Nigerian Statistical Association Logo - A Galaxy of Professional Statisticians
The third edition of the Nigerian Statistical Association Competition for Undergraduate students will commence on the 24th to 30th of July 2022

Volatility Persistence in Naira Exchange Rates: A Pre- and Post- Global Financial Crisis Analysis


O. S. Yaya

JOURNAL: Journal of the Nigerian Statistical Association Vol. 28, 2016
YEAR: 2016


The exchange rates of the naira against other currencies around the world have been affected by market reactions, with the greatest crash during the 2008 global financial crisis. As a result, structural pattern and volatility persistence in the pre-global and post-global crisis periods might have undergone a shift. This paper considered high-frequency naira exchange rate time series for pre-global and post-global crisis periods to investigate the volatility persistence in the financial time series. Long range dependence techniques and volatility modelling approaches were applied on level series, absolute and squared log-returns of six daily naira exchange rate series between 12 October 2001 and 19 December 2014. Significant persistence of volatility in both absolute and square returns of the exchange rates series was observed, and there was the difference in the level of persistence between the two-time series sub-samples, that is, the pre-crisis period seemed to exhibit a lower level of volatility than the post-global crisis period. Further investigation using estimates of volatility modelling confirmed lower volatility persistence in the pre-crisis period, and possible asymmetry in the entire time series sample. The higher persistence of volatility observed in naira exchange rates during the post-crisis period was as a result of the residual impacts of the global crisis on the economy that we experienced till the end of the sampled period.


Abadir, K.M., Distaso, W. and Giraitis, L. (2007). Nonstationarity extended local whittle estimation. Journal of Econometrics, 141: 1353-1384.

Ajibola, I.O., Udoette, U.S., Omotosho, B.S. and Muhammad, R.A. (2015). Nonlinear adjustments between exchange rates and external reserves in Nigeria, a threshold cointegration analysis. CBN Journal of Applied Statistics, 6(1a): 111-132.

Akpokodje, G. (2009). Exchange rate volatility and external trade, the experience of selected African countries. In: Adenikinju, A., Busari, D. and Olofin, S. (Editors), Applied Econometrics and Macroeconometric Modelling in Nigeria, Ibadan University Press, pp.349-362.

Bauwens, L., Hafner, C. and Laurent, S. (2012). Volatility models. In: Bauwen, L., Hafner, C. and Laurent, S. (Editors), Handbook of Volatility Models and their Applications, Wiley, UK, pp. 1-45.

Benassy-Quere, A., Fontagne, L. and Lahreche-Revil, A. (2001). Exchange rate strategies in the competition for attracting foreign direct Investment. Journal of the Japanese and International Economies, 1 (2): 178-198.

Beine, M., Benassy-Quere, A. and Lecourt, C. (2002). Central bank intervention

and foreign exchange rates, new evidence from FIGARCH estimations. Journal of International Money and Finance, 21: 115-144.

Carpantier, J.-F. (2010). Commodities inventory eect. CORE Discussion Paper Number DP 2010/40, Universite catholique de Louvain, Center for Operations Research and Econometrics (CORE).

Diebold, F.X. and Inoue, A. (2001). Long memory and regime switching. Journal of Econometrics, 105: 131-159.

Ding, Z., Granger, C.W.J. and Engle, R.F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1: 83-106.

Doornik, J.A. and Hendry, D.F. (2001). Econometric Modelling using PcGive, Volume III, Timberlake Consultants Press, London.

Engle, R.F. (2011). Long term skewness and systemic risk. Journal of Financial Econometrics, 9: 437-468.

Engle, R.F. and Ng, V. (1993). Time-varying volatility and the dynamic behaviour of the term structure. Journal of Money, Credit and Banking, 25: 336-349.

Francq, C. and Zakoian, J.-M. (2010). GARCH Models, Structure, Statistical Inference and Financial Applications. Wiley, UK.

Gil-Alana, L.A., Shittu, O.I. and Yaya, O.S. (2014). On the persistence and volatility in European, American and Asian stocks bull and bear markets. Journal of International Money and Finance, 40: 149-162.

Giraitis, L.R. Leipus, S. and Surgailis, D. (2006). Recent advances in ARCH modelling. In: A. Kirman and G. Teyssiere (Editors), Long Memory in Economics, Springer, UK, pp. 3-38.

Glosten, L., Jagannathan, R. and Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Econometrics, 48: 1779-1801.

Goffe, W.L., Ferrier, G.D. and Rogers, J. (1994). Global optimization of statistical functions with simulated annealing. Journal of Econometrics, 601(2): 65-99.

