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Real-Time Market Abuse Detection with a Stochastic Parameter Model

Author

Listed:
  • Radosław Cholewiński

    (Noble Bank)

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.

Suggested Citation

  • Radosław Cholewiński, 2009. "Real-Time Market Abuse Detection with a Stochastic Parameter Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 1(3), pages 261-284, November.
    • Handle: RePEc:psc:journl:v:1:y:2009:i:3:p:261-284
    as

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    References listed on IDEAS

    as
    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    2. 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.
    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. Meulbroek, Lisa K, 1992. "An Empirical Analysis of Illegal Insider Trading," Journal of Finance, American Finance Association, vol. 47(5), pages 1661-1699, December.
    5. Robert F. Engle, 2000. "The Econometrics of Ultra-High Frequency Data," Econometrica, Econometric Society, vol. 68(1), pages 1-22, January.
    6. 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.
    7. Watson, Mark W., 1989. "Recursive solution methods for dynamic linear rational expectations models," Journal of Econometrics, Elsevier, vol. 41(1), pages 65-89, May.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Market abuse detection; insider trading; real-time analysis; timevarying parameters; uni- and bivariate GARCH processes;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G19 - Financial Economics - - General Financial Markets - - - Other

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