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Real-Time Market Abuse Detection with a Stochastic Parameter Model

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  • Radosław Cholewiński

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    (Noble Bank)

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    Abstract

    This paper develops a new model of market abuse detection in real time. Market abuse is detected, as Minenna (2003) proposed, on the basis of prediction intervals. The model structure is based on the discrete-time, extended market model introduced by Monteiro, Zaman, Leitterstorf (2007) to analyze the market cleanliness. Parameters of the expected return equation are assumed, however, to be time-varying and estimated under the state-space framework using the extended Kalman filter postulated by Chou, Engle, Kane (1992) to capture the GARCH effect in returns. QML estimation is performed on intraday data; its utilization is proposed as an alternative to the continuous time modeling by Minenna (2003). This framework is generalized to the bivariate case which enables the analysis of daily open/close data. The paper also extends procedures of the statistical verification of the estimated state-space model to include the uncertainty arising from time-invariant parameters.

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    Bibliographic Info

    Article provided by CEJEME in its journal Central European Journal of Economic Modelling and Econometrics.

    Volume (Year): 1 (2009)
    Issue (Month): 3 (November)
    Pages: 261-284

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    Handle: RePEc:psc:journl:v:1:y:2009:i:3:p:261-284

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    Web page: http://cejeme.org/

    Related research

    Keywords: Market abuse detection; insider trading; real-time analysis; timevarying parameters; uni- and bivariate GARCH processes;

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    1. Rebecca Emerson & Stephen Hall & Anna Zalewska-Mitura, 1997. "Evolving Market Efficiency with an Application to Some Bulgarian Shares," Economic Change and Restructuring, Springer, vol. 30(2), pages 75-90, May.
    2. Ray Chou & Robert F. Engle & Alex Kane, 1991. "Measuring Risk Aversion From Excess Returns on a Stock Index," NBER Working Papers 3643, National Bureau of Economic Research, Inc.
    3. Rockinger, Michael & Urga, Giovanni, 2000. "The Evolution of Stock Markets in Transition Economies," Journal of Comparative Economics, Elsevier, vol. 28(3), pages 456-472, September.
    4. Watson, Mark W., 1989. "Recursive solution methods for dynamic linear rational expectations models," Journal of Econometrics, Elsevier, vol. 41(1), pages 65-89, May.
    5. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
    6. Andrew C. Worthington & Helen Higgs, 2003. "Weak-form market efficiency in European emerging and developed stock markets," School of Economics and Finance Discussion Papers and Working Papers Series 159, School of Economics and Finance, Queensland University of Technology.
    7. Hamilton, James D., 1986. "A standard error for the estimated state vector of a state-space model," Journal of Econometrics, Elsevier, vol. 33(3), pages 387-397, December.
    8. Zalewska-Mitura, Anna & Hall, Stephen G., 1999. "Examining the first stages of market performance: a test for evolving market efficiency," Economics Letters, Elsevier, vol. 64(1), pages 1-12, July.
    9. Meulbroek, Lisa K, 1992. " An Empirical Analysis of Illegal Insider Trading," Journal of Finance, American Finance Association, vol. 47(5), pages 1661-99, December.
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