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
Volume 4, Issue 3, September 2018, Pages: 60-67
Received: Oct. 18, 2018;
Accepted: Nov. 20, 2018;
Published: Dec. 17, 2018
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Carrine Andeyo Nandwa, Faculty of Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Waititu, Faculty of Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya, Faculty of Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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.
Carrine Andeyo Nandwa,
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.
Vol. 4, No. 3,
2018, pp. 60-67.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
K, Oude. The effect of exchange rate fluctuations on GDP in Kenya. Nairobi s.n., 2013.
Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation. Engle, Robert F. 4, 1982, Econometrica, Vol. 50, pp. 987-1008.
Generalized Autoregressive Conditional Heteroskedasticity. Bollerslev, Tim. 1986, Journal of Econometrics, Vol. 31, pp. 307-327.
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.
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.
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.
Nganga. The effects of exchange rate volatility on inflation rates in Kenya. University of Nairobi. Nairobi: s.n., 2015. Masters Thesis.
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.
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.
Weighted least squares estimation with sampling weights. Shin, Hee-Choon. Alexandria: s.n., 2013, American statistical asscociation.
Tsay, R. S. Analysis of Financial Time Series. s.l.: John Wiley & Sons, 2005.
Adhikari, R. and Agrawal, R. An introductory study on time series modeling and forecasting. s.l.: arXiv, 2013.
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.
Arma models with arch errors.. Weiss, A. A. 2, 1984, Journal of time series analysis, Vol. 5, pp. 129–143.
Dukich John, Kyung Yong Kim, and Huan-Hsun Lin. Modeling exchange rates using the GARCH Model. 2010.