American Journal of Theoretical and Applied Statistics

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Modelling the Volatility of Exchange Rates in Rwandese Markets

Received: 15 August 2014    Accepted: 19 March 2015    Published: 25 September 2015
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Abstract

This work applied Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approachto modelling volatility in Rwanda Exchange rate returns. The Autoregressive (AR) model with GARCH errors was fitted to the daily exchange rate returns using Quasi-Maximum Likelihood Estimation (Q-MLE) method to get the current volatility, asymptotic consistency and asymptotic normality of estimated parameters.Akaike Information criterion was used for appropriate GARCH model selection while Jarque Bera test used for normality testing revealed that both returns and residuals have fat tails behaviour. It was shown that the estimated model fits Rwanda exchange rate returns data well.

DOI 10.11648/j.ajtas.20150406.12
Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6, November 2015)
Page(s) 426-431
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Model, Volatility, ExchangeRate, Quasi Maximum Likelihood, GARCH Model

References
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[8] Hull, J., & White, A. (1998). Value at Risk When Changes in Market Variables are not Normally Distributed. Journal of Risk Vol.1 , 47-61.
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Author Information
  • Mahatma Gandhi University-Rwanda, Faculty of Science, Department of Mathematics, Kigali-Rwanda

  • Jomo Kenyatta University of Agriculture and Technology, Faculty of Applied Science, Department of Statistics and Actuarial Science, Kisumu-Kenya

  • Jomo Kenyatta University of Agriculture and Technology, Faculty of Applied Science, Department of Statistics and Actuarial Science, Nairobi-Kenya

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  • APA Style

    Jean de Dieu Ntawihebasenga, Joseph Kyalor Mung’atu, Peter Nyamuhanga Mwita. (2015). Modelling the Volatility of Exchange Rates in Rwandese Markets. American Journal of Theoretical and Applied Statistics, 4(6), 426-431. https://doi.org/10.11648/j.ajtas.20150406.12

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    ACS Style

    Jean de Dieu Ntawihebasenga; Joseph Kyalor Mung’atu; Peter Nyamuhanga Mwita. Modelling the Volatility of Exchange Rates in Rwandese Markets. Am. J. Theor. Appl. Stat. 2015, 4(6), 426-431. doi: 10.11648/j.ajtas.20150406.12

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    AMA Style

    Jean de Dieu Ntawihebasenga, Joseph Kyalor Mung’atu, Peter Nyamuhanga Mwita. Modelling the Volatility of Exchange Rates in Rwandese Markets. Am J Theor Appl Stat. 2015;4(6):426-431. doi: 10.11648/j.ajtas.20150406.12

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  • @article{10.11648/j.ajtas.20150406.12,
      author = {Jean de Dieu Ntawihebasenga and Joseph Kyalor Mung’atu and Peter Nyamuhanga Mwita},
      title = {Modelling the Volatility of Exchange Rates in Rwandese Markets},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {4},
      number = {6},
      pages = {426-431},
      doi = {10.11648/j.ajtas.20150406.12},
      url = {https://doi.org/10.11648/j.ajtas.20150406.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20150406.12},
      abstract = {This work applied Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approachto modelling volatility in Rwanda Exchange rate returns. The Autoregressive (AR) model with GARCH errors was fitted to the daily exchange rate returns using Quasi-Maximum Likelihood Estimation (Q-MLE) method to get the current volatility, asymptotic consistency and asymptotic normality of estimated parameters.Akaike Information criterion was used for appropriate GARCH model selection while Jarque Bera test used for normality testing revealed that both returns and residuals have fat tails behaviour. It was shown that the estimated model fits Rwanda exchange rate returns data well.},
     year = {2015}
    }
    

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    T1  - Modelling the Volatility of Exchange Rates in Rwandese Markets
    AU  - Jean de Dieu Ntawihebasenga
    AU  - Joseph Kyalor Mung’atu
    AU  - Peter Nyamuhanga Mwita
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    AB  - This work applied Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approachto modelling volatility in Rwanda Exchange rate returns. The Autoregressive (AR) model with GARCH errors was fitted to the daily exchange rate returns using Quasi-Maximum Likelihood Estimation (Q-MLE) method to get the current volatility, asymptotic consistency and asymptotic normality of estimated parameters.Akaike Information criterion was used for appropriate GARCH model selection while Jarque Bera test used for normality testing revealed that both returns and residuals have fat tails behaviour. It was shown that the estimated model fits Rwanda exchange rate returns data well.
    VL  - 4
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