A vector integer-valued moving average model for high frequency financial count data

Quoreshi, Shahiduzzaman

doi:10.1016/j.econlet.2008.08.027

Cited by 25 publications

(18 citation statements)

References 8 publications

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“…Quoreshi [17] extends the INMA model to a vector INMA (VINMA) model. The VINMA enables the study of the spillover effects of transactions from one stock to the other.…”

Section: The Inma Binma and Vinma Modelsmentioning

confidence: 99%

“…The counts in the intervals are the number of transactions for corresponding intervals. Brännäs and Quoreshi [15] employ one minute time scale while Quoreshi [16,17] uses a five minute scale. The collection of the number of transactions over a time period makes up a time series of count data.…”

Section: Application Of Inma Model Class On High Frequency Datamentioning

confidence: 99%

“…Quoreshi [16] discusses the conditional least squares CLS, FGLS and GMM estimator for BINMA model. Quoreshi [17,18] consider CLS and FGLS estimators for VINMA and long memory model. The authors did not consider the maximum likelihood due to unknown underlying distributions of the innovations.…”

Section: The Inma Binma and Vinma Models Estimationmentioning

confidence: 99%

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A review of INMA integer-valued model class, application and further development

Shahiduzzaman¹,

Uddin²,

Mamode³

2020

Filomat

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In this paper, we review INMA time series of integer-valued model class, and discuss its further development. These models have been developed for analyzing high frequency financial count data. A vivid description of high frequency data in the context of market micro structure is given. The most distinguishing feature that makes the INMA model class different from its continuous variable MA counterpart is that multiplication of variables with real valued parameters no longer remains a viable operation when the result is to be integer-valued. In the estimation of these models, no underlying distributions are assumed. Hence, the discussion of estimations is limited to CLS, FGLS and GMM. A further development of estimation procedures for these models have also been reviewed. We suggest that the models could be estimated with Quasi Maximum Likelihood and propose in addition a Generalized Method of Moment of Quasi Maximum Likelihood. We have also discussed how INMA model class can be extended with different underlying distributions for innovations.

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“…Quoreshi [17] extends the INMA model to a vector INMA (VINMA) model. The VINMA enables the study of the spillover effects of transactions from one stock to the other.…”

Section: The Inma Binma and Vinma Modelsmentioning

confidence: 99%

Section: Application Of Inma Model Class On High Frequency Datamentioning

confidence: 99%

See 1 more Smart Citation

A review of INMA integer-valued model class, application and further development

Shahiduzzaman¹,

Uddin²,

Mamode³

2020

Filomat

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“…Brännäs and Quoreshi (2010) advance integer-valued moving average model to model the number of transactions in intraday data of stocks. Quoreshi (2006Quoreshi ( , 2008Quoreshi ( , 2014Quoreshi ( , 2017 advances further the INMA model into bivariate, multivariate and long memory (INARFIMA) framework. These papers consider Conditional Least Square (CLS), Feasible Generalized Least Square (FGLS), and Generalized Methods of Moments (GMM).…”

Section: Introductionmentioning

confidence: 99%

Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework

Quoreshi

Uddin

Khan

2019

JRFM

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This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying distribution. The moments with conditional heteroscedasticity have been discussed. In a Monte Carlo experiment, it was found that the QML estimator performs as well as CLS and FGLS in terms of eliminating serial correlations, but the estimator can be sensitive to start value. Hence, two-stage QML has been suggested. In empirical estimation on two stock transaction data for Ericsson and AstraZeneca, the 2SQML turns out relatively more efficient than CLS and FGLS. The empirical results suggest that both of the series have long memory properties that imply that the impact of macroeconomic news or rumors in one point of time has a persistence impact on future transactions.

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“…The Conditional Least Squares (CLS) is also considered in this paper. In many time series studies conducted by Quoreshi [2008], CLS was used for estimation purpose and yielded consistent parameter estimates. Another popular technique is the Generalized Method of Moments (GMM) which combines a set of moment scores into an objective function and this is minimized and solved iteratively using the Newton-Raphson technique.…”

Section: Introductionmentioning

confidence: 99%

Estimation Methods for a Flexible INAR(1) COM-Poisson Time Series Model

Sunecher

Khan

Jowaheer

2018

Journal of Applied Mathematics, Statistics and Informatics

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Time series of counts occur in many real-life situations where they exhibit various forms of dispersion. To facilitate the modeling of such time series, this paper introduces a flexible first-order integer-valued non-stationary autoregressive (INAR(1)) process where the innovation terms follow a Conway-Maxwell Poisson distribution (COM-Poisson). To estimate the unknown parameters in this model, different estimation approaches based on likelihood and quasi-likelihood formulations are considered. From simulation experiments and a real-life data application, the Generalized Quasi-Likelihood (GQL) approach yields estimates with lower bias than the other estimation approaches.

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A vector integer-valued moving average model for high frequency financial count data

Cited by 25 publications

References 8 publications

A review of INMA integer-valued model class, application and further development

A review of INMA integer-valued model class, application and further development

Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework

Estimation Methods for a Flexible INAR(1) COM-Poisson Time Series Model

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