Predicting exchange rates using a novel “cointegration based neuro-fuzzy system”

Gharleghi, Behrooz; Shaari, Abu Hassan; Shafighi, Najla

doi:10.1016/j.inteco.2013.12.001

Cited by 11 publications

(9 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years there has been a growing interest in machine learning (ML) techniques for economic forecasting (Weron 2014;Gharleghi et al 2014;Kock and Teräsvirta 2014;Ben Taieb et al 2012;Crone et al 2011;Andrawis et al 2011;Carbonneau et al 2008). ML is based on the construction of algorithms that learn through experience.…”

Section: Introductionmentioning

confidence: 99%

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

Clavería

Monte

Porras

2016

SERIEs

View full text Add to dashboard Cite

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

Clavería

Monte

Porras

2016

SERIEs

View full text Add to dashboard Cite

show abstract

“…Granger and Engle have indicated that if the linear combination of two or more nonstationary time series is stationary, then there is a cointegration relationship between the nonstationary time series (Engle & Granger, ). The combination is known as “the cointegrating equation” and can be interpreted as a long‐term equilibrium relationship among the time series (Gharleghi et al, ). In this study, the Johansen test is utilized to determine whether the cointegration relationship exists or not.…”

Section: Experimental Studymentioning

confidence: 99%

A hybrid model considering cointegration for interval‐valued pork price forecasting in China

Zhang

Mugera

et al. 2020

Journal of Forecasting

View full text Add to dashboard Cite

Compared with point forecasting, interval forecasting is believed to be more effective and helpful in decision making, as it provides more information about the data generation process. Based on the well-established "linear and nonlinear" modeling framework, a hybrid model is proposed by coupling the vector error correction model (VECM) with artificial intelligence models which consider the cointegration relationship between the lower and upper bounds (Coin-AIs). VECM is first employed to fit the original time series with the residual error series modeled by Coin-AIs. Using pork price as a research sample, the empirical results statistically confirm the superiority of the proposed VECM-CoinAIs over other competing models, which include six single models and six hybrid models. This result suggests that considering the cointegration relationship is a workable direction for improving the forecast performance of the interval-valued time series. Moreover, with a reasonable data transformation process, interval forecasting is proven to be more accurate than point forecasting.

show abstract

“…Industrial production index is an indicator that helps to measure all changes in production for the manufacturing and utilities; therefore it is good index for gross domestic product or output (Gharleghi, Shaari, and Shafighi, 2014). IPI is a substantial implement in estimating possible future output and performance in economic policy.…”

Section: Industrial Production Indexmentioning

confidence: 99%

The Augmented Monetary Model and Exchange Rate Fluctuations; Case of RUB/USD

Zhuk¹,

Gharleghi²

2015

ASS

Self Cite

View full text Add to dashboard Cite

The present study is examining the impact of macro-economic variables towards exchange rate determination in Russian Federation currency, Ruble. In order to achieve this aim, the monetary model of exchange rate determination is applied here. This study contributes to the literature by adding a new variable of Oil Price into the flexible price monetary model. This objective is examined through a regression analysis. The data is collected from 1998-2013 and natural logarithm is taken. The empirical results revealed that the augmented monetary model is not validated. But there found a significant relationship between oil price and Ruble. The industrial production function is identified not to be significant in the model.

show abstract

Predicting exchange rates using a novel “cointegration based neuro-fuzzy system”

Cited by 11 publications

References 38 publications

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

A hybrid model considering cointegration for interval‐valued pork price forecasting in China

The Augmented Monetary Model and Exchange Rate Fluctuations; Case of RUB/USD

Contact Info

Product

Resources

About