IDEAS home Printed from https://ideas.repec.org/a/hur/ijarbs/v4y2014i7p369-381.html
   My bibliography  Save this article

Forecasting of Exchange Rate Volatility between Naira and US Dollar Using GARCH Models

Author

Listed:
  • Musa Y.
  • Tasi’u M.
  • Abubakar Bello

Abstract

Exchange rates are important financial problem that is receiving attention globally. This study investigated the volatility modeling of daily Dollar/Naira exchange rate using GARCH, GJR- GARCH, TGRACH and TS-GARCH models by using daily data over the period June 2000 to July 2011. The aim of the study is to determine volatility modeling of daily exchange rate between US (Dollar) and Nigeria (Naira). The results show that the GJR-GARCH and TGARCH models show the existence of statistically significant asymmetry effect. The forecasting ability is subsequently assessed using the symmetric lost functions which are the Mean Absolute Error (MAE), Root Mean Absolute Error (RMAE), Mean Absolute Percentage Error (MAPE) and Theil inequality Coefficient. The results show that TGARCH model provide the most accurate forecasts. This model will captured all the necessary stylize facts (common features) of financial data, such as persistent, volatility clustering and asymmetric effects.

Suggested Citation

  • Musa Y. & Tasi’u M. & Abubakar Bello, 2014. "Forecasting of Exchange Rate Volatility between Naira and US Dollar Using GARCH Models," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 4(7), pages 369-381, July.
    • Handle: RePEc:hur:ijarbs:v:4:y:2014:i:7:p:369-381
    as

    Download full text from publisher

    File URL: http://hrmars.com/hrmars_papers/Forecasting_of_Exchange_Rate_Volatility_between_Naira_and_US_Dollar_Using_GARCH_Models.pdf
    Download Restriction: no
    File URL: http://hrmars.com/hrmars_papers/Forecasting_of_Exchange_Rate_Volatility_between_Naira_and_US_Dollar_Using_GARCH_Models.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    3. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    4. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    5. Rohan Longmore & Wayne Robinson, 2005. "Modelling and Forecasting Exchange Rate Dynamics in Jamaica: an Application of Asymmetric Volatility Models," Money Affairs, CEMLA, vol. 0(1), pages 23-56, January-J.
    6. Taylor, Stephen J., 1987. "Forecasting the volatility of currency exchange rates," International Journal of Forecasting, Elsevier, vol. 3(1), pages 159-170.
    7. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hatice Erkekoglu & Aweng Peter Majok Garang & Adire Simon Deng, 2020. "Modeling and Forecasting USD/UGX Volatility through GARCH Family Models: Evidence from Gaussian, T and GED Distributions," International Journal of Economics and Financial Issues, Econjournals, vol. 10(2), pages 268-281.
    2. Nyoni, Thabani, 2018. "Modeling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box - Jenkins ARIMA approach," MPRA Paper 88622, University Library of Munich, Germany, revised 19 Aug 2018.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Debopam Rakshit & Ranjit Kumar Paul & Md Yeasin & Walid Emam & Yusra Tashkandy & Christophe Chesneau, 2023. "Modeling Asymmetric Volatility: A News Impact Curve Approach," Mathematics, MDPI, vol. 11(13), pages 1-14, June.
    2. Piotr Fiszeder & Witold Orzeszko, 2012. "Nonparametric Verification of GARCH-Class Models for Selected Polish Exchange Rates and Stock Indices," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(5), pages 430-449, November.
    3. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    4. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107034723, September.
    5. Sheriffdeen A. Tella & Olumuyiwa G. Yinusa & Ayinde Taofeek Olusola & Saban Celik, 2011. "Global Economic Crisis And Stock Markets Efficiency: Evidence From Selected Africa Countries," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 25(1), pages 139-169.
    6. Ilu, Ahmad Ibraheem, 2020. "Exchange Rate Pass through to Stock Prices: A Multi GARCH Approach," MPRA Paper 98442, University Library of Munich, Germany.
    7. Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.
    8. Teräsvirta, Timo, 2006. "An introduction to univariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 646, Stockholm School of Economics.
    9. Hentschel, Ludger, 1995. "All in the family Nesting symmetric and asymmetric GARCH models," Journal of Financial Economics, Elsevier, vol. 39(1), pages 71-104, September.
    10. Rakshit, Debopam & Paul, Ranjit Kumar & Panwar, Sanjeev, 2021. "Asymmetric Price Volatility of Onion in India," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 0(Number 2), June.
    11. Subrata Roy, 2020. "Stock Market Asymmetry and Investors’ Sensation on Prime Minister: Indian Evidence," Jindal Journal of Business Research, , vol. 9(2), pages 148-161, December.
    12. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    13. Köksal, Bülent, 2009. "A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns," MPRA Paper 30510, University Library of Munich, Germany.
    14. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    15. Bucevska Vesna, 2013. "An Empirical Evaluation of GARCH Models in Value-at-Risk Estimation: Evidence from the Macedonian Stock Exchange," Business Systems Research, Sciendo, vol. 4(1), pages 49-64, March.
    16. Jamal Bouoiyour & Refk Selmi, 2015. "Exchange volatility and export performance in Egypt: New insights from wavelet decomposition and optimal GARCH model," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 24(2), pages 201-227, March.
    17. Committee, Nobel Prize, 2003. "Time-series Econometrics: Cointegration and Autoregressive Conditional Heteroskedasticity," Nobel Prize in Economics documents 2003-1, Nobel Prize Committee.
    18. Neifar, Malika, 2020. "Stock Market Volatility Analysis: A Case Study of TUNindex," MPRA Paper 99140, University Library of Munich, Germany.
    19. Levent, Korap, 2008. "Asymmetric information content of the YTL/US$ exchange rate return: new evidence from the post-crisis data using arma-egarch-m modeling," MPRA Paper 19631, University Library of Munich, Germany.
    20. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.

    More about this item

    Keywords

    Volatility; GARCH; Asymmetric models; Exchange Rates;
    All these keywords.

    JEL classification:

    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F43 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Economic Growth of Open Economies

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hur:ijarbs:v:4:y:2014:i:7:p:369-381. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Hassan Danial Aslam (email available below). General contact details of provider: http://hrmars.com/index.php/pages/detail/IJARBSS .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.