Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2016 | 8 | nr 1 | 1--20
Tytuł artykułu

The UHF-GARCH-Type Model in the Analysis of Intraday Volatility and Price Durations - the Bayesian Approach

Treść / Zawartość
Warianty tytułu
Języki publikacji
In empirical research on financial market microstructure and in testing some predictions from the market microstructure literature, the behavior of some characteristics of trading process can be very important and useful. Among all characteristics associated with tick-by-tick data, the trading time and the price seem the most important. The very first joint model for prices and durations, the so-called UHF-GARCH, has been introduced by Engle (2000). The main aim of this paper is to propose a simple, novel extension of Engle's specification based on trade-to-trade data and to develop and apply the Bayesian approach to estimation of this model. The intraday dynamics of the return volatility is modelled by an EGARCH-type specification adapted to irregularly time-spaced data. In the analysis of price durations, the Box-Cox ACD model with the generalized gamma distribution for the error term is considered. To the best of our knowledge, the UHF-GARCH model with such a combination of the EGARCH and the Box-Cox ACD structures has not been studied in the literature so far. To estimate the model, the Bayesian approach is adopted. Finally, the methodology developed in the paper is employed to analyze transaction data from the Polish Stock Market.(original abstract)
Opis fizyczny
  • Cracow University of Economics, Poland
  • [1] Allen D., Chan F., McAleer M., Peiris S., (2008), Finite sample properties of the QMLE for the Log-ACD model: application to Australian stocks, Journal of Econometrics 147, 163-185.
  • [2] Bauwens L., Giot P., (2000), The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks, Annales d'Économie et de Statistique 60, 117-149.
  • [3] Bauwens L., Giot P., (2001), Econometric Modelling of Stock Market Intraday Activity, Kluwer Academic Publishers, Boston.
  • [4] Bauwens L., Giot P., (2003), Asymmetric ACD models: introducing price information in ACD models, Empirical Economics 28, 709-731.
  • [5] Bauwens L., Giot P., Grammig J., Veredas D., (2004), A comparison of financial duration models via density forecast, International Journal of Forecasting 20, 589-609.
  • [6] 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.
  • [7] Bień K., (2004), Zastosowanie modeli Ultra-High-Frequency GARCH do analizy zmienności szeregów czasowych o bardzo dużej częstotliwości (UHF-GARCH Models: an Application to the Volatility Analysis of Ultra-High-Frequency Data, In Polish), Prace Naukowe Akademii Ekonomicznej we Wrocławiu, 1037, 38-47.
  • [8] Bień K., (2006), Zaawansowane specyfikacje modeli ACD - prezentacja oraz przykład zastosowania, Przegląd Statystyczny 53(1), 90-107.
  • [9] Bień K., (2006a), Model ACD - podstawowa specyfikacja i przykład zastosowania, Przegląd Statystyczny 53(3), 83-97.
  • [10] Bień-Barkowska K., (2011), Distribution Choice for the Asymmetric ACD Models, Dynamic Econometric Models 11, 55-72.
  • [11] Bień-Barkowska K., (2012), A Bivariate Copula-based Model for a Mixed Binary-Continuous Distribution: A Time Series Approach, Central European Journal of Economic Modelling and Econometrics 4, 117-142.
  • [12] Bień-Barkowska K., (2014), Explaining Liquidity Dynamics in the Order Driven FX Spot Market, Przegląd Statystyczny 61(3), 223-243.
  • [13] Bień-Barkowska K., (2014a), Capturing Order Book Dynamics in the Interbank EUR/PLN Spot Market, Emerging Markets Finance and Trade 50, 93-117.
  • [14] De Luca G.D., Gallo G., (2009), Time-varying mixing weights in mixture autoregressive conditional duration models, Economic Review 28, 101-120.
  • [15] De Luca G.D., Zuccolotto P., (2006), Regime-switching Pareto distributions for ACD models, Computational Statistics and Data Analysis 51, 2179-2191.
  • [16] Doman M., (2011), Mikrostruktura giełd papierów wartościowych, Poznań University of Economics Press, Poznań.
  • [17] Doman M., Doman R., (2010), Dependencies between price duration, volatility, volume and return on the Warsaw Stock Exchange, Journal of Modern Accounting and Auditing 6, 27-38.
  • [18] Dufour A., Engle R.F., (2000), The ACD model: predictability of the time between consecutive trades, Discussion paper, ISMA Centre, University of Reading.
  • [19] Easley D., O'Hara M., (1992), Time and the process of security price adjustment, Journal of Finance 47, 577-606.
  • [20] Engle R.F., (2000), The econometrics of ultra-high-frequency Data, Econometrica 68, 1-22.
  • [21] Engle R.F., Russell J.R., (1998), Autoregressive conditional duration: a new model for irregularly spaced transaction data, Econometrica 66, 1127-1162.
  • [22] Fernandes M., Grammig J., (2006), A family of autoregressive conditional duration models, Journal of Econometrics 130, 1-23.
  • [23] Ghysels E., Jasiak J., (1998), GARCH for irregularly spaced financial data: the ACD-GARCH model, Studies in Nonlinear Dynamics and Econometrics 2, 133-149.
  • [24] Grammig J., Maurer K., (2000), Non-monotonic hazard functions and the autoregrssive conditional duration model, Econometrics Journal, 3, 16-38.
  • [25] Hastings W.K., (1970), Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97-109.
  • [26] Hautsch N., (2002), Modelling intraday trading activity using Box-Cox ACD models, Discussion Paper 02/05, CoFE, University of Konstanz.
  • [27] Hautsch N., (2004), Modelling Irregularly Spaced Financial Data - Theory and Practice of Dynamic Duration Models, Lecture Notes in Economics and Mathematical Systems 539, Springer, Berlin.
  • [28] Hautsch N., (2012), Econometrics of Financial High-Frequency Data, Springer-Verlag, Berlin Heidelberg.
  • [29] Huptas R., (2009), Intraday Seasonality in Analysis of UHF Financial Data: Models and Their Empirical Verification, Dynamic Econometric Models 9, 129-138.
  • [30] Huptas R., (2014), Bayesian Estimation and Prediction for ACD Models in the Analysis of Trade Durations from the Polish Stock Market, Central European Journal of Economic Modelling and Econometrics 6, 237-273.
  • [31] Liu C., Maheu J.M., (2012), Intraday Dynamics of Volatility and Duration: Evidence from Chinese Stocks, Pacific-Basin Finance Journal 20, 329-348.
  • [32] Lunde A., (1999), A generalized gamma autoregressive conditional duration model, Discussion paper, Alborg University.
  • [33] Manganelli S., (2005), Duration, volume and volatility impact of trades, Journal of Financial Markets 8, 377-399.
  • [34] Meddahi N., Renault E., Werker B., (2006), GARCH and Irregularly Spaced Data, Economics Letters 90, 200-204.
  • [35] Osiewalski J., (2001), Ekonometria bayesowska w zastosowaniach, Cracow University of Economics Press, Kraków.
  • [36] Roll R., (1984), A Simple Implicit Measure of the Bid-Ask Spread in an Efficient Market, Journal of Finance 39, 1127-1139.
  • [37] Yu B., Mykland P., (1994), Looking at Markov samplers through CUMSUM paths plots: a simple diagnostic idea, Technical Report 413, Department of Statistics, University of Carolina at Berkeley.
  • [38] Zhang M., 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.
  • [39] Zellner A., (1971), An Introduction to Bayesian Inference in Econometrics, John Wiley, New York.
Typ dokumentu
Identyfikator YADDA

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.