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Trading Volume Volatility And Return Dynamics
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Book Synopsis Trading Volume, Volatility and Return Dynamics by : Leon Zolotoy
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.
Book Synopsis Stock Market Dynamics by : Robert Maria Margaretha Jozef Bauer
Download or read book Stock Market Dynamics written by Robert Maria Margaretha Jozef Bauer and published by . This book was released on 1997 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Volume and the Nonlinear Dynamics of Stock Returns by : Chiente Hsu
Download or read book Volume and the Nonlinear Dynamics of Stock Returns written by Chiente Hsu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: This manuscript is about the joint dynamics of stock returns and trading volume. It grew out of my attempt to construct an intertemporal asset pricing model with rational agents which can. explain the relation between volume, volatility and persistence of stock return documented in empirical literature. Most part of the manuscript is taken from my thesis. I wish to express my deep appreciation to Peter Kugler and Benedikt Poetscher, my advisors of the thesis, for their invaluable guidance and support. I wish to thank Gerhard Orosel and Gerhard Sorger for their encouraging and helpful discussions. Finally, my thanks go to George Tauchen who has been generous in giving me the benefit of his numerical and computational experience, in providing me with programs and in his encouragement. Contents 1 Introduction 1 7 2 Efficient Stock Markets Equilibrium Models of Asset Pricing 8 2. 1 2. 1. 1 The Martigale Model of Stock Prices 8 2. 1. 2 Lucas' Consumption Based Asset Pricing Model 9 2. 2 Econometric Tests of the Efficient Market Hypothesis 13 2. 2. 1 Autocorrelation Based Tests 14 16 2. 2. 2 Volatility Tests Time-Varying Expected Returns 25 2. 2. 3 3 The Informational Role of Volume 29 3. 1 Standard Grossman-Stiglitz Model 31 3. 2 The No-Trad Result of the BEO Model 34 A Model with Nontradable Asset 37 3. 3 4 Volume and Volatility of Stock Returns 43 4. 1 Empirical and Numerical Results 45 4.
Book Synopsis A dynamic structural model for stock return volatility and trading volume by : William A. Brock
Download or read book A dynamic structural model for stock return volatility and trading volume written by William A. Brock and published by . This book was released on 1994 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Dynamic Relation between Stock Returns, Trading Volume, and Volatility by : Gong-meng Chen
Download or read book The Dynamic Relation between Stock Returns, Trading Volume, and Volatility written by Gong-meng Chen and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis The Joint Dynamics of Asset Returns, Trading Volume and Volatility by : Gavin Conor Boyle
Download or read book The Joint Dynamics of Asset Returns, Trading Volume and Volatility written by Gavin Conor Boyle and published by . This book was released on 2000 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Volume, Volatility, and Return Relationships by : Megan Yuan Sun
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:
Book Synopsis A Dynamic Structural Model for Stock Return Volatility and Trading Volume by : William A. Brock
Download or read book A Dynamic Structural Model for Stock Return Volatility and Trading Volume written by William A. Brock and published by . This book was released on 2010 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper seeks to develop a structural model that lets data on asset returns and trading volume speak to whether volatility autocorrelation comes from the fundamental that the trading process is pricing or, is caused by the trading process itself. Returns and volume data argue, in the context of our model, that persistent volatility is caused by traders experimenting with different beliefs based upon past profit experience and their estimates of future profit experience. A major theme of our paper is to introduce adaptive agents in the spirit of Sargent (1993) but have them adapt their strategies on a time scale that is slower than the time scale on which the trading process takes place. This will lead to positive autocorrelation in volatility and volume on the time scale of the trading process which generates returns and volume data. Positive autocorrelation of volatility and volume is caused by persistence of strategy patterns that are associated with high volatility and high volume. Thee following features seen in the data: (i) The autocorrelation function of a measure of volatility such as squared returns or absolute value of returns is positive with a slowly decaying tail. (ii) The autocorrelation function of a measure of trading activity such as volume or turnover is positive with a slowly decaying tail. (iii) The cross correlation function of a measure of volatility such as squared returns is about zero for squared returns with past and future volumes and is positive for squared returns with current volumes. (iv) Abrupt changes in prices and returns occur which are hard to attach to 'news.' The last feature is obtained by a version of the model where the Law of Large Numbers fails in the large economy limit.
Book Synopsis Identifying Common Long-Range Dependence in Volume and Volatility Using High-Frequency Data by : Roman Liesenfeld
Download or read book Identifying Common Long-Range Dependence in Volume and Volatility Using High-Frequency Data written by Roman Liesenfeld and published by . This book was released on 2002 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines the joint long-run dynamics of trading volume and return volatility in futures contracts on the German stock index DAX using a sample of 5-minute returns and trading volume. Employing robust semiparametric methods of inference on memory parameters, I find that volume and volatility exhibit the same degree of long-memory which is consistent with a mixture-of-distributions (MOD) model in which the latent number of information arrivals follows a long-memory process. However, there is some evidence that volume and volatility are not driven by the same long-memory process suggesting that the MOD model cannot explain the joint long-run dynamics of volatility and volume.
