Security bid/ask dynamics with discreteness and clustering: Simple strategies for modeling and estimation

Hasbrouck, Joel

doi:10.1016/s1386-4181(98)00008-1

Cited by 53 publications

(5 citation statements)

References 50 publications

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“…Although bid/ask spread is an important factor in determining the overall degree of clustering, some of the prior empirical studies have shown that price clustering could also influence the bid/ask spread (ap Gwilym et al, 1998a;Hasbrouck, 1999). Hence, both the bid/ask spread and the degree of clustering could be determined simultaneously.…”

Section: Price Clustering Of Floor-traded and E-mini Index Futuresmentioning

confidence: 99%

Price clustering in E‐mini and floor‐traded index futures

Chung

Chiang

2006

Journal of Futures Markets

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This article sets out to investigate price clustering in both the open-outcry (floor-traded) and electronically traded (E-mini) index futures markets of the DJIA, S&P 500, and NASDAQ-100 indices. The results show that although price clustering is ubiquitous in both the floor-traded and E-mini index futures markets, it nevertheless tends to be higher for open-outcry index futures, with the clustering in floor-traded NASDAQ-100 index futures demonstrating the highest level (97%) at zero digits. A significant increase was also found in price clustering in floor-traded index futures after the introduction of E-mini futures trading. The results tend to suggest that those trading mechanisms that involve higher levels of human participation, such as the open-outcry markets, may well lead to increased incidences of price clustering.

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Section: Price Clustering Of Floor-traded and E-mini Index Futuresmentioning

confidence: 99%

Price clustering in E‐mini and floor‐traded index futures

Chung

Chiang

2006

Journal of Futures Markets

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“…Similar to the nonclustering noise, the biasing function b(·) can also depend on x, but the sequence of {b i (·)} is assumed to be independent given the value sequence {y i }. The random biasing function is similar to the clustering control variable proposed in Hasbrouck (1999).…”

Section: Construction Of Y From Xmentioning

confidence: 99%

A Partially Observed Model for Micromovement of Asset Prices with Bayes Estimation via Filtering

Zeng

2003

Mathematical Finance

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A general micromovement model that describes transactional price behavior is proposed. The model ties the sample characteristics of micromovement and macromovement in a consistent manner. An important feature of the model is that it can be transformed to a filtering problem with counting process observations. Consequently, the complete information of price and trading time is captured and then utilized in Bayes estimation via filtering for the parameters. The filtering equations are derived. A theorem on the convergence of conditional expectation of the model is proved. A consistent recursive algorithm is constructed via the Markov chain approximation method to compute the approximate posterior and then the Bayes estimates. A simplified model and its recursive algorithm are presented in detail. Simulations show that the computed Bayes estimates converge to their true values. The algorithm is applied to one month of intraday transaction prices for Microsoft and the Bayes estimates are obtained.

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“…Hence it is rather ironic that science, whether physical or social, often delivers just the opposite: parametric non-linear dynamics, driven by nonparametric non-Gaussian shocks. Hasbrouck (1999) Even in cases where theory does produce linear systems with parametric non-Gaussian disturbances, transformations can sometimes be used to achieve normality, allowing us to use traditional linear Gaussian methods instead of the more tedious Durbin±Koopman methods. Consider the stochastic volatility model, for example.…”

Section: J a Nelder (Imperial College Of Science Technology And Mementioning

confidence: 99%

Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives

Durbin

Koopman

2000

Journal of the Royal Statistical Society Series B: Statistical Methodology

286

162

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The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Markov chain Monte Carlo methods are not employed. Non-Gaussian disturbances for the state equation as well as for the observation equation are considered. Methods for estimating conditional and posterior means of functions of the state vector given the observations, and the mean-square errors of their estimates, are developed. These methods are extended to cover the estimation of conditional and posterior densities and distribution functions. Choice of importance sampling densities and antithetic variables is discussed. The techniques work well in practice and are computationally ef®cient. Their use is illustrated by applying them to a univariate discrete time series, a series with outliers and a volatility series.

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Security bid/ask dynamics with discreteness and clustering: Simple strategies for modeling and estimation

Cited by 53 publications

References 50 publications

Price clustering in E‐mini and floor‐traded index futures

Price clustering in E‐mini and floor‐traded index futures

A Partially Observed Model for Micromovement of Asset Prices with Bayes Estimation via Filtering

Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives

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