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Bivariate FIGARCH and Fractional Cointegration

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Author Info
Celso Brunetti (University of Edinburgh)
Christopher L. Gilbert (Queen Mary and Westfield College, University of London)

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Abstract

We consider the modelling of volatility on closely related markets. Univariate fractional volatility (FIGARCH) models are now standard, as are multivariate GARCH models. In this paper we adopt a combination of the two methodologies. There is as yet little consensus on the methodology for testing for fractional cointegration. The contribution of this paper is to demonstrate the feasibility of estimating and testing cointegrated bivariate FIGARCH models. We apply these methods to volatility on the NYMEX and IPE crude oil markets. We find a common order of fractional integration for the two volatility processes and confirm that they are fractionally cointegrated. An estimated error correction FIGARCH model indicates that the preponderant adjustment is of the IPE towards NYMEX.

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Paper provided by Queen Mary, University of London, Department of Economics in its series Working Papers with number 408.

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Date of creation: Dec 1999
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Handle: RePEc:qmw:qmwecw:wp408

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Keywords: FIGARCH Fractional Cointegration ECM

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Find related papers by JEL classification:
G0 - Financial Economics - - General
C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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  1. Jonathan Dark, 2004. "Bivariate error correction FIGARCH and FIAPARCH models on the Australian All Ordinaries Index and its SPI futures," Monash Econometrics and Business Statistics Working Papers 4/04, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  2. Rehim Kiliç, 2007. "Conditional Volatility and Distribution of Exchange Rates: GARCH and FIGARCH Models with NIG Distribution," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 11(3), pages 1430-1430. [Downloadable!] (restricted)
  3. Federico Bandi & Benoit Perron, 2003. "Long memory and the relation between implied and realized volatility," Econometrics 0305004, EconWPA. [Downloadable!]
  4. Alfonso Mendoza, 2004. "Modelling Long Memory and Risk Premia in Latin American Sovereign Bond Markets," Econometrics 0410004, EconWPA. [Downloadable!]
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  5. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109. [Downloadable!]
  6. John Maheu, 2005. "Can GARCH Models Capture Long-Range Dependence?," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 9(4), pages 1269-1269. [Downloadable!] (restricted)
  7. Katsumi Shimotsu, 2006. "Gaussian Semiparametric Estimation of Multivariate Fractionally Integrated Processes," Working Papers 1062, Queen's University, Department of Economics. [Downloadable!]
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  8. Jonathan Dark, 2004. "Long term hedging of the Australian All Ordinaries Index using a bivariate error correction FIGARCH model," Monash Econometrics and Business Statistics Working Papers 7/04, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  9. Kin-Yip Ho & Ka Cheng Tsui, 2004. "Volatility Dynamics of the Tokyo Stock Exchange: A Sectoral Analysis based on the Multivariate GARCH Approach," Money Macro and Finance (MMF) Research Group Conference 2004 12, Money Macro and Finance Research Group. [Downloadable!]
  10. Katsumi Shimotsu & Morten Ørregaard Nielsen, 2006. "Determining the Cointegrating Rank in Nonstationary Fractional Systems by the Exact Local Whittle Approach," Working Papers 1029, Queen's University, Department of Economics. [Downloadable!]
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