Distribution Choice for the Asymmetric ACD Models

• Autorzy: Katarzyna Bień-Barkowska

Warianty tytułu

Wybór rozkładu składnika losowego w asymetrycznych modelach ACD

• Języki publikacji: EN

Abstrakty

EN

In the paper, I generalize the Asymmetric Autoregressive Conditional Duration (AACD) model proposed by Bauwens and Giot (2003) with respect to the generalized gamma and the Burr distribution for an error term. I derive the log likelihood functions for the augmented models and show how to check the goodness-of-fit of the distributional assumptions with the application of the probability integral transforms proposed by Diebold, Gunther and Tay (1998). Moreover, I present an exemplary empirical application of the Asymmetric ACD model for the durations between submissions of market or best limit orders on the interbank trading platform for the Polish zloty. I test the impact of selected market microstructure factors (i.e. the bid-ask spread, volatility) on the time of order submissions. (original abstract)

W artykule dokonano uogólnienia asymetrycznego modelu ACD, zaproponowanego w pracy (Bauwens, Giot, 2003) w odniesieniu do nowych rozkładów składnika losowego: rozkładu Burra i uogólnionego rozkładu gamma. Wyprowadzono funkcję wiarygodności dla rozszerzonych specyfikacji i przedstawiono procedurę testowania jakości dopasowania modeli za pomocą transformat gęstości (Diebold i in., 1998). Dodatkowo, przedstawiono przykładową aplikację asymetrycznych modeli ACD w odniesieniu do odstępów czasu (tzw. czasów trwania) pomiędzy momentami, w których składane są zlecenia z limitem ceny lub zlecenia rynkowe na kierowanym zleceniami międzybankowym kasowym rynku złotego. Dokonano weryfikacji wpływu dwóch czynników mikrostruktury rynku (spreadu bid-ask i zmienności) na tempo składania wyróżnionych typów zleceń. (abstrakt oryginalny)

Słowa kluczowe

EN

Econometric models; Time-series analysis; ACD model (Autoregressive conditional duration)

PL

Modele ekonometryczne; Analiza szeregów czasowych; Model ACD (Autoregresyjna duracja warunkowa)

• Czasopismo: Dynamic Econometric Models
• Rocznik: 2011
• Tom: 11
• Strony: 55--72

Twórcy

autor

Katarzyna Bień-Barkowska

• Warsaw School of Economics, Poland; National Bank of Poland

Bibliografia

• Bauwens, L., Giot, P. (2000), The Logarithmic ACD Model: An Application to the Bid/Ask Quote Process of two NYSE Stocks, Annales d'Economie et de Statistique, 60, 117-149.
• Bauwens, L., Giot, P. (2001), Econometric Modelling of Stock Market Intraday Activity, Kluwer Academic Publishers, Boston.
• Bauwens, L., Giot, P. (2003), Asymmetric ACD Models: Introducing Price Information in ACD Models, Empirical Economics, 28, 709-731.
• Bauwens, L., Giot, P., Grammig, J., Veredas, D. (2004), A Comparison of Financial Duration Models via Density Forecasts, International Journal of Forecasting 20, 589-609.
• Bauwens, L., Veredas, D. (2004), The Stochastic Conditional Duration Model: A Latent Variable Model for the Analysis of Financial Durations, Journal of Econometrics 119, 381-412.
• Biais, B., Hillion, P., Spatt, C. (1995), An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse, Journal of Finance 50, 1655-1689.
• Bień, K. (2006), Advanced ACD Models - Presentation and the Example of Application, Statistical Review 53 (1), 90-108.
• Diebold, F.X., Gunther, T.A., Tay, A.S. (1998), Evaluating Density Forecasts with Applications to Financial Risk Management, International Economic Review 39, 863-883.
• Ellul, A., Holden C., Jain, P., Jennings, R. (2007), Order Dynamics: Recent Evidence from the NYSE, Journal of Empirical Finance, 14, 636-661.
• Engle, R.F., Russell, J.R. (1998), Autoregressive Conditional Duration; a New Approach for Irregularly Spaced Transaction Data, Econometrica 66, 1127-1162.
• Foucault, T. (1999), Order Flow Decomposition and Trading Costs in a Dynamic Limit Order Market, Journal of Financial Markets 2, 99-134.
• Ghysels, E., Jasiak, J. (1998), GARCH for Irregularly Spaced Financial Data: The ACD-GARCH Model, Studies in Nonlinear Dynamics and Econometrics 2, 133-149.
• Giot, P. (2000), Time Transformations, Intraday Data and Volatility Models, Journal of Computational Finance, 4(2), 31-62.
• Giot, P. (2005), Market Risk Models for Intraday Data, European Journal of Finance, 11, 309-324.
• Gouriéroux, C., Jasiak, J. (2001), Financial Econometrics: Problems, Models and Methods, Princeton University Press.
• Grammig, J., Maurer, K. (2000), Non-monotonic Hazard Functions and the Autoregressive Conditional Duration Model, Econometrics Journal 3, 16-38.
• Grammig, J., Wellner, M. (2002), Modeling the Interdependence of Volatility and Inter-Transaction Duration Process, Journal of Econometrics, 106, 369-400.
• Hautsch, N. (2001), The Generalized F ACD Model, Discussion Paper, University of Konstanz.
• Hautsch, N. (2004), Modelling Irregularly Spaced Financial Data, Springer, Berlin.
• Kalimipalli, M., Warga, A. (2002), Bid-Ask Spread, Volatility and Volume in the Corporate Bond Market, Journal of Fixed Income, 3, 31-42.
• Lo, I., Sapp, S. (2008), The Submission of Limit Orders or Market Orders: The Role of Timing and Information in the Reuters D2000-2 System, Journal of International Money and Finance, 27, 1056-1073.
• Nolte, I. (2008), Modeling a Multivariate Trading Process, Journal of Financial Econometrics, 6, 143-170.
• Osińska, M. (2006), Financial Econometrics, PWE, Warszawa.
• Pacurar. M. (2008), Autoregressive Conditional Duration Models in Finance: A Survey of Theoretical and Empirical Literature, Journal of Economic Surveys, 22 (4), 711-751.
• Ranaldo, A. (2004), Order Aggresiveness in Limit Order Book Markets, Journal of Financial Markets, 7, 53-74.
• Tsay, R. S. (2002), Analysis of Financial Time Series, Wiley: New York.
• Verhoeven, P., Ching, S., Ng, H. (2003), Determinants of the Decision to Submit Market or Limit Orders on the ASX, Pacific-Basin Finance Journal, 12, 1-18.
• Zhang, M. Y., Russell, J. R., Tsay, R. S. (2001), A Nonlinear Autoregressive Conditional Duration Model with Applications to Financial Transaction Data, Journal of Econometrics 104, 179-207.

Identyfikatory

DOI

10.12775/DEM.2011.004