IDEAS home Printed from https://ideas.repec.org/a/rfe/zbefri/v35y2017i1p73-95.html
   My bibliography  Save this article

Modelling exchange rate variations and global shocks in Brazil

Author

Listed:
  • Harold Ngalawa

    (School of Accounting, Economics & Finance, University of KwaZulu-Natal, Durban, Republic of South Africa)

  • Adebayo Augustine Kutu

    (School of Accounting, Economics & Finance, University of KwaZulu-Natal, Durban, Republic of South Africa)

Abstract

The purpose of this paper is to model variations of Brazil’s exchange rates and global shocks in order to establish if global oil prices and international interest rates (global shocks) have any impact on exchange rate variations in Brazil. After establishing the existence of ARCH effects and ensuring the stationarity of the data set, we estimate the symmetric GARCH (1,1) model along with two asymmetric EGARCH (1,1) and APARCH (1,1) models using the theoretical model of Kamal et al. (2012). The results show that the GARCH (1,1) model provides the best fit for Brazil’s exchange rate variations while the model selection chooses the Student’s t distribution as the preferable model of good fit compared to the alternatives. The study results show that Brazil’s exchange rates are significantly influenced by global shocks. Accordingly, we recommend that the Brazilian government should consider the impact of oil prices and global interest rates when formulating and implementing policies that impact on the exchange rate.

Suggested Citation

  • Harold Ngalawa & Adebayo Augustine Kutu, 2017. "Modelling exchange rate variations and global shocks in Brazil," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 35(1), pages 73-95.
    • Handle: RePEc:rfe:zbefri:v:35:y:2017:i:1:p:73-95
    as

    Download full text from publisher

    File URL: https://www.efri.uniri.hr/sites/efri.uniri.hr/files/cr-collections/2/08-ngalawa-2017-1-1498741962.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    2. Guillermo A. Calvo & Carmen M. Reinhart, 2002. "Fear of Floating," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(2), pages 379-408.
    3. Stephanie Schmitt-Grohé & Martin Uríbe, 2011. "Pegs and Pain," NBER Working Papers 16847, National Bureau of Economic Research, Inc.
    4. Kandil, Magda & Mirazaie, Ida, 2004. "The Effects of Exchange Rate Fluctuations on Output and Prices: Evidence from Developing Countries," Journal of Developing Areas, Tennessee State University, College of Business, vol. 38(2), pages 189-219, January-M.
    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. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Monacelli, Tommaso, 2004. "Into the Mussa puzzle: monetary policy regimes and the real exchange rate in a small open economy," Journal of International Economics, Elsevier, vol. 62(1), pages 191-217, January.
    8. Elbourne, Adam, 2008. "The UK housing market and the monetary policy transmission mechanism: An SVAR approach," Journal of Housing Economics, Elsevier, vol. 17(1), pages 65-87, March.
    9. Khuram Shafi & Liu Hua & Zahra Idrees & Javed Altaf Satti & Amna Nazeer, 2015. "Exchange Rate Volatility and Oil Prices Shocks," 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. 5(1), pages 249-256, January.
    10. Antonio Aguirre & Afonso Ferreira & Hilton Notini, 2007. "The Impact of Exchange Rate Volatility on Brazilian Manufactured Exports," Económica, Instituto de Investigaciones Económicas, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 0(1-2), pages 3-19, January-D.
    11. Andreas Benedictow & Pål Boug, 2010. "Trade liberalisation and import price behaviour: the case of textiles and wearing apparels," Discussion Papers 605, Statistics Norway, Research Department.
    12. Ghulam Ali, 2013. "EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 2(3), pages 1-6.
    13. Ogundipe, Adeyemi & Ogundipe, Oluwatomisin, 2013. "Oil Price and Exchange Rate Volatility in Nigeria," MPRA Paper 51668, University Library of Munich, Germany.
    14. Riman, Hodo B. & Akpan, Emmanuel S. & Offiong, Amenawo I, 2013. "Asymetric Effect of Oil Price Shocks on Exchange Rate Volatility and Domestic Investment in Nigeria," MPRA Paper 53282, University Library of Munich, Germany, revised 21 Aug 2013.
    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. Alaba David Alori & Adebayo Augustine Kutu, 2019. "Export Function of Cocoa Production, Exchange Rate Volatility and Prices in Nigeria," Journal of Economics and Behavioral Studies, AMH International, vol. 11(2), pages 1-14.

