Price discovery in tick time

Frijns, Bart; Schotman, Peter C.

doi:10.1016/j.jempfin.2009.07.002

Cited by 17 publications

(10 citation statements)

References 20 publications

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“…15 The stock exchanges in both countries use the decimal pricing system. 16 The Canadian and U.S. stock exchanges have the identical open time (9:30 EST or 14:30 UTC) and close time (16:00 EST or 21:00 UTC). Similarities between the Canadian and U.S. stock markets provide a fertile environment for studying price discovery in synchronous markets.…”

Section: Datamentioning

confidence: 99%

Information flow and price discovery dynamics

Meng

2020

Rev Quant Finan Acc

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Non-homogeneous and time-varying information flow that affects the price discovery processes within and across markets is a common occurrence in reality but is often neglected in the literature of price discovery. To analyze such information flow within and across markets, we propose a new strategy with a new dynamic price discovery measure. We use this strategy to test the efficient home market hypothesis and the sector effect hypothesis based on the intraday data of the 115 stocks cross-listed and traded in the Canadian and U.S. stock markets. We find that the Canadian stock market is more efficient in price discovery for the Canadian stocks cross-listed in the U.S. stock market. A higher trading volume in the Canadian market makes price discovery in that market more efficient. The Canadian stock market is more efficient in price discovery for stocks in the basic materials sector but not in the technology and financial sectors. The NYSE Alternext is more efficient for junior stocks while the NASDAQ is more efficient for technology stocks.

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Section: Datamentioning

confidence: 99%

Information flow and price discovery dynamics

Meng

2020

Rev Quant Finan Acc

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“…However, since we do not observe trades and quotes at the same point in time during the day, the model cannot be estimated directly. To estimate the model, we follow Frijns () and Frijns and Schotman (). Specifically, let y l be the observation vector that contains the most recent log prices and log quotes.…”

Section: Modelmentioning

confidence: 99%

The Informativeness of Trades and Quotes in the FTSE 100 Index Futures Market

Frijns

Tse

2014

Journal of Futures Markets

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This study examines the informativeness of trades and quotes in the FTSE 100 index futures market. Using a tick time model, we decompose the innovation in the efficient price into a trade‐induced and a quote‐induced part. For the extensive time period from 2001 to 2011, we find that trades are highly informative, explaining about 80% of the innovation in the efficient price. Large trades are more informative than smaller trades. We observe a noticeable upward trend in the contribution of trades, but also notice large drops in price informativeness around the recent global financial crisis and the European debt crisis. These drops could be attributed to noise trading during volatile periods. © 2014 Wiley Periodicals, Inc. Jrl Fut Mark 35:105–126, 2015

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“…Because both log P 1,t and log P 2,t are I(1) (see Theorem 1) and the linear combination log P 1,t − θ log P 2,t is I(1 + d η ) as defined in Section 3, the logprice series are cointegrated (see Theorems 3, 4, and 5). Frijns and Schotman (2006) considered a mechanism for generating quotes in tick time that is similar to the mechanism shown in Figure 1; however, they condition on durations, whereas we endogenize them in our model (1). Furthermore, their model implies standard cointegration, with a cointegrating parameter known to be 1 and a single value shock component.…”

Section: A Pure-jump Model For Log Pricesmentioning

confidence: 99%

A Pure-Jump Transaction-Level Price Model Yielding Cointegration

Hurvich

Wang

2010

Journal of Business & Economic Statistics

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We propose a new transaction-level bivariate log-price model that yields fractional or standard cointegration. The model provides a link between market microstructure and lower-frequency observations. The two ingredients of our model are a long-memory stochastic duration process for the waiting times, {τ k }, between trades and a pair of stationary noise processes, ({e k } and {η k }), which determine the jump sizes in the pure-jump log-price process. Our model includes feedback between the disturbances of the two log-price series at the transaction level, which induces standard or fractional cointegration for any fixed sampling interval t. We prove that the cointegrating parameter can be consistently estimated by the ordinary least squares estimator, and we obtain a lower bound on the rate of convergence. We propose transaction-level method-of-moments estimators of the other parameters in our model and discuss the consistency of these estimators.

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Price discovery in tick time

Cited by 17 publications

References 20 publications

Information flow and price discovery dynamics

Information flow and price discovery dynamics

The Informativeness of Trades and Quotes in the FTSE 100 Index Futures Market

A Pure-Jump Transaction-Level Price Model Yielding Cointegration

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