2018, Volume 71 - Issue 2
RSS feed citation: At RePEc
Publication date: 02 May 2018
SOME INTERNATIONAL FINANCIAL CONTRIBUTIONS: EMPIRICAL RESULTS AND POLICY IMPLICATIONSRead the article
ASSESSING PORTFOLIO MARKET RISK IN THE BRICS ECONOMIES: USE OF MULTIVARIATE GARCH MODELSRead the article
BANK COMPETITION, CONCENTRATION AND RISK-TAKING IN THE UAE BANKING INDUSTRYRead the article
Imad MOOSA, School of Economics, Finance and Marketing, RMIT University, Melbourne, Australia
John VAZ, Department of Accounting and Finance, Monash University, Clayton, Victoria, Australia
The objective of this paper is to determine whether direct forecasting is more or less accurate than indirect forecasting when applied to the cross exchange rate as a defined variable. By using the flexible price monetary model to represent three cross rates, the results show that indirect forecasting is better than direct forecasting, when forecasting accuracy is measured in terms of the root mean square error (RMSE), for two of the three cross rates examined while the opposite is true for the third rate. However, no difference is apparent when performance is measured in terms of directional accuracy. It is concluded that the choice between direct and indirect forecasting is an empirical issue and that the results of such an exercise are case-specific.
C53, F31, F37
Forecasting, Random Walk, Exchange Rate Models, Cross Exchange Rates
Ashley, R., C.W.J. Granger and R. Schmalensee (1980), “Advertising and Aggregate Consumption: An Analysis of Causality”, Econometrica, 48(5), 1149-1167.
Bermingham, C. and A. D’agostino (2011), “Understanding and Forecasting Aggregate and Disaggregate Price Dynamics”, European Central Bank, Working Paper Series, No. 1365, August.
Bidarkota, P.V. (1998), “The Comparative Forecast Performance of Univariate and Multivariate Models: An Application to Real Interest Rate Forecasting”, International Journal of Forecasting, 14(4), 457-468.
Edwards, J.B. and G.H. Orcutt (1969), “Should Aggregation Prior to Estimation be the Rule?”, Review of Economics and Statistics, 51(4), 409-420.
Engel, E.M. (1984), “A Unified Approach to the Study of Sums, Products, Time-Aggregation and other Functions of ARMA Processes”, Journal of Time Series Analysis , 5(3), 159-171.
Evans, M.D.D. and R.K. Lyons (2005), “Meese-Rogoff Redux: Micro-Based Exchange Rate Forecasting”, American Economic Review, 95(2), 405-414.
Fliedner, G. (1999), “An Investigation of Aggregate Variable Time Series Forecast Strategies with Specific Subaggregate Time Series Statistical Correlation”, Computers & Operations Research, 26(10-11), 1133-1149.
Grunfeld, Y. and Z. Griliches (1960), “Is Aggregation Necessarily Bad?”, Review of Economics and Statistics, 42(1), 1-13.
Hafer, R.W. and S.E. Hein (1984), “Predicting the Money Multiplier: Forecasts from Component and Aggregate Models”, Journal of Monetary Economics, 14(3), 375-384.
Hendry, D.F. and K. Hubrich (2005), “Forecasting Aggregates by Disaggregates”, Working Paper, Department of Economics, Oxford University, <http://www.nuff.ox.ac.uk/users/hendry/HendryHubrich05.pdf>.
Johannes, J.M. and R.H. Rasche (1979), “Predicting the Money Multiplier”, Journal of Monetary Economics, 5(3), 301-325.
Johannes, J.M. and R.H. Rasche (1981), “Can the Reserves Approach to Monetary Control Really Work?”, Journal of Money, Credit and Banking, 13(3), 298-313.
Kang, H. (1986), “Univariate ARIMA Forecasts of Defined Variables”, Journal of Business & Economic Statistics, 4(1), 81-86.
Kohn, R. (1982), “When is an Aggregate of a Time Series Efficiently Forecast by its Past?”, Journal of Econometrics, 18(3), 337-349.
Lütkepohl, H. (1984), “Forecasting Contemporaneously Aggregated Vector ARMA Processes”, Journal of Business and Economic Statistics, 2(3), 201-214.
Meese, R. and K. Rogoff (1983), “Empirical Exchange Rate Models of the Seventies: Do They Fit out of Sample?”, Journal of International Economics, 14(1-2), 3-24.
Moosa, I.A. (2013), “Why is it so Difficult to Outperform the Random Walk in Exchange Rate Forecasting?”, Applied Economics, 45(23), 3340-3346.
Moosa, I.A. and K. Burns (2012), “Can Exchange Rate Models Outperform the Random Walk? Magnitude, Direction and Profitability as Criteria”, Economia Internazionale/International Economics, 65(3), 473-490.
Moosa, I.A. and J.H. Kim (2001), “Forecasting the Real Exchange Rate as a Defined Variable”, Journal of Economic Research, 6(1), 1-27.
Moosa, I.A. and J.H. Kim (2004a), “Direct and Indirect Forecasting of the Money Multiplier and Velocity of Circulation in the U.K.”, International Economic Journal, 18(1), 103-118.
Moosa, I.A. and J.H. Kim (2004b), “Forecasting the Velocity of Circulation in the Japanese Economy”, Hititsobashi Journal of Economics, 45(1), 1-14.
Moosa, I.A. and J.H. Kim (2004c), “Predicting the Money Multiplier and Velocity of Circulation in the U.S. Economy: Direct versus Indirect Forecasting Methods”, Asian-African Journal of Economics and Econometrics, 4(1), 13-34.
Orcutt, G.H., H.W. Watts and J.B. Edwards (1968), “Data Aggregation and Information Loss”, American Economic Review, 58(4), 773-787.
Silhan, P.A. (1986), “Direct versus Indirect ARIMA Forecasts of Defined Variables: Some Further Evidence Based on Corporate Accounting Data”, BEBR Faculty Working Paper No. 1292, <https://www.ideals.illinois.edu/bitstream/handle/2142/28421/directversusindi1292silh.pdf?sequence=1>.
Tiao, G.C. and I. Guttman (1980), “Forecasting Contemporal Aggregates of Multiple Time Series”, Journal of Econometrics, 12(2), 219-230.
Wei, W.W.S. and B. Abraham (1981), “Forecasting Contemporal Time Series Aggregates”, Communications in Statistics – Theory and Methods, 10(13), 1335-1344.