Book Synopsis Time and Dynamic Volume-Volatility Relation by : Xiaoqing Eleanor Xu
Download or read book Time and Dynamic Volume-Volatility Relation written by Xiaoqing Eleanor Xu and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines volume and volatility dynamics by accounting for market activity measured by the time duration between two consecutive transactions. A time-consistent vector autoregressive model (VAR) is employed to test the dynamic relationship between return volatility and trades using intraday irregularly spaced transaction data. The model is used to identify the informed and uninformed components of return volatility and to estimate the speed of price adjustment to new information. It is found that volatility and volume are persistent and highly correlated with past volatility and volume. The time duration between trades has a negative effect on the volatility response to trades and correlation between trades. Consistent with microstructure theory, shorter time duration between trades implies higher probability of news arrival and higher volatility. Furthermore, bid-ask spreads are serially dependent and strongly affected by the informed trading and inventory costs.
Book Synopsis Return, Trading Volume, and Market Depth in Currency Futures Markets by : Ai-ru Meg Cheng
Download or read book Return, Trading Volume, and Market Depth in Currency Futures Markets written by Ai-ru Meg Cheng and published by . This book was released on 2008 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Testing the Impact of Trading Volume on Market Return and Volatility by : Cristiana Tudor
Download or read book Testing the Impact of Trading Volume on Market Return and Volatility written by Cristiana Tudor and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Return Volatility and Trading Volume by : Torben G. Andersen
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.
Book Synopsis Price-Volume Relations of DAX Companies by : Henryk Gurgul
Download or read book Price-Volume Relations of DAX Companies written by Henryk Gurgul and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study provides empirical evidence of the joint dynamics between stock returns and trading volume using stock data of DAX companies. Contemporaneous as well as dynamic interactions are investigated for a period from January 1994 to December 2005 on a daily basis. Our results suggest that there is almost no relationship between stock return levels and trading volume in either direction. We find that trading volume is contemporaneously positively related to return volatility. In addition, we establish that lagged return volatility induces trading volume movements.Finally, we examine dependencies in the tails and find no significant support for the hypothesis of the independence of the maximal values of absolute returns and trading volume.
Book Synopsis Dynamic Volume-Return Relationship by : Bartosz Gebka
Download or read book Dynamic Volume-Return Relationship written by Bartosz Gebka and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We test the relationship between the changes in trading volume and subsequent returns for stocks traded on the Warsaw Stock Exchange (WSE). We find high volume stocks to experience strong price reversals and low volume stocks to experience weak price reversals and even continuations. Focusing on longer portfolio selection periods does not strengthen these results, and focusing on extreme change in past trading volume and past returns does so only for some high volume portfolios. The sign of volume changes is more informative than the magnitude. Our results can be interpreted as evidence of the prevalence of uninformed traders on the WSE.
Book Synopsis Arch Effects and Trading Volume by : Jeff Fleming
Download or read book Arch Effects and Trading Volume written by Jeff Fleming and published by . This book was released on 2005 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: Studies that fit volume-augmented GARCH models often find support for the hypothesis that trading volume explains ARCH effects in daily stock returns. We show that this finding is due to an unrecognized constraint imposed by the GARCH specification used for the analysis. Using a more flexible specification, we find no evidence that inserting volume into the conditional variance function of the model reduces the importance of lagged squared returns in capturing volatility dynamics. Volume is strongly correlated with contemporaneous return volatility, but the correlation is driven largely by transitory volatility shocks that have little to do with the highly persistent component of volatility captured by standard volatility models.
Book Synopsis Stock Market Volatility Dynamics by : Maxime Bonelli
Download or read book Stock Market Volatility Dynamics written by Maxime Bonelli and published by . This book was released on 2016 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a two-factor volatility model to study the impact of news arrival and trading volume on stock returns variance. The model can explicitly account for the association between volatility and volume, as well as the persistence in equity variance. Unlike the standard "Mixture of Distribution Hypothesis", the conditional variance is governed by the stochastic information arrival and adds a persistent GARCH component, in order to disentangle transient from persistent volatility variations. The common observation that large volumes are associated with high volatility is explained by the fact that unexpected shocks in volume increase volatility, which is not the case for expected volumes of trading. Furthermore, the persistence of volatility is essentially unrelated to volume implying that the latter does not explain ARCH effect. Finally, we find that unexpected shocks in volume and the persistent GARCH component are both main drivers of volatility dynamics.
