Modelling the BRICS Exchange Rates Using the Vector Autoregressive (VAR) Model

  • Lebotsa Daniel Metsileng North West University, Corner Dr Albert Lithuli, Mmabatho
  • Ntebogang Dinah Moroke North West University, Corner Dr Albert Lithuli, Mmabatho
  • Johannes Tshepiso Tsoku North West University, Corner Dr Albert Lithuli, Mmabatho

Abstract

The paper modelled the BRICS exchange rates using the Vector Autoregressive (VAR) model. Monthly time series data ranging from January 2008 to January 2018 was used. All the analysis was computed using the R programming software. The study aimed to determine a suitable VAR model in modelling the BRICS exchange rates and determine the linear dependency between the financial markets (in particular BRICS exchange rates). Optimal lag length of one (1) was selected using the SIC. The VAR model with lag length one was fitted and the parameters were estimated. The results revealed that there is a unidirectional relationship amongst the BRICS exchange rates. The VAR (1) model did not satisfy all the diagnostic tests, therefore forecasting future values of the BRICS exchange rates could not be computed. Recommendations for different approaches were formulated.

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References

Adenomon, M., Ojehomon, V. T. & Oyejola, B. (2013). Modelling The Dynamic Relationship Between Rainfall and Temperature Time Series Data In Niger State, Nigeria. Athanasopoulos, G., de Carvalho guillén, O. T., Issler, J. V. & Vahid, F. (2011). Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions. Journal of Econometrics, 164, 116-129. Backus, D. (1986). The Canadian--US Exchange Rate: Evidence from a Vector Autoregression. The Review of Economics and Statistics, 628-637. Bagliano, F. C. & Favero, C. A. (1998). Measuring monetary policy with VAR models: An evaluation. European Economic Review, 42, 1069-1112. ai, E. S. & Karaca, S. S. (2012). The determinants of stock market index: VAR approach to the Turkish stock market. International Journal of Economics and Financial Issues, 3, 163-171. Bessler, D. A. (1984). Relative prices and money: a vector autoregression on Brazilian data. American Journal of Agricultural Economics, 66, 25-30. Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17, 334-355. Chamalwa, H. A. & Bakari, H. R. (2016). A Vector Autoregressive (VAR) Cointegration and Vector Error Correction Model (VECM) Approach for Financial Deepening Indicators AND Economic Growth in Nigeria. American Journal of Mathematical Analysis, 4, 1-6. Dickey, D. A. & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74, 427-431. Edgerton, D. & Shukur, G. (1999). Testing autocorrelation in a system perspective testing autocorrelation. Econometric Reviews, 18, 343-386. Eklund, B. (2007). Forecasting the Icelandic business cycle using vector autoregressive models. Enders, W. & Sandler, T. (1993). The effectiveness of antiterrorism policies: A vector-autoregressionintervention analysis. American Political Science Review, 87, 829-844. Enisan, A. A. & Olufisayo, A. O. (2009). Stock market development and economic growth: Evidence from seven sub-Sahara African countries. Journal of economics and business, 61, 162-171. Estenson, P. S. (1992). The Keynesian theory of the price level: an econometric evaluation using a vector autoregression model. Journal of Post Keynesian Economics, 14, 547-560. Freeman, J. R., Williams, J. T. & Lin, T. M. (1989). Vector autoregression and the study of politics. American Journal of Political Science, 842-877. Hall, A. (1989). Testing for a unit root in the presence of moving average errors. Biometrika, 76, 49-56. Ijumba, C. (2013). Multivariate Analysis of the BRICS Financial Markets. University of KwaZulu-Natal, Durban.
Jarque, C. M. & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics letters, 6, 255-259. Lama, A., Jha, G., Gurung, B., Paul, R. K. & Sinha, K. (2016). VAR-MGARCH Models for Volatility Modelling of Pulses Prices: An Application. Liitkepohl, H. (1991). Introduction to multiple time series analysis. Berlin et al. Lu, M. (2001). Vector autoregression (VAR)an approach to dynamic analysis of geographic processes. Geografiska Annaler: Series B, Human Geography, 83, 67-78. LuTkepohl, H. (2007). New introduction to multiple time series analysis. Mcmillin, W. D. (1991). The velocity of M1 in the 1980s: evidence from a multivariate time series model. Southern Economic Journal, 634-648. Mohanasundaram, T. & Karthikeyan, P. (2015). Cointegration and stock market interdependence: Evidence from South Africa, India and the USA. South African Journal of Economic and Management Sciences, 18, 475-485. Ono, S. (2011). Oil price shocks and stock markets in BRICs. The European Journal of Comparative Economics, 8, 29-45. Pfaff, B. (2008). VAR, SVAR and SVEC models: Implementation within R package vars. Journal of Statistical Software, 27, 1-32. Phillips, P. C. & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335-346. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48. Tsay, R. S. (2005). Analysis of financial time series, John Wiley & Sons. Wang, J. & Zivot, E. (2006). Modeling Financial Time Series with S-PLUS. Zhang, T., Yin, F., Zhou, T., Zhang, X. Y. & Li, X. S. (2016). Multivariate time series analysis on the dynamic relationship between Class B notifiable diseases and gross domestic product (GDP) in China. Scientific reports, 6, 29.
Published
2018-11-03
How to Cite
METSILENG, Lebotsa Daniel; MOROKE, Ntebogang Dinah; TSOKU, Johannes Tshepiso. Modelling the BRICS Exchange Rates Using the Vector Autoregressive (VAR) Model. Journal of Economics and Behavioral Studies, [S.l.], v. 10, n. 5, p. 220-229, nov. 2018. ISSN 2220-6140. Available at: <https://ifrnd.org/journal/index.php/jebs/article/view/2511>. Date accessed: 23 mar. 2019. doi: https://doi.org/10.22610/jebs.v10i5.2511.
Section
Research Paper