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Accounting for conditional leptokurtosis and closing days effects in FIGARCH models of daily exchange rates


  • Michel Beine
  • Sebastien Laurent
  • Christelle Lecourt


This paper, estimates FIGARCH models introduced by Baillie et al. (1996a) for the four major daily exchange rates against the USD (DEM, FRF, YEN and the GBP). The former contributions are extended by accounting for the observed kurtosis through a Student- t based maximum likelihood estimation and by including variables capturing the effect of closing days. These estimations suggest that the introduction of these features improves the goodness of fit properties of the model on the one hand, and may lead to different interest parameters estimates on the other hand. In particular, it is shown that in the case of the DEM, volatility shocks may display much less persistence than documented by previous studies. Finally, it is shown that an ARFIMA-FIGARCH framework turns out to be relevant for all the currencies (except the GBP), without inducing any significant changes in the inference of the stochastic volatility process.

Suggested Citation

  • Michel Beine & Sebastien Laurent & Christelle Lecourt, 2002. "Accounting for conditional leptokurtosis and closing days effects in FIGARCH models of daily exchange rates," Applied Financial Economics, Taylor & Francis Journals, vol. 12(8), pages 589-600.
  • Handle: RePEc:taf:apfiec:v:12:y:2002:i:8:p:589-600 DOI: 10.1080/09603100010014041

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    References listed on IDEAS

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    3. Beine, Michel & Laurent, Sebastien, 2003. "Central bank interventions and jumps in double long memory models of daily exchange rates," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 641-660, December.
    4. Wu, Ping-Tsung & Shieh, Shwu-Jane, 2007. "Value-at-Risk analysis for long-term interest rate futures: Fat-tail and long memory in return innovations," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 248-259, March.
    5. Michel Beine & Sébastien Laurent, 2000. "Structural change and long memory in volatility: new evidence from daily exchange rates," ULB Institutional Repository 2013/10473, ULB -- Universite Libre de Bruxelles.
    6. Deniz Erdemlioglu & Sébastien Laurent & Christopher J. Neely, 2013. "Econometric modeling of exchange rate volatility and jumps," Chapters,in: Handbook of Research Methods and Applications in Empirical Finance, chapter 16, pages 373-427 Edward Elgar Publishing.
    7. Algieri, Bernardina, 2014. "The influence of biofuels, economic and financial factors on daily returns of commodity futures prices," Energy Policy, Elsevier, vol. 69(C), pages 227-247.
    8. Han, Young Wook, 2007. "High frequency perspective on jump process, long memory property and temporal aggregation: Case of $-AUD exchange rates," Japan and the World Economy, Elsevier, vol. 19(2), pages 248-262, March.
    9. Beine, Michel & Benassy-Quere, Agnes & Lecourt, Christelle, 2002. "Central bank intervention and foreign exchange rates: new evidence from FIGARCH estimations," Journal of International Money and Finance, Elsevier, vol. 21(1), pages 115-144, February.
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    11. Thompson, James R. & Wilson, James R., 2016. "Multifractal detrended fluctuation analysis: Practical applications to financial time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 126(C), pages 63-88.
    12. Sang Hoon Kang & Seong-Min Yoon, 2009. "Value-at-Risk Analysis for Asian Emerging Markets: Asymmetry and Fat Tails in Returns Innovation," Korean Economic Review, Korean Economic Association, vol. 25, pages 387-411.
    13. Ken Johnston & David Carter & John Hatem, 2005. "Exchange rates, and fundamental variables: a semi-parametric analysis of binary choice," Applied Economics, Taylor & Francis Journals, vol. 37(16), pages 1915-1924.
    14. Michel Beine & Agnès Bénassy-Quéré & Christelle Lecourt, 1999. "The Impact of Foreign Exchange Interventions: New Evidence from FIGARCH Estimations," Working Papers 1999-14, CEPII research center.
    15. Karanasos, Menelaos & Kim, Jinki, 2006. "A re-examination of the asymmetric power ARCH model," Journal of Empirical Finance, Elsevier, vol. 13(1), pages 113-128, January.
    16. Agnieszka Jach & Piotr Kokoszka, 2010. "Empirical wavelet analysis of tail and memory properties of LARCH and FIGARCH models," Computational Statistics, Springer, vol. 25(1), pages 163-182, March.
    17. Su, Jung-Bin & Hung, Jui-Cheng, 2011. "Empirical analysis of jump dynamics, heavy-tails and skewness on value-at-risk estimation," Economic Modelling, Elsevier, vol. 28(3), pages 1117-1130, May.
    18. Kang, Sang Hoon & Yoon, Seong-Min, 2007. "Long memory properties in return and volatility: Evidence from the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 591-600.
    19. Trino-Manuel Ñíguez, 2003. "Volatility And Var Forecasting For The Ibex-35 Stock-Return Index Using Figarch-Type Processes And Different Evaluation Criteria," Working Papers. Serie AD 2003-33, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).

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