International Journal of Statistical Distributions and Applications

| Peer-Reviewed |

Modelling Volatility of the US Dollar Against the Kenyan Shilling Exchange Rate and Investigating the Effect of Kenyan Inflation Rates on this Volatility in Kenya

Received: 18 October 2018    Accepted: 20 November 2018    Published: 17 December 2018
Views:       Downloads:

Share This Article

Abstract

Exchange rates and monetary policies are key tools in economic management and in the stabilization and adjustment process in developing countries, where low inflation rates and international competitiveness have become major policy targets. The study modelled the volatility of the US dollar against the Kenyan shilling (USD/KES) exchange rate and investigated the effect of inflation rates in Kenya on this volatility for the years 2005 to 2017. The data for this research was obtained from secondary sources: Central Bank of Kenya and the Kenya National Bureau of Statistics. The results indicated that the USD/KES exchange rate exhibited persistent signs of volatility. A number of heteroscedasticity models were then tested and the GARCH family (ARMA (1, 3)/EGARCH (1, 2)) model was concluded to be the best model to fit the volatility of the USD/KES exchange rate. The study tested the forecasting power of this model by comparing in-sample and out of sample observations and comprehensive conclusions were made that the model was the best fit to forecast the volatility of the USD/KES exchange rate. The volatility figures of the USD/KES exchange rate were extracted from the EGARCH model and further tests were conducted to investigate the effect of Kenyan inflation rates on them. Weighted Least Squares regression was conducted on the Kenyan inflation rates and volatility of the USD/KES exchange rate and comprehensive conclusions were made that there existed a significant relationship between the Kenyan inflation rates and the volatility of the USD/KES.

DOI 10.11648/j.ijsd.20180403.12
Published in International Journal of Statistical Distributions and Applications (Volume 4, Issue 3, September 2018)
Page(s) 60-67
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

Autoregressive Conditional Heteroscedasticity (ARCH), Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH), Weighted Least Squares (WLS)

References
[1] K, Oude. The effect of exchange rate fluctuations on GDP in Kenya. Nairobi s.n., 2013.
[2] Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation. Engle, Robert F. 4, 1982, Econometrica, Vol. 50, pp. 987-1008.
[3] Generalized Autoregressive Conditional Heteroskedasticity. Bollerslev, Tim. 1986, Journal of Econometrics, Vol. 31, pp. 307-327.
[4] Nelson, D. B. and Cao, C. Q., 1992. Inequality constraints in the univariate GARCH model. Nelson, D. B., & Cao, C. Q. 2, 1992, Journal of Business & Economic Statistics, Vol. 10, pp. 229-235.
[5] Estimating stock market volatility Using assymetric GARCH Models. Alberg, Dima, Shalit, Haim and Yosef, Rami. 15, 2008, Applied Financial Economics, Vol. 18, pp. 1201-1208.
[6] Assessing Volatility Forecasting Models:Why GARCH Models Take the Lead. Matei, Marius. 4, s.l. Romanian Journal of Economic Forecasting, 2009, Romanian Journal of Economic Forecasting, Vol. 12, pp. 42-65.
[7] Nganga. The effects of exchange rate volatility on inflation rates in Kenya. University of Nairobi. Nairobi: s.n., 2015. Masters Thesis.
[8] On the causes and effects of exchange rate volatility on economic growth:Evidence from Ghana. Alagidede, Paul and Muazu, Ibrahim. 2, 2017, Journal of African Business, Vol. 18, pp. 162-193.
[9] Application of Weighted Least Squares Regression in Forecasting. Sulaimon Mutiu, O. 3, 2015, International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS), Vol. 2, pp. 45-54.
[10] Weighted least squares estimation with sampling weights. Shin, Hee-Choon. Alexandria: s.n., 2013, American statistical asscociation.
[11] Tsay, R. S. Analysis of Financial Time Series. s.l.: John Wiley & Sons, 2005.
[12] Adhikari, R. and Agrawal, R. An introductory study on time series modeling and forecasting. s.l.: arXiv, 2013.
[13] Mostafa, Fahed, Tharam, Dillon and Chang, Elizabeth. Computational intelligence applications to option pricing, volatility forecasting and value at risk. s.l.: Springer, 2017. Vol. 697.
[14] Arma models with arch errors.. Weiss, A. A. 2, 1984, Journal of time series analysis, Vol. 5, pp. 129–143.
[15] Dukich John, Kyung Yong Kim, and Huan-Hsun Lin. Modeling exchange rates using the GARCH Model. 2010.
Author Information
  • Faculty of Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Faculty of Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Faculty of Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Cite This Article
  • APA Style

    Carrine Andeyo Nandwa, Anthony Waititu, Anthony Wanjoya. (2018). Modelling Volatility of the US Dollar Against the Kenyan Shilling Exchange Rate and Investigating the Effect of Kenyan Inflation Rates on this Volatility in Kenya. International Journal of Statistical Distributions and Applications, 4(3), 60-67. https://doi.org/10.11648/j.ijsd.20180403.12

