Intraday Trading Volume and Return Volatility of the Djia Stocks

Intraday Trading Volume and Return Volatility of the Djia Stocks PDF Author: Ali F. Darrat
Publisher:
ISBN:
Category :
Languages : en
Pages : 13

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Book Description
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.

Intraday Trading Volume and Return Volatility of the Djia Stocks

Intraday Trading Volume and Return Volatility of the Djia Stocks PDF Author: Ali F. Darrat
Publisher:
ISBN:
Category :
Languages : en
Pages : 13

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Book Description
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.

Volume and Volatility in the Stock Market

Volume and Volatility in the Stock Market PDF Author: Melissa Danielle Davis
Publisher:
ISBN:
Category :
Languages : en
Pages : 44

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Book Description


Derivatives and Hedge Funds

Derivatives and Hedge Funds PDF Author: Stephen Satchell
Publisher: Springer
ISBN: 1137554177
Category : Science
Languages : en
Pages : 416

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Book Description
Over the last 20 years hedge funds and derivatives have fluctuated in reputational terms; they have been blamed for the global financial crisis and been praised for the provision of liquidity in troubled times. Both topics are rather under-researched due to a combination of data and secrecy issues. This book is a collection of papers celebrating 20 years of the Journal of Derivatives and Hedge Funds (JDHF). The 18 papers included in this volume represent a small sample of influential papers included during the life of the Journal, representing industry-orientated research in these areas. With a Preface from co-editor of the journal Stephen Satchell, the first part of the collection focuses on hedge funds and the second on markets, prices and products.

The Intraday Behaviour of Bid-Ask Spreads, Trading Volume and Return Volatility

The Intraday Behaviour of Bid-Ask Spreads, Trading Volume and Return Volatility PDF Author: Syed Mujahid Hussain
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
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.

Intraday Information, Trading Volume, and Return Volatility

Intraday Information, Trading Volume, and Return Volatility PDF Author: Edward H. Chow
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 148

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Book Description


Intraday Patterns in Returns, Trading Volume, Volatility and Trading Frequency on SEATS

Intraday Patterns in Returns, Trading Volume, Volatility and Trading Frequency on SEATS PDF Author: Michael J. Aitken
Publisher:
ISBN:
Category : Securities
Languages : en
Pages : 83

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Book Description


Intraday Versus Inter-day Trading : Analysis of Market Depth, Trading Volume and Return Volatility with Holiday Effects on US and Taiwan Stock Market

Intraday Versus Inter-day Trading : Analysis of Market Depth, Trading Volume and Return Volatility with Holiday Effects on US and Taiwan Stock Market PDF Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 100

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Book Description


Commonality, Information and Return/Return Volatility - Volume Relationship

Commonality, Information and Return/Return Volatility - Volume Relationship PDF Author: Xiaojun He
Publisher:
ISBN:
Category :
Languages : en
Pages : 36

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Book Description
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.

Market Volatility and Investor Confidence

Market Volatility and Investor Confidence PDF Author: New York Stock Exchange. Market Volatility and Investor Confidence Panel
Publisher:
ISBN:
Category : Program trading (Securities)
Languages : en
Pages : 396

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Book Description


Information, Trading and Stock Returns

Information, Trading and Stock Returns PDF Author: K. C. Chan
Publisher:
ISBN:
Category : Stock quotations
Languages : en
Pages : 60

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Book Description
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.