IDEAS home Printed from https://ideas.repec.org/a/prg/jnlpol/v2014y2014i1id939p100-116.html
   My bibliography  Save this article

Neparametrický heuristický přístup k odhadu modelu GARCH-M a jeho výhody
[Estimating a GARCH-M Model by a Non-Parametric Heuristic Method and Its Advantages]

Author

Listed:
  • Jaromír Kukal
  • Tran Van Quang

Abstract

The models from the GARCH family are often estimated by maximum likelihood method, either parametrically or non-parametrically. Since the parametric estimation procedure is based on an a priori distribution, its misspecification can lead to the inconsistency of the estimators. Therefore non-parametric approach, in which both model's parameters and the distribution of error terms are estimated from the data, seems to be a better alternative. In our work, we propose a non-parametric technique with the use of a heuristic called differential evolution to estimate the parameters of a GARCH-M model. This technique can more likely reach to a global solution of maximum likelihood estimation (MLE) task. Further, it can also more effectively control the required properties of the estimates. The suitability of our approach is verified on modeling the CZK/USD and CZK/EURO forward exchange rate premium of period from 2007 to 2012 by a GARCH-M model.

Suggested Citation

  • Jaromír Kukal & Tran Van Quang, 2014. "Neparametrický heuristický přístup k odhadu modelu GARCH-M a jeho výhody [Estimating a GARCH-M Model by a Non-Parametric Heuristic Method and Its Advantages]," Politická ekonomie, Prague University of Economics and Business, vol. 2014(1), pages 100-116.
  • Handle: RePEc:prg:jnlpol:v:2014:y:2014:i:1:id:939:p:100-116
    DOI: 10.18267/j.polek.939
    as

    Download full text from publisher

    File URL: http://polek.vse.cz/doi/10.18267/j.polek.939.html
    Download Restriction: free of charge

    File URL: http://polek.vse.cz/doi/10.18267/j.polek.939.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.18267/j.polek.939?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hai, Weike & Mark, Nelson C & Wu, Yangru, 1997. "Understanding Spot and Forward Exchange Rate Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(6), pages 715-734, Nov.-Dec..
    2. Baillie, Richard T. & Bollerslev, Tim, 2000. "The forward premium anomaly is not as bad as you think," Journal of International Money and Finance, Elsevier, vol. 19(4), pages 471-488, August.
    3. Engel, Charles, 1996. "The forward discount anomaly and the risk premium: A survey of recent evidence," Journal of Empirical Finance, Elsevier, vol. 3(2), pages 123-192, June.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Rene M. Stulz, 1994. "International Portfolio Choice and Asset Pricing: An Integrative Survey," NBER Working Papers 4645, National Bureau of Economic Research, Inc.
    6. Bekaert, Geert, 1994. "Exchange rate volatility and deviations from unbiasedness in a cash-in-advance model," Journal of International Economics, Elsevier, vol. 36(1-2), pages 29-52, February.
    7. Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
    8. Buhlmann, Peter & McNeil, Alexander J., 2002. "An algorithm for nonparametric GARCH modelling," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 665-683, October.
    9. Mishra, Santosh & Su, Liangjun & Ullah, Aman, 2010. "Semiparametric Estimator of Time Series Conditional Variance," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 256-274.
    10. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    11. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    12. Domowitz, Ian & Hakkio, Craig S., 1985. "Conditional variance and the risk premium in the foreign exchange market," Journal of International Economics, Elsevier, vol. 19(1-2), pages 47-66, August.
    13. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    14. Härdle,Wolfgang, 1992. "Applied Nonparametric Regression," Cambridge Books, Cambridge University Press, number 9780521429504.
    15. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    16. Evžen Koèenda & Tigran Poghosyan, 2010. "Exchange Rate Risk in Central European Countries," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 60(1), pages 22-39, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Josef Arlt & Martin Mandel, 2019. "Determinanty forwardového kurzu a role rizikových prémií (příklad měnových párů czk/eur a czk/usd) [Determinants of Forward Exchange Rate and the Role of Risk Premiums (Case of CZK/EUR and CZK/USD ," Politická ekonomie, Prague University of Economics and Business, vol. 2019(5), pages 476-489.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dutta, Shantanu & Essaddam, Naceur & Kumar, Vinod & Saadi, Samir, 2017. "How does electronic trading affect efficiency of stock market and conditional volatility? Evidence from Toronto Stock Exchange," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 867-877.
    2. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
    3. Belke, Ansgar & Gokus, Christian, 2011. "Volatility Patterns of CDS, Bond and Stock Markets Before and During the Financial Crisis – Evidence from Major Financial Institutions," Ruhr Economic Papers 243, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Köksal, Bülent, 2009. "A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns," MPRA Paper 30510, University Library of Munich, Germany.
    5. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    6. Claudeci Da Silva & Hugo Agudelo Murillo & Joaquim Miguel Couto, 2014. "Early Warning Systems: Análise De Ummodelo Probit De Contágio De Crise Dos Estados Unidos Para O Brasil(2000-2010)," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 110, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    7. Ansgar Belke & Christian Gokus, 2011. "Volatility Patterns of CDS, Bond and Stock Markets Before and During the Financial Crisis – Evidence from Major Financial Institutions," Ruhr Economic Papers 0243, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    8. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    9. Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.
    10. Coelho dos Santos, Marcelo Bittencourt & Klotzle, Marcelo Cabus & Figueiredo Pinto, Antonio Carlos, 2016. "Evidence of risk premiums in emerging market carry trade currencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 103-115.
    11. Ming Chen & Qiongxia Song, 2016. "Semi-parametric estimation and forecasting for exogenous log-GARCH models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 93-112, March.
    12. Mauro Bernardi & Leopoldo Catania, 2014. "The Model Confidence Set package for R," Papers 1410.8504, arXiv.org.
    13. Anders Wilhelmsson, 2006. "Garch forecasting performance under different distribution assumptions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(8), pages 561-578.
    14. Charles, Amélie & Darné, Olivier, 2014. "Volatility persistence in crude oil markets," Energy Policy, Elsevier, vol. 65(C), pages 729-742.
    15. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    16. Bauer, Rob M M J & Nieuwland, Frederick G M C & Verschoor, Willem F C, 1994. "German Stock Market Dynamics," Empirical Economics, Springer, vol. 19(3), pages 397-418.
    17. repec:zbw:rwirep:0243 is not listed on IDEAS
    18. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    19. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    20. Gatfaoui, Hayette, 2013. "Translating financial integration into correlation risk: A weekly reporting's viewpoint for the volatility behavior of stock markets," Economic Modelling, Elsevier, vol. 30(C), pages 776-791.
    21. Papantonis Ioannis & Tzavalis Elias & Agapitos Orestis & Rompolis Leonidas S., 2023. "Augmenting the Realized-GARCH: the role of signed-jumps, attenuation-biases and long-memory effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(2), pages 171-198, April.

    More about this item

    Keywords

    GARCH-M model; Non-parametric method; heuristic; forward risk premium;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • F31 - International Economics - - International Finance - - - Foreign Exchange

    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:prg:jnlpol:v:2014:y:2014:i:1:id:939:p:100-116. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stanislav Vojir (email available below). General contact details of provider: https://edirc.repec.org/data/uevsecz.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.