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Testing the Conditional Mean Function of Autoregressive Conditional Duration Models

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  • Nikolaus Hautsch

    (Department of Economics, University of Copenhagen)

Abstract

This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. A categorization of the durations allows us to reformulate the PH model as an ordered response model based on extreme value distributed errors. In order to capture persistent serial dependence in the duration process, we extend the model by an observation driven ARMA dynamic based on generalized errors. We illustrate the maximum likelihood estimation of both the model parameters and discrete points of the underlying unspecified baseline survivor function. The dynamic properties of the model as well as an assessment of the estimation quality is investigated in a Monte Carlo study. It is illustrated that the model is a useful approach to estimate conditional failure probabilities based on (persistent) serial dependent duration data which might be subject to censoring structures. In an empirical study based on financial transaction data we present an application of the model to estimate conditional asset price change probabilities. Evaluating the forecasting properties of the model, it is shown that the proposed approach is a promising competitor to well-established ACD type models.

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Bibliographic Info

Paper provided by University of Copenhagen. Department of Economics. Finance Research Unit in its series FRU Working Papers with number 2006/06.

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Length: 21 pages
Date of creation: Dec 2006
Date of revision:
Handle: RePEc:kud:kuiefr:200606

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Related research

Keywords: augmented ACD models; semiparametric ACD models; news impact function; Lagrange multiplier tests;

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Cited by:
  1. Nikolaus Hautsch, 2007. "Capturing Common Components in High-Frequency Financial Time Series: A Multivariate Stochastic Multiplicative Error Model," SFB 649 Discussion Papers SFB649DP2007-052, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  2. Nikolaus Hautsch & Vahidin Jeleskovic, 2008. "Modelling High-Frequency Volatility and Liquidity Using Multiplicative Error Models," SFB 649 Discussion Papers SFB649DP2008-047, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  3. Allen, David & Chan, Felix & McAleer, Michael & Peiris, Shelton, 2008. "Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks," Journal of Econometrics, Elsevier, vol. 147(1), pages 163-185, November.
  4. Yongmiao Hong & Yoon-Jin Lee, 2007. "Detecting Misspecifications in Autoregressive Conditional Duration Models," Caepr Working Papers, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington 2007-019, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
  5. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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