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A flexible parametric GARCH model with an application to exchange rates

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Author Info

  • Kai-Li Wang

    (Department of International Trade, Tam Kang University, Taiwan)

  • Christopher Fawson

    (Department of Economics, 3530 Old Main Hill, Utah State University, Logan, UT 84322)

  • Christopher B. Barrett

    (Department of Applied Economics and Management, 351 Warren Hall, Cornell University, Ithaca, NY 14853-7801, USA)

  • James B. McDonald

    (Department of Economics, 130, FOB, Brigham Young University, Provo, Utah 84602, USA)

Abstract

Many asset prices, including exchange rates, exhibit periods of stability punctuated by infrequent, substantial, often one-sided adjustments. Statistically, this generates empirical distributions of exchange rate changes that exhibit high peaks, long tails, and skewness. This paper introduces a GARCH model, with a flexible parametric error distribution based on the exponential generalized beta (EGB) family of distributions. Applied to daily US dollar exchange rate data for six major currencies, evidence based on a comparison of actual and predicted higher-order moments and goodness-of-fit tests favours the GARCH-EGB2 model over more conventional GARCH-t and EGARCH-t model alternatives, particularly for exchange rate data characterized by skewness. Copyright © 2001 John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 16 (2001)
Issue (Month): 4 ()
Pages: 521-536

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Handle: RePEc:jae:japmet:v:16:y:2001:i:4:p:521-536

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References

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Citations

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Cited by:
  1. C. James Hueng & Ruey Yau, 2006. "Investor preferences and portfolio selection: is diversification an appropriate strategy?," Quantitative Finance, Taylor and Francis Journals, vol. 6(3), pages 255-271.
  2. Kai-Li Wang & Mei-Ling Chen, 2007. "The dynamics in the spot, futures, and call options with basis asymmetries: an intraday analysis in a generalized multivariate GARCH-M MSKST framework," Review of Quantitative Finance and Accounting, Springer, vol. 29(4), pages 371-394, November.
  3. Luc Bauwens & Arie Preminger & Jeroen V.K. Rombouts, 2006. "Regime switching GARCH models," Cahiers de recherche 06-08, HEC Montréal, Institut d'économie appliquée.
  4. Leonidas Tsiaras, 2010. "Dynamic Models of Exchange Rate Dependence Using Option Prices and Historical Returns," CREATES Research Papers 2010-35, School of Economics and Management, University of Aarhus.
  5. Fukuhara, Masahiro & Saruwatari, Yasufumi, 2003. "An Analysis of Contagion in Emerging Currency Markets Using Multivariate Extreme Value Theory," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 21(2), pages 113-131, August.
  6. Emese Lazar & Carol Alexander, 2006. "Normal mixture GARCH(1,1): applications to exchange rate modelling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 307-336.
  7. Park, Sung Y. & Bera, Anil K., 2009. "Maximum entropy autoregressive conditional heteroskedasticity model," Journal of Econometrics, Elsevier, vol. 150(2), pages 219-230, June.
  8. Yi-Ting Chen, 2008. "A unified approach to standardized-residuals-based correlation tests for GARCH-type models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 111-133.
  9. Hueng, C. James & McDonald, James B., 2005. "Forecasting asymmetries in aggregate stock market returns: Evidence from conditional skewness," Journal of Empirical Finance, Elsevier, vol. 12(5), pages 666-685, December.
  10. Ramirez, Octavio A. & Fadiga, Mohamadou L., 2003. "Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(01), April.
  11. Henrik Amilon, 2003. "Estimation of an Adaptive Stock Market Model with Heterogeneous Agents," Research Paper Series 107, Quantitative Finance Research Centre, University of Technology, Sydney.
  12. Zhang, Rongmao & Peng, Liang & Qi, Yongcheng, 2012. "Jackknife-blockwise empirical likelihood methods under dependence," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 56-72, February.

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