Real-Time Market Abuse Detection with a Stochastic Parameter Model

• Autorzy: Radosław Cholewiński
• Języki publikacji: EN

Abstrakty

EN

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. (original abstract)

Słowa kluczowe

EN

GARCH model; Stochastic models; Economic frauds

PL

Model GARCH; Modele stochastyczne; Nadużycia gospodarcze

• Czasopismo: Central European Journal of Economic Modelling and Econometrics (CEJEME)
• Rocznik: 2009
• Tom: 1
• Numer: nr 3
• Strony: 261--284

Twórcy

autor

Radosław Cholewiński

• Noble Bank, Poland

Bibliografia

• [1] Alexander C., (2001), Market models, John Wiley and Sons, Chichester.
• [2] Chou R., Engle R. F., Kane A., (1992), Measuring risk aversion from excess returns on a stock index, Journal of Econometrics 52.
• [3] Dubow B., Monteiro N., (2006), Measuring market cleanliness, Financial Securities Authority Occasional Papers 23.
• [4] Efron B., (1987), The jackknife, the bootstrap, and other resampling plans, CBMS-NSF Regional Conference Series in Applied Mathematics 38.
• [5] Emerson R., Hall S. G., Zalewska-Mitura A., (1997), Evolving market efficiency with an application to some Bulgarian shares, Economics of Planning 30, 75-90.
• [6] Engle R. F., (2000), The econometrics of ultra-high-frequency data, Econometrica 68, 1-22.
• [7] Engle R. F., Kroner K. F., (1995), Multivariate Simultaneous Generalized ARCH, Econometric Theory 11, 122-150.
• [8] Hamilton J. D., (1986), A standard error for the estimated state vector of a state-space model, Journal of Econometrics 33, 387-397.
• [9] Hamilton J. D., (1994), Time Series Analysis, Princeton University Press.
• [10] Meulbroek L., (1992), An empirical analysis of illegal insider trading, Journal of Finance 47, 1661-1700.
• [11] Minenna M., (2003), The detection of market abuse on financial markets: A quantitative approach, Quaderni Di Finanza 54.
• [12] Mitchell M. L., Netter J. M., (1994), The role of financial economics in securities fraud cases: Applications at the securities and exchange commission, The Business Lawyer 40, 545-590.
• [13] Monteiro N., Zaman Q., Leitterstorf S., (2007), Updated measurement of market cleanliness, Financial Securities Authority Occasional Papers 25.
• [14] Rockinger M., Urga G., (2000), The evolution of stock markets in transition economies, Journal of Comparitive Economics 28, 456-472.
• [15] Watson M. W., (1989), Recursive solution methods for dynamic linear rational expectations models, Journal of Econometrics 41, 65-89.
• [16] Worthington A. C., Higgs H., (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.
• [17] Zalewska-Mitura A., Hall S. G. (1999), Examining the first stages of market performance: a test for evolving market efficiency, Economic Letters 64, 1-12.

Identyfikatory

DOI

10.24425/cejeme.2009.12235