Forecasting the exchange rate using nonlinear Taylor rule based models

Wang, Rudan; Morley, Bruce; Stamatogiannis, Michalis P.

doi:10.1016/j.ijforecast.2018.07.017

Cited by 17 publications

(10 citation statements)

References 45 publications

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“…Based on the Taylor rule model, we take capital flow as a variable, and assume that capital flows (CFUS), China-US output gap (GAP), China-US inflation gap (CPI), and China-US interest rate gap (ID) have negative impacts on the RMB exchange rate in the context of the pandemic. Interested readers are referred to Wang et al (2019). We select monthly data from January 2005 to June 2020, and use the VECM for the empirical test.…”

Section: Introduction I Introductionmentioning

confidence: 99%

“…On the other hand, Chen et al (2017) show the difference in the direction and degree of influence on the time-varying characteristics of asset prices through interest rate differential and exchange rate expectations. Moreover, Wang et al (2019) endogenously treat international capital flows to investigate the dynamic determination mechanism of the RMB exchange rate. Given the popularity of the Taylor rule model, we employ it for our hypothesis test.…”

Section: Introduction I Introductionmentioning

confidence: 99%

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An Analysis of the Dynamic Asymmetric Impact of the COVID-19 Pandemic on the RMB Exchange Rate

Fang

Zhang

2021

Asian Economics Letters

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In this paper, we assume that the COVID-19 pandemic exerts a time-varying asymmetric impact on the RMB exchange rate. Based on the Taylor rule model, we examine the RMB exchange rate fluctuations around the outbreak of COVID-19. We find that the RMB rate rose steadily before the outbreak but fluctuated during the pandemic. This shows that the pandemic had a transient time-varying impact on the RMB exchange rate.

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Section: Introduction I Introductionmentioning

confidence: 99%

Section: Introduction I Introductionmentioning

confidence: 99%

An Analysis of the Dynamic Asymmetric Impact of the COVID-19 Pandemic on the RMB Exchange Rate

Fang

Zhang

2021

Asian Economics Letters

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“…However, applications require a more "personalized" approach to choosing a most useful and effective nonlinearity. On this pathway it seems fruitful, e.g., to forecast exchange rates using logistic and exponential nonlinearities [10], or to predict the daily electrical load of a power system using periodic autoregressive models [11] and multidimensional time series [12].…”

Section: Introductionmentioning

confidence: 99%

Elements of Randomized Forecasting and Its Application to Daily Electrical Load Prediction in a Regional Power System

Popkov

Dubnov

2020

Autom Remote Control

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A randomized forecasting method based on the generation of ensembles of entropyoptimal forecasting trajectories is developed. The latter are generated by randomized dynamic regression models containing random parameters, measurement noises, and a random input. The probability density functions of random parameters and measurement noises are estimated using real data within the randomized machine learning procedure. The ensembles of forecasting trajectories are generated by the sampling of the entropy-optimal probability distributions. This procedure is used for the randomized prediction of the daily electrical load of a regional power system. A stochastic oscillatory dynamic regression model is designed. One-, two-, and three-day forecasts of the electrical load are constructed, and their errors are analyzed.

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“…There were many studies that use nonlinearity test in their research. Wang et al [6] used Terasvirta test in the identification of nonlinearity in his research. Lacheheb et al [7] used Ramsey test for identification of nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

The Performance of Ramsey Test, White Test and Terasvirta Test in Detecting Nonlinearity

2020

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Abstrak-The objective of this research is to compare Ramsey test, White test and Terasvirta test in the identification of nonlinearity. Ramsey test is a test based on the regression specification error test. While White test and Terasvirta test are based on neural network models. The difference between White test and Terasvirta test is in determining its weight, White test based on random sampling, while Terasvirta test based on Taylor expansion. Simulation studies are carried out with various scenarios in each test by generating linear models, linear models with outliers and nonlinear models. The results of the simulation study showed that Terasvirta test had better power than Ramsey test and White test in detecting nonlinearity. Terasvirta test is also more sensitive to the presence of outliers in linear models.

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Forecasting the exchange rate using nonlinear Taylor rule based models

Cited by 17 publications

References 45 publications

An Analysis of the Dynamic Asymmetric Impact of the COVID-19 Pandemic on the RMB Exchange Rate

An Analysis of the Dynamic Asymmetric Impact of the COVID-19 Pandemic on the RMB Exchange Rate

Elements of Randomized Forecasting and Its Application to Daily Electrical Load Prediction in a Regional Power System

The Performance of Ramsey Test, White Test and Terasvirta Test in Detecting Nonlinearity

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