the effect of volume on subsequent volatility is asymmetry when the stock price moves up and when the stock price moves down, and we attribute this asymmetry to the short-selling constraints.