Forecasting exchange rate for the Australian dollar via-à-vis the US dollar using multivariate time-series models

Hoque, Azizul; Latif, Abdul

doi:10.1080/00036849300000047

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…Kim, 2008;Sarantis & Stewart, 1995;Taylor, 1988). Hoque and Latif (1993) found that including error-correction term increased the forecasting power against the simple vector autoregression (VAR). Mark (1995) performed out-of-sample exchange rate forecasts using the error correction method of four currencies visà-vis the US dollar.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Markov switching model for BRICS currencies' exchange rates

Kumar,

Ahmad,

Uddin

2024

Journal of Forecasting

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Exchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time‐varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis‐à‐vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal‐gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime‐specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime‐specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out‐of‐sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model.

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Section: Introductionmentioning

confidence: 99%

Bayesian Markov switching model for BRICS currencies' exchange rates

Kumar,

Ahmad,

Uddin

2024

Journal of Forecasting

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“…Among the most relevant contributions aimed at comparing the forecasting abilities of multivariate time series models, Hoque and Latif (1993) use vector error correction models, unrestricted vector autoregressions and Bayesian VAR in order to forecast the Australian Dollar-US Dollar exchange rate, and conclude on the supremacy of the latter over the unrestricted VAR, and of the VEC over the other two. Liu et al (1994) perform a very similar exercise using the exchange rate of the US Dollar against the Japanese Yen, the Canadian Dollar and the Deutsche Mark, and conclude on the higher forecasting abilities of both stochastically and deterministically restricted models when compared to unrestricted ones.…”

Section: Introductionmentioning

confidence: 99%

Beating the random walk in Central and Eastern Europe

Cuaresma

Hlouskova

2005

J. Forecast.

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We compare the accuracy of vector autoregressive (VAR), restricted vector autoregressive (RVAR), Bayesian vector autoregressive (BVAR), vector error correction (VEC) and Bayesian error correction (BVEC) models in forecasting the exchange rates of five Central and Eastern European currencies (Czech Koruna, Hungarian Forint, Slovak Koruna, Slovenian Tolar and Polish Zloty) against the US Dollar and the Euro. Although these models tend to outperform the random walk model for long-term predictions (6 months ahead and beyond), even the best models in terms of average prediction error fail to reject the test of equality of forecasting accuracy against the random walk model in short-term predictions. Copyright © 2005 John Wiley & Sons, Ltd.

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“…One exception is Mizrach (1992), who ®nds a multivariate nearestneighbours model has limited forecasting power for three EMS exchange rates. VAR models have also been used to forecast exchange rates (for example Hoque and Latif, 1993;Liu, Gerlow, and Irwin, 1994;Sarantis and Stewart, 1995;Tse, 1995). The ®rst three applications have beeǹ structural' (rather than time series in nature) and also linear.…”

Section: Introductionmentioning

confidence: 99%