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This paper examines the exchange rate volatility with GARCH-type model of the daily exchange rate return series from January 2012 – August 2016 for Naira/Chinese Yuan, Naira/India Rupees, Naira/Spain Euro, Naira/UK Pounds and Naira/US Dollar returns. The studies compare estimates of variants of GARCH (1, 1), EGARCH (1, 1), TGARCH (1,1) and GJR-GARCH (1,1) models. The result from all models indict presence of volatility in the five currencies and equally indicate that most of the asymmetric models rejected the existence of a leverage effect except for models with volatility break. For GARCH (1, 1), GJR-GARCH (1, 1,) EGARCH (1,1) and TGARCH (1, 1), it was observed that India have the best exchange rate with the highest log-likelihood (Log L) and the lowest AIC and BIC followed by USA, China, Spain and United Kingdom respectively. The four models was later compared for the exchange rates of the five countries under consideration i.e. China, India, Spain, UK and USA to select the best fitted model for each country and it was discovered that GJR-GARCH (1,1) is the best fitted model for all the countries followed by GARCH (1,1), TGARCH (1,1) and EGARCH (1,1) in that order.
Sanus JO. Exchange rate mechanism: The Current Nigeria Experience. Being a paper delivered at the Nigeria-British Chamber of Commerce; Feb. 24, 2004.
Dahiru AB, Asemota OJ. Exchange rates volatility in nigeria: application of GARCH models with exogenous break. CBN Journal of Applied Statistics. 2013;4(1):89–116.
Opara CC, Emenike KO, Ani WU. Behaviour of Nigeria financial market indicator: Evidence from descriptive analysis. American Journal of Economic, Finance and Management. 2015;1(5):421-429.
Charles NO. Challenges of exchange rates volatility in economic management in Nigeria. Central Bank of Nigeria Bullion. 2006;30(3):17–25.
Olowe RO. Modelling naira/dollar exchange rate volatility: application of garch and assymetric models. International Review of Business Research Papers. 2009;5(3):377-398.
Benson UO, Godwin A. A comparative analysis of the effect of exchange rate volatility on exports in the CFA and Non-CFA Countries of Africa. Journal of Social Sciences. 2010;24(1):23-31.
Akpan and Atan. Effects of exchange rate movements on economic growth in Nigeria. CBN Journal of Applied Statistics. 2011;2(2):23-30.
Engle RF. Autoregressive Conditional heteroskedasticity with estimates of the variance of U.K. Inflation. Econometrica. 1982;50:987-1008.
Taylor SJ. Forecasting the volatility of currency exchange rates. International Journal of Forecasting. 1987;3(1):159-70.
Chang EJ, Tabak BM. Tracking Brazilian exchange rate volatility; 2003.
Meese R, Rogoff K. Empirical exchange rate models of the seventies: do they fit the out of sample? Journal of International Economics. 1983;14(12):3-24.
Longmore R, Robinson W. Modelling and forecasting exchange rate dynamics: An application of asymmetric volatility models. Bank of Jamaica. Working Paper WP2004/03; 2004.
Chong CW, Ahmad MI, Abdullah MY. Performance of GARCH models in forecasting stock market volatility. Journal of Forecasting. 1999;18:333-343.
Bollerslev T. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics. 1986;31:307–327.
Nelson DB. Conditional heteroskedasticity in asset returns; A new approach. Econometrica. 1991;59: 347–370.
Glosten LR, Jagannathan R, Runkle D. On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance. 1993;48:1779-1801.
Rabemananjara R, Zakoian JM. Threshold arch models and asymmetries in volatility. Journal of Applied Econometrics. 1993;8(1):31–49.
Tsay RS. Analysis of financial time series, 2nd edition, New Jersey, University of Chicago, John Wiley Sons; 2005.