    Copy | Download

    ACS Style

    Carrine Andeyo Nandwa; Anthony Waititu; Anthony Wanjoya. Modelling Volatility of the US Dollar Against the Kenyan Shilling Exchange Rate and Investigating the Effect of Kenyan Inflation Rates on this Volatility in Kenya. Int. J. Stat. Distrib. Appl. 2018, 4(3), 60-67. doi: 10.11648/j.ijsd.20180403.12

    Copy | Download

    AMA Style

    Carrine Andeyo Nandwa, Anthony Waititu, Anthony Wanjoya. Modelling Volatility of the US Dollar Against the Kenyan Shilling Exchange Rate and Investigating the Effect of Kenyan Inflation Rates on this Volatility in Kenya. Int J Stat Distrib Appl. 2018;4(3):60-67. doi: 10.11648/j.ijsd.20180403.12

    Copy | Download

  • @article{10.11648/j.ijsd.20180403.12,
      author = {Carrine Andeyo Nandwa and Anthony Waititu and Anthony Wanjoya},
      title = {Modelling Volatility of the US Dollar Against the Kenyan Shilling Exchange Rate and Investigating the Effect of Kenyan Inflation Rates on this Volatility in Kenya},
      journal = {International Journal of Statistical Distributions and Applications},
      volume = {4},
      number = {3},
      pages = {60-67},
      doi = {10.11648/j.ijsd.20180403.12},
      url = {https://doi.org/10.11648/j.ijsd.20180403.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijsd.20180403.12},
      abstract = {Exchange rates and monetary policies are key tools in economic management and in the stabilization and adjustment process in developing countries, where low inflation rates and international competitiveness have become major policy targets. The study modelled the volatility of the US dollar against the Kenyan shilling (USD/KES) exchange rate and investigated the effect of inflation rates in Kenya on this volatility for the years 2005 to 2017. The data for this research was obtained from secondary sources: Central Bank of Kenya and the Kenya National Bureau of Statistics. The results indicated that the USD/KES exchange rate exhibited persistent signs of volatility. A number of heteroscedasticity models were then tested and the GARCH family (ARMA (1, 3)/EGARCH (1, 2)) model was concluded to be the best model to fit the volatility of the USD/KES exchange rate. The study tested the forecasting power of this model by comparing in-sample and out of sample observations and comprehensive conclusions were made that the model was the best fit to forecast the volatility of the USD/KES exchange rate. The volatility figures of the USD/KES exchange rate were extracted from the EGARCH model and further tests were conducted to investigate the effect of Kenyan inflation rates on them. Weighted Least Squares regression was conducted on the Kenyan inflation rates and volatility of the USD/KES exchange rate and comprehensive conclusions were made that there existed a significant relationship between the Kenyan inflation rates and the volatility of the USD/KES.},
     year = {2018}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Modelling Volatility of the US Dollar Against the Kenyan Shilling Exchange Rate and Investigating the Effect of Kenyan Inflation Rates on this Volatility in Kenya
    AU  - Carrine Andeyo Nandwa
    AU  - Anthony Waititu
    AU  - Anthony Wanjoya
    Y1  - 2018/12/17
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ijsd.20180403.12
    DO  - 10.11648/j.ijsd.20180403.12
    T2  - International Journal of Statistical Distributions and Applications
    JF  - International Journal of Statistical Distributions and Applications
    JO  - International Journal of Statistical Distributions and Applications
    SP  - 60
    EP  - 67
    PB  - Science Publishing Group
    SN  - 2472-3509
    UR  - https://doi.org/10.11648/j.ijsd.20180403.12
    AB  - Exchange rates and monetary policies are key tools in economic management and in the stabilization and adjustment process in developing countries, where low inflation rates and international competitiveness have become major policy targets. The study modelled the volatility of the US dollar against the Kenyan shilling (USD/KES) exchange rate and investigated the effect of inflation rates in Kenya on this volatility for the years 2005 to 2017. The data for this research was obtained from secondary sources: Central Bank of Kenya and the Kenya National Bureau of Statistics. The results indicated that the USD/KES exchange rate exhibited persistent signs of volatility. A number of heteroscedasticity models were then tested and the GARCH family (ARMA (1, 3)/EGARCH (1, 2)) model was concluded to be the best model to fit the volatility of the USD/KES exchange rate. The study tested the forecasting power of this model by comparing in-sample and out of sample observations and comprehensive conclusions were made that the model was the best fit to forecast the volatility of the USD/KES exchange rate. The volatility figures of the USD/KES exchange rate were extracted from the EGARCH model and further tests were conducted to investigate the effect of Kenyan inflation rates on them. Weighted Least Squares regression was conducted on the Kenyan inflation rates and volatility of the USD/KES exchange rate and comprehensive conclusions were made that there existed a significant relationship between the Kenyan inflation rates and the volatility of the USD/KES.
    VL  - 4
    IS  - 3
    ER  - 

    Copy | Download

  • Sections