    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. Adebayo Augustine Kutu & Harold Ngalawa, 2017. "Modelling Exchange Rate Volatility and Global Shocks in South Africa," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 13(3), pages 178-193, JUNE.
    2. Jordi Galí & Tommaso Monacelli, 2016. "Understanding the Gains from Wage Flexibility: The Exchange Rate Connection," American Economic Review, American Economic Association, vol. 106(12), pages 3829-3868, December.
    3. Minot, Nicholas, 2014. "Food price volatility in sub-Saharan Africa: Has it really increased?," Food Policy, Elsevier, vol. 45(C), pages 45-56.
    4. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2016. "Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution," International Journal of Forecasting, Elsevier, vol. 32(2), pages 437-457.
    5. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    6. Ngozi G. Emenogu & Monday Osagie Adenomon & Nwaze Obini Nweze, 2020. "On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    7. Javed Farrukh & Podgórski Krzysztof, 2017. "Tail Behavior and Dependence Structure in the APARCH Model," Journal of Time Series Econometrics, De Gruyter, vol. 9(2), pages 1-48, July.
    8. Harry-Paul Vander Elst, 2015. "FloGARCH: Realizing Long Memory and Asymmetries in Returns Valitility," Working Papers ECARES ECARES 2015-12, ULB -- Universite Libre de Bruxelles.
    9. Bouri, Elie & Gabauer, David & Gupta, Rangan & Tiwari, Aviral Kumar, 2021. "Volatility connectedness of major cryptocurrencies: The role of investor happiness," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    10. Dutta, Shantanu & Essaddam, Naceur & Kumar, Vinod & Saadi, Samir, 2017. "How does electronic trading affect efficiency of stock market and conditional volatility? Evidence from Toronto Stock Exchange," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 867-877.
    11. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
    12. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    13. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    14. Muneer Shaik & Mohd Ziaur Rehman, 2023. "The Dynamic Volatility Connectedness of Major Environmental, Social, and Governance (ESG) Stock Indices: Evidence Based on DCC-GARCH Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(1), pages 231-246, March.
    15. repec:zbw:rwirep:0243 is not listed on IDEAS
    16. Grané, Aurea & Veiga, Helena, 2010. "Outliers in Garch models and the estimation of risk measures," DES - Working Papers. Statistics and Econometrics. WS ws100502, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Pierre J. Venter & Eben Maré, 2020. "GARCH Generated Volatility Indices of Bitcoin and CRIX," JRFM, MDPI, vol. 13(6), pages 1-15, June.
    18. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
    19. Erdal Demirhan & Banu Demirhan, 2015. "The Dynamic Effect of ExchangeRate Volatility on Turkish Exports: Parsimonious Error-Correction Model Approach," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 62(4), pages 429-451, September.
    20. Freddy Ronalde Camacho-Villagomez & Yanina Shegia Bajaña-Villagomez & Andrea Johanna Rodríguez-Bustos, 2024. "Estimating the Impact of Oil Price Volatility on the Ecuadorian Economy: A MIDAS Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 371-376, July.
    21. Siem Jan Koopman & André Lucas & Marcel Scharth, 2016. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 97-110, March.

    More about this item

    Keywords

    modelling exchange rate variations; GARCH; EGARCH and APARCH models;
    All these keywords.

    JEL classification:

    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • F1 - International Economics - - Trade

    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:rfe:zbefri:v:35:y:2017:i:1:p:73-95. 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: Danijela Ujcic (email available below). General contact details of provider: https://edirc.repec.org/data/efrijhr.html .

    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.