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Dynamic cointegration and relevant vector machine: the relationship between gold and silver

Author

Listed:
  • Isabella Procidano

    (Dep. of Statistics University Ca' Foscari Venice)

  • Margherita Gerolimetto
  • Silio Rigatti Luchini

Abstract

We use the Relevant Vector Machine, a technique of supervised learning introduced by Tipping (2001), to conduct a dynamic cointegration analysis on the time series of the price of gold and silver over the period 1971-2004. Unlike the results of traditional cointegration analysis, this study reveals that there is a dynamic long run relationship over the whole period

Suggested Citation

  • Isabella Procidano & Margherita Gerolimetto & Silio Rigatti Luchini, 2006. "Dynamic cointegration and relevant vector machine: the relationship between gold and silver," Computing in Economics and Finance 2006 380, Society for Computational Economics.
    • Handle: RePEc:sce:scecfa:380
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    File URL: http://repec.org/sce2006/up.27361.1141139739.pdf
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    References listed on IDEAS

    as
    1. Escribano, Álvaro & Mira, Santiago, 1996. "Nonlinear cointegration and nonlinear error correction," DES - Working Papers. Statistics and Econometrics. WS 4546, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Escribano, Álvaro & Granger, C.W.J. (Clive William John), 1995. "Investigating the relationship between gold and silver prices," DES - Working Papers. Statistics and Econometrics. WS 4517, Universidad Carlos III de Madrid. Departamento de Estadística.
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    Cited by:

    1. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.

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    More about this item

    Keywords

    Dynamic cointegration; relevant vector machine;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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