Forecasting the Uk/Us Exchange Rate With Divisia Monetary Models and Neural Networks

Bissoondeeal, Rakesh K.; Karoglou, Michail; Gazely, Alicia M.

doi:10.1111/j.1467-9485.2010.00538.x

Cited by 13 publications

(8 citation statements)

References 50 publications

(65 reference statements)

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“…Bissoondeeal et al (2011) stressed that the most accurate forecasts of the UK/U,S. Bissoondeeal et al (2011) stressed that the most accurate forecasts of the UK/U,S.…”

Section: Literature Review On Exchange Rate Forecastingmentioning

confidence: 99%

The Construction and Application of a New Exchange Rate Forecast Model Combining ARIMA with a Chaotic BP Algorithm

Mingye

et al. 2015

Emerging Markets Finance and Trade

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This article introduces a new model that combines an ARIMA with a chaotic BP (Backforward Propagation Neural Network) algorithm for exchange rate forecasting purposes, which is based on sample data collected from January 4, 2010, to October 20, 2011. The forecast of the exchange rate trend is then provided for the subsequent twenty-five days. Other models are also constructed, such as the ARIMA, BP, ARIMA, and BP algorithms, in order to evaluate the forecast accuracy. Based on our results, the combination of an ARIMA and a chaotic BP algorithm outperforms all other models in terms of the statistical accuracy of short-term forecasts.

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“…Bissoondeeal et al (2011) stressed that the most accurate forecasts of the UK/U,S. Bissoondeeal et al (2011) stressed that the most accurate forecasts of the UK/U,S.…”

Section: Literature Review On Exchange Rate Forecastingmentioning

confidence: 99%

The Construction and Application of a New Exchange Rate Forecast Model Combining ARIMA with a Chaotic BP Algorithm

Mingye

et al. 2015

Emerging Markets Finance and Trade

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“…Following studies such as Bissoondeeal et al . (), we use the Treasury bill rate (TBR), a short term rate, as the opportunity cost variable. Data on consumer price index (CPI), GDP, TBR and ULC are obtained from Datastream.…”

Section: Datamentioning

confidence: 99%

“…Household sector Simple Sum and Divisia aggregates, for the M4 level of aggregation, were obtained from the Bank of England's website. 8 Following studies such as Bissoondeeal et al (2011), we use the Treasury bill rate (TBR), a short term rate, as the opportunity cost variable. Data on consumer price index (CPI), GDP, TBR and ULC are obtained from Datastream.…”

Section: Data and Preliminary Analysismentioning

confidence: 99%

Breaks in the UK Household Sector Money Demand Function

Bissoondeeal

Karoglou

Mullineux

2013

The Manchester School

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We use non‐parametric procedures to identify breaks in the underlying series of UK household sector money demand functions. Money demand functions are estimated using cointegration techniques and by employing both the Simple Sum and Divisia measures of money. P‐star models are also estimated for out‐of‐sample inflation forecasting. Our findings suggest that the presence of breaks affects both the estimation of cointegrated money demand functions and the inflation forecasts. P‐star forecast models based on Divisia measures appear more accurate at longer horizons and the majority of models with fundamentals perform better than a random walk model.

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“…The RMB forecasting from BPNN is precise and efficient. Bissoondeeal et al (2011) compared UK/US exchange rate forecasting performance of linear and nonlinear models based on monetary fundamentals, to a RW model. They concluded that the most accurate forecasts of the UK/US exchange rate are obtained with a nonlinear model.…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Thai Baht and Oil Price: A Neural Network Model

Harnphattananusorn¹

2020

IJEEP

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The research aims to investigate the relationship between the exchange rate of Thai Baht against USD and oil price using daily data from January 1999 to March 2019. To test whether there is the long-run relationship between selected variables, the Johansen cointegration method is employed. The results indicate the evidence of long-run relationship between oil price and the Thai Baht. Then, Artificial Neural Networks (ANN) technique is employed for estimation. For ANN estimation, the results suggest that the most influential variables for the Thai Baht is gold price and oil price is the third influential variable for Thai Baht. The research applies the mean squared error, root mean square error and the mean absolute percentage error to measure the error. The results suggest that ANN estimation is more efficient than linear model in term of error estimation.

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Forecasting the Uk/Us Exchange Rate With Divisia Monetary Models and Neural Networks

Cited by 13 publications

References 50 publications

The Construction and Application of a New Exchange Rate Forecast Model Combining ARIMA with a Chaotic BP Algorithm

The Construction and Application of a New Exchange Rate Forecast Model Combining ARIMA with a Chaotic BP Algorithm

Breaks in the UK Household Sector Money Demand Function

Relationship Between Thai Baht and Oil Price: A Neural Network Model

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