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Testing the Conditional Mean Function of Autoregressive Conditional Duration Models

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Author Info
Nikolaus Hautsch (Department of Economics, University of Copenhagen)

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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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Publisher 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;

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    Other versions:
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Wing Lon NG, 2004. "Duration and Order Type Clusters," Econometric Society 2004 Australasian Meetings 272, Econometric Society. [Downloadable!]
  2. Frank Gerhard & Nikolaus Hautsch, 2007. "A Dynamic Semiparametric Proportional Hazard Model," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 11(2). [Downloadable!]
  3. Wing Lon NG, 2004. "Duration and Order Type Clusters," Econometric Society 2004 Far Eastern Meetings 730, Econometric Society. [Downloadable!]
  4. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques. [Downloadable!]
    Other versions:
  5. Meitz, Mika & Teräsvirta, Timo, 2004. "Evaluating models of autoregressive conditional duration," Working Paper Series in Economics and Finance 557, Stockholm School of Economics, revised 13 Dec 2004. [Downloadable!]
    Other versions:
  6. Yongmiao Hong & Yoon-Jin Lee, 2007. "Detecting Misspecifications in Autoregressive Conditional Duration Models," Caepr Working Papers 2007-019, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington. [Downloadable!]
  7. Stanislav Anatolyev, 2006. "Dynamic modeling under linear-exponential loss," Working Papers w0092, Center for Economic and Financial Research (CEFIR). [Downloadable!]
    Other versions:
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