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Trading intensity, volatility, and arbitrage activity [An article from: Journal of Banking and Finance]

AuthorN. Taylor
PublisherElsevier
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Author(s)N. Taylor
PublisherElsevier
ISBN / ASINB000RR11PE
ISBN-13978B000RR11P8
AvailabilityAvailable for download now
Sales Rank11,232,420
MarketplaceUnited States  🇺🇸

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This digital document is a journal article from Journal of Banking and Finance, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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The objective of this paper is to uncover the determinants of trading intensity in futures markets. In particular, the time between adjacent transactions (referred to as transaction duration) on the FTSE 100 index futures market is modeled using various augmentations of the basic autoregressive conditional duration (ACD) model introduced by Engle and Russell [Econometrica 66 (1998) 1127]. The definition of transaction duration used in this paper is an important variable as it represents the inverse of instantaneous conditional return volatility. As such, this paper can also be viewed as an investigation into the determinants of (the inverse of) instantaneous conditional return volatility. The estimated parameters from various ACD models form the basis of the hypothesis tests carried out in the paper. As predicted by various market microstructure theories, we find that bid-ask spread and transaction volume have a significant impact upon subsequent trading intensity. However, the major innovation of this paper is the finding that large (small) differences between the market price and the theoretical price of the futures contract (referred to as pricing error) lead to high (low) levels of trading intensity in the subsequent period. Moreover, the functional dependence between pricing error and transaction duration appears to be non-linear in nature. Such dependence is implied by the presence of arbitragers facing non-zero transaction costs. Finally, a comparison of the forecasting ability of the various estimated models shows that a threshold ACD model provides the best out-of-sample performance.

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