Download or read book Intraday Trading Volume and Return Volatility of the Djia Stocks written by Ali F. Darrat and published by . This book was released on 2003 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: We examine the contemporaneous correlation as well as the lead-lag relation between trading volume and return volatility in all stocks comprising the Dow Jones Industrial Average (DJIA). We use 5-minute intraday data and measure return volatility by the EGARCH method. Contrary to the mixture of distribution hypothesis, the vast majority of the DJIA stock shows no contemporaneous correlation between volume and volatility. However, we find evidence of significant lead-lag relations between the two variables in a large number of the DJIA stocks in accordance with the sequential information arrival hypothesis.
Download or read book Intraday Information Trading Volume and Return Volatility written by Edward H. Chow and published by . This book was released on 2004 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Testing the Implications of Overconfidence for Intraday Trading written by Ali F. Darrat and published by . This book was released on 2005 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: We test the implications of overconfidence behavior using U.S. intraday trading data. We propose several testable hypotheses for return autocorrelations, trading volume, return volatility, and for the causal interrelations between volume and volatility. As predicted by overconfidence behavior, return autocorrelations are positive for short lags and then gradually decline as lags lengthen. Also consistent with the prediction of overconfidence together with biased self-attribution, return volatility is higher during periods containing public news signals compared with volatility during periods without public news signals. To differentiate between the overconfidence hypothesis and the sequential information arrival hypothesis, we test the lead-lag links between trading volume and return volatility during periods without public news. After necessary Bayesian adjustments to avoid large sample biases, we find evidence that volume Granger-causes volatility but without feedback during the periods without public news. The results lend support to the overconfidence hypothesis as opposed to the sequential information arrival hypothesis and suggest that investors trade according to their private signals but are reluctant to close their positions afterwards.
Download or read book Commonality Information and Return Return Volatility Volume Relationship written by Xiaojun He and published by . This book was released on 2003 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book The Intraday Behaviour of Bid Ask Spreads Trading Volume and Return Volatility written by Syed Mujahid Hussain and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper undertakes a fresh empirical investigation of key financial market variables and the theories that link them. We employ high frequency 5-minute data that include transaction price, trading volume, and the close bid and ask quote for the period May 5, 2004 through September 29, 2005. We document a number of regularities in the pattern of intraday return volatility, trading volume and bid-ask spreads. We are able to confirm the reverse J-shaped pattern of intraday bid-ask spreads with the exception of a major bump following the intraday auction at 13:05 CET. The aggregate trading volume exhibits L-shaped pattern for the German blue chip index, while German index volatility displays a somewhat reverse J-shaped pattern with two major bumps at 14:30 and 15:30 CET. Our empirical findings show that contemporaneous and lagged trading volume and bid-ask spreads have numerically small but statistically significant effect on return volatility. Our results also indicate asymmetry in the effects of volume on conditional volatility. However, inclusion of both measures as proxy for informal arrival in the conditional volatility equation does not explain the well known volatility persistence in intraday stock returns.
Download or read book Intraday Patterns in Returns Trading Volume Volatility and Trading Frequency on SEATS written by Michael J. Aitken and published by . This book was released on 1993 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Public Information Arrival and Volatility of Intraday Stock Returns written by Petko S. Kalev and published by . This book was released on 2014 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study employs firm-specific announcements as a proxy for information flows and investigates the information-volatility relation using high-frequency data from the Australian Stock Exchange. Our analysis reveals a positive and significant impact of the arrival rate of the selected news variable on the conditional variance of stock returns, even after controlling for the potential effects of trading volume and high opening volatility. Furthermore, the inclusion of the news variable in the conditional variance equation of the generalized autoregressive conditional heteroscedastic model also reduces volatility persistence, especially with intraday data. Combined with the evidence that news arrivals display a very strong pattern of autocorrelation, our results are consistent with the Mixture of Distribution Hypothesis, which attributes conditional heteroscedasticity of stock returns to time-dependence in the news arrival process.
