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Selecting the Order of an ARCH Model

Author

Listed:
  • Anthony W. Hughes

    (Department of Economics, University of Adelaide)

  • Maxwell L. King

    (Monash University)

  • Kwek Kian Teng

    (Monash University)

Abstract

Since the parameters of an autoregressive conditional heteroskedasticity (ARCH) process must be non-negative, inference on ARCH parameters can be improved by using inequality constrained estimation. In this paper, we extend this principle to the problem of ARCH lag order selection. We show that in the case of AIC, the appropriate adjustment to the penalty function has a simple form.

Suggested Citation

  • Anthony W. Hughes & Maxwell L. King & Kwek Kian Teng, 1999. "Selecting the Order of an ARCH Model," School of Economics and Public Policy Working Papers 1999-01, University of Adelaide, School of Economics and Public Policy.
    • Handle: RePEc:adl:wpaper:1999-01
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    File URL: https://media.adelaide.edu.au/economics/papers/doc/wp1999-01.pdf
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    Cited by:

    1. is not listed on IDEAS
    2. Rinke Saskia & Sibbertsen Philipp, 2016. "Information criteria for nonlinear time series models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 325-341, June.
    3. Manera, Matteo & Nicolini, Marcella & Vignati, Ilaria, 2016. "Modelling futures price volatility in energy markets: Is there a role for financial speculation?," Energy Economics, Elsevier, vol. 53(C), pages 220-229.

    More about this item

    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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