IDEAS home Printed from https://ideas.repec.org/p/rug/rugwps/16-918.html
   My bibliography  Save this paper

Estimation Of Star-Garch Models With Iteratively Weighted Least Squares

Author

Listed:
  • Murat Midilic

    ()

Abstract

This study applies the Iteratively Weighted Least Squares (IWLS) algorithm to a Smooth Transition Autoregressive (STAR) model with conditional variance. Monte Carlo simulations are performed to measure the performance of the algorithm, to compare its performance with the performances of established methods in the literature, and to see the effect of initial value selection method. Simulation results show that low bias and mean squared error are received for the slope parameter estimator from the IWLS algorithm when the real value of the slope parameter is low. In an empirical illustration, STAR-GARCH model is used to forecast daily US Dollar/Australian Dollar and FTSE Small Cap index returns. 1-day ahead out-of-sample forecast results show that forecast performance of the STAR-GARCH model improves with the IWLS algorithm and the model performs better that the benchmark model.

Suggested Citation

  • Murat Midilic, 2016. "Estimation Of Star-Garch Models With Iteratively Weighted Least Squares," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/918, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:16/918
    as

    Download full text from publisher

    File URL: http://wps-feb.ugent.be/Papers/wp_16_918.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
    2. Rech, Gianluigi & Teräsvirta, Timo & Tschernig, Rolf, 1999. "A simple variable selection technique for nonlinear models," SSE/EFI Working Paper Series in Economics and Finance 296, Stockholm School of Economics, revised 06 Apr 2000.
    3. Stefan Reitz & Frank Westerhoff, 2007. "Commodity price cycles and heterogeneous speculators: a STAR–GARCH model," Empirical Economics, Springer, vol. 33(2), pages 231-244, September.
    4. Mika Meitz & Pentti Saikkonen, 2008. "Stability of nonlinear AR-GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 453-475, May.
    5. Mak, T. K. & Wong, H. & Li, W. K., 1997. "Estimation of nonlinear time series with conditional heteroscedastic variances by iteratively weighted least squares," Computational Statistics & Data Analysis, Elsevier, vol. 24(2), pages 169-178, April.
    6. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    7. Jean‐Thomas Bernard & Jean‐Marie Dufour & Lynda Khalaf & Maral Kichian, 2012. "An identification‐robust test for time‐varying parameters in the dynamics of energy prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(4), pages 603-624, June.
    8. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    9. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    10. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    11. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    12. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    13. Felix Chan & Michael McAleer, 2003. "Estimating smooth transition autoregressive models with GARCH errors in the presence of extreme observations and outliers," Applied Financial Economics, Taylor & Francis Journals, vol. 13(8), pages 581-592.
    14. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    15. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    16. He, Changli & Terasvirta, Timo, 1999. "Properties of moments of a family of GARCH processes," Journal of Econometrics, Elsevier, vol. 92(1), pages 173-192, September.
    17. Barry K. Goodwin & Matthew T. Holt & Jeffrey P. Prestemon, 2011. "North American Oriented Strand Board Markets, Arbitrage Activity, and Market Price Dynamics: A Smooth Transition Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(4), pages 993-1014.
    18. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    19. Frauke Schleer, 2015. "Finding Starting-Values for the Estimation of Vector STAR Models," Econometrics, MDPI, Open Access Journal, vol. 3(1), pages 1-26, January.
    20. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    21. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    22. Brooks, Chris & Burke, Simon P. & Persand, Gita, 2001. "Benchmarks and the accuracy of GARCH model estimation," International Journal of Forecasting, Elsevier, vol. 17(1), pages 45-56.
    23. Novella Maugeri, 2014. "Some Pitfalls in Smooth Transition Models Estimation: A Monte Carlo Study," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 339-378, October.
    24. A. Stan Hurn & Annastiina Silvennoinen & Timo Teräsvirta, 2016. "A Smooth Transition Logit Model of The Effects of Deregulation in the Electricity Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 707-733, June.
    25. Stefan Lundbergh & Timo Teräsvirta, 1999. "Modelling Economic High-Frequency Time Series," Tinbergen Institute Discussion Papers 99-009/4, Tinbergen Institute.
    26. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    27. Busetti, Fabio & Marcucci, Juri, 2013. "Comparing forecast accuracy: A Monte Carlo investigation," International Journal of Forecasting, Elsevier, vol. 29(1), pages 13-27.
    28. Chan, Felix & Theoharakis, Billy, 2011. "Estimating m-regimes STAR-GARCH model using QMLE with parameter transformation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1385-1396.
    29. Felix Chan & Michael McAleer, 2002. "Maximum likelihood estimation of STAR and STAR-GARCH models: theory and Monte Carlo evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 509-534.
    30. Nakatsuma, Teruo, 2000. "Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach," Journal of Econometrics, Elsevier, vol. 95(1), pages 57-69, March.
    31. Banz, Rolf W., 1981. "The relationship between return and market value of common stocks," Journal of Financial Economics, Elsevier, vol. 9(1), pages 3-18, March.
    32. He, Changli & Ter svirta, Timo, 1999. "FOURTH MOMENT STRUCTURE OF THE GARCH(p,q) PROCESS," Econometric Theory, Cambridge University Press, vol. 15(06), pages 824-846, December.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    STAR; GARCH; iteratively weighted least squares; Australian Dollar; FTSE;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • F31 - International Economics - - International Finance - - - Foreign Exchange

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rug:rugwps:16/918. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nathalie Verhaeghe). General contact details of provider: http://edirc.repec.org/data/ferugbe.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.