Download or read book Volume and Volatility in the Stock Market written by Melissa Danielle Davis and published by . This book was released on 2000 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Return Volatility and Trading Volume in Financial Markets written by Torben G. Andersen and published by . This book was released on 1993 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Information Trading and Stock Returns written by K. C. Chan and published by . This book was released on 1994 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper compares the intra-day patterns on the NYSE and AMEX of volatility, trading volume and bid-ask spreads for European dually- listed stocks, Japanese dually-listed stocks also listed in London, and Japanese dually-listed stocks not listed in London with American stocks of comparable average trading volume and volatility. It is shown that the intra-day patterns for these stocks are remarkably similar even though the public information flows differ markedly across these stocks during the trading day. In the morning, Japanese stocks have the greatest volatility and volume, followed by European stocks and American stocks. These rankings are reversed in the afternoon. We argue that these patterns are consistent with markets reacting to the overnight accumulation of public information which is greatest for Japanese stock and smallest for American stocks and inconsistent with the view that early morning volatility can be attributed to monopolistic specialist behavior.
Download or read book Return Volatility and Trading Volume written by Torben G. Andersen and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: An empirical model for the return volatility-trading volume system is developed from a mircostructure framework in which informational asymmetries and liquidity needs motivate trade in response to the arrival of new information. The specification modifies the quot;Mixture of Distribution Hypothesisquot; (MDH). The dynamic features of the system are governed by the information flow, modeled as a stochastic volatility process that generalizes successful ARCH specifications. The persistence of volatility is fairly low, hinting at a quot;robustifyingquot; impact of including volume in the system. Speciification tests support the modified specification and show that it outperforms the standard MDH.
Download or read book Volume Volatility and Return Relationships written by Megan Yuan Sun and published by . This book was released on 2003 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Intraday Versus Inter day Trading Analysis of Market Depth Trading Volume and Return Volatility with Holiday Effects on US and Taiwan Stock Market written by and published by . This book was released on 2015 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Intraday Price Volatility and Trading Volume written by Toshiaki Watanabe and published by . This book was released on 1996 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Intraday Trading Activity and Volatility written by Vivek Rajvanshi and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We use tick-by-tick data for one energy futures (crude oil) and four metal futures (gold, silver, copper, and zinc) traded at Multi-Commodity Exchange India Limited (MCX) for the period of four years from January 1, 2009 to December 31, 2012. We test and find support for the Mixture of-Distribution Hypothesis (MDH), which suggests a positive simultaneous relationship between trading volume and price volatility, and the Sequential Information Arrival Hypothesis (SIAH), which argues that information arrives sequentially in the market and there would be a lead-lag relationship between volatility and volume. Further, in order to test the dispersed belief and asymmetrical information hypothesis, we test the impact of the net effect of trading numbers and order imbalance on volatility. We find that trading numbers explain the volume-volatility relationship better than the order imbalance and mainly drive the return volatility in the Indian commodity futures market. Our results find strong support for the above hypotheses and suggest that the four theories -- MDH, SIAH, dispersed belief, and asymmetrical information hypothesis -- complement each other.
Download or read book Trading Volume Volatility and Return Dynamics written by Leon Zolotoy and published by . This book was released on 2007 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Econometric Modelling of Stock Market Intraday Activity written by Luc Bauwens and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past 25 years, applied econometrics has undergone tremen dous changes, with active developments in fields of research such as time series, labor econometrics, financial econometrics and simulation based methods. Time series analysis has been an active field of research since the seminal work by Box and Jenkins (1976), who introduced a gen eral framework in which time series can be analyzed. In the world of financial econometrics and the application of time series techniques, the ARCH model of Engle (1982) has shifted the focus from the modelling of the process in itself to the modelling of the volatility of the process. In less than 15 years, it has become one of the most successful fields of 1 applied econometric research with hundreds of published papers. As an alternative to the ARCH modelling of the volatility, Taylor (1986) intro duced the stochastic volatility model, whose features are quite similar to the ARCH specification but which involves an unobserved or latent component for the volatility. While being more difficult to estimate than usual GARCH models, stochastic volatility models have found numerous applications in the modelling of volatility and more particularly in the econometric part of option pricing formulas. Although modelling volatil ity is one of the best known examples of applied financial econometrics, other topics (factor models, present value relationships, term structure 2 models) were also successfully tackled.