Huang, K. (2011). Modelling volatility of S & P 500 index daily returns, a comparison between model-based forecasts and implied volatility. PhD thesis, Department of Finance and Statistics, Hanken School of Economics, Vasa.

Kiyota, K. and Urata, S. (2004). Exchange rate, exchange rate volatility and foreign direct investment. TheWorld Economy, 27: 1501-1536.

Lo, A.W. (1991). Long term memory in Stock market prices. Econometrica, 59:1279-1313.

Mills, T.C. (1996). Non-linear forecasting of financial time series, an overview and some new models. Journal of Forecasting, 15: 127-135.

Mikosch, T. and Starica, C. (2004). Non-stationarities in financial time series, the long-range dependence, and the IGARCH effects. The Review of Economics and Statistics, 86: 378-390.

Nelson, D.B. (1991). Conditional heteroscedasticity in asset returns, a new approach.Econometrica, 592: 347-70.

Ooms, M. and Doornik, J. A. (1999). Inference and forecasting for fractional autoregressive integrated moving average models, with application to US and UK inflation.Econometric Institute Report, 9947/A, pp 1-34.

Ooms, M. and Doornik, J. (2004). Inference and forecasting for fractional autoregressive moving average models, with application to US and UK inflation. Studies in Nonlinear Dynamics and Econometrics, 82: 1-25.

Ooms, M. and Doornik, J. A. (2006). A package for estimating, forecasting and simulating ARFIMA models, ARFIMA package 1.04 for Ox. Nueld College, Oxford, pp. 1-30.

Pagan, A. and Sossounov, K.A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18: 23-46.

Perron, P. and Qu, Z. (2010). Long-memory and level shifts in the volatility of stock market return indices. Journal of Business, Economics and Statistics, 28: 275-290.

Phillips, P.C.B. and Shimotsu, K. (2004). Local whittle estimation in nonstationary and unit root cases. Annals of Statistics, 32: 656-692.

Robinson, P.M. (1995a). Gaussian semiparametric estimation of long range dependence. Annals of Statistics, 23: 1630-1661.

Robinson, P.M. (1995b). Log-periodogram regression of time series with long-range dependence. Annals of Statistics, 23: 1048-1072.

Robinson, P.M. and Henry, M. (1999). Long and short memory conditional heteroskedasticity in estimating the memory in levels. Econometric Theory, 15: 299-336.

Ruiz, I.C. (2005). Exchange rate as a determinant of Foreign Direct Investment, does it really matter? Theoretical aspects, literature review and applied proposal. Ecos de Economia, 10: 153-171.

Salisu, A.A. and Mobolaji, H. (2013). Modeling returns and volatility transmission between oil price and US-Nigeria exchange rate. Energy Economics, 39: 169-176.

Salisu, A.A., Oloko, T.F. and Oyewole, O.J. (2016). Testing for martingale difference hypothesis with structural breaks: evidence from Asia-Pacific foreign exchange markets. Borsa Istanbul Review,

Shimotsu, K. and Phillips, P.C.B. (2005). Exact local whittle estimation of fractional integration. Annals of Statistics, 33: 1890-1933.

Tsay, R. (2010). Analysis of Financial Time Series (3rd edition). John Wiley & Sons, New York.

van Bellegem, S. (2012). Locally stationary volatility modelling. In: Bauwen, L., Hafner, C. and Laurent, S. (Editors), Handbook of Volatility Models and their Applications, Wiley, UK, pp. 249-268.

Velasco, C. (1999). Gaussian semiparametric estimation of non-stationary time series. Journal of Time Series Analysis, 20: 87-127.

Velasco, C. and Robinson, P.M. (2000). Whittle pseudo-maximum likelihood estimation for nonstationary time series. Journal of the American Statistical Association, 95:1229-1243.

Xekalaki, E. and Degiannakis, S. (2010). ARCH Models for Financial Applications.John Wiley & Sons, UK.

Yaya, O.S. (2013). On the variants of nonlinear models. PhD Thesis, Department of Statistics, University of Ibadan.

Yaya, O.S., Gil-Alana, L.A. and Shittu, O.I. (2015). Fractional integration and asymmetric volatility in European, American and Asian bull and bear markets, application of high-frequency stock data. International Journal of Finance and Economics, 203: 276-290.

Yaya, O.S. and Shittu, O.I. (2014). Naira exchange rate volatility, linear or nonlinear GARCH specifications? Journal of the Nigerian Statistical Association, 26: 78-87


Our journal is now available to everyone
Download Journal

Journal of the Nigerian Statistical Association Vol. 28, 2016


Privacy Policy Terms of Service © 2024 Nigerian Statistical Association - All Rights Reserved Developed by Masterweb Solutions