Estimating structural exchange rate models by artificial neural networks

Plasmans, Joseph; Verkooijen, W.J.H.; Daniels, Hennie

doi:10.1080/096031098332844

Cited by 34 publications

(20 citation statements)

References 27 publications

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“…Some other studies involving ANNs were less encouraging. Plasmans et al (1998) and Verkooijen (1996) used macroeconomic models, but they could not produce any satisfactory monthly forecasts. However, Zhang and Hu (1998) modeled exchange rate as depending non-linearly on its past values, and their model outperformed simple linear models, but they never compared it to a random walk.…”

Section: Introductionmentioning

confidence: 99%

Non‐linear, non‐parametric, non‐fundamental exchange rate forecasting

Gradojević

Yang

2006

Journal of Forecasting

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This paper employs a non-parametric method to forecast high-frequency Canadian|US dollar exchange rate. The introduction of a microstructure variable, order flow, substantially improves the predictive power of both linear and non-linear models. The non-linear models outperform random walk and linear models based on a number of recursive out-of-sample forecasts. Two main criteria that are applied to evaluate model performance are root mean squared error (RMSE) and the ability to predict the direction of exchange rate moves. The artificial neural network (ANN) model is consistently better in RMSE to random walk and linear models for the various out-of-sample set sizes. Moreover, ANN performs better than other models in terms of percentage of correctly predicted exchange rate changes. The empirical results suggest that optimal ANN architecture is superior to random walk and any linear competing model for high-frequency exchange rate forecasting. Copyright © 2006 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Non‐linear, non‐parametric, non‐fundamental exchange rate forecasting

Gradojević

Yang

2006

Journal of Forecasting

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“…This way, the expected real exchange rate gap is partly covered by the previous gap and partly by the exchange rate difference. Despite the widespread use of UIP, estimates for η are not one as UIP predicts, but usually small and quantitatively similar for OECD countries - Plasmans, Verkooijen, and Daniëls (1998) and ASEAN countries -Boldea, Engwerda, Michalak, Plasmans, and Salmah (2010). We show in Section 7 that the exchange rate dynamics is well-specified across all models and identification strategies we use.…”

Section: Open Economymentioning

confidence: 90%

“…The third equation specifies the exchange rate path. Unlike in Berg, Karam, and Laxton (2006), the real exchange rate dynamics is a linear expected exchange rate rule, known as "partially uncovered interest parity" -see Plasmans, Verkooijen, andDaniëls (1998) andDe Grauwe andVansteenkiste (2007). Unlike Mkrtchyan, Dabla-Norris, and Stepanyan (2009), we do not impose uncovered interest parity (UIP), as it would require perfect capital mobility and complete financial markets, features that are unlikely to hold for developing countries like Armenia.…”

Section: Open Economymentioning

confidence: 96%

Structural Versus Matching Estimation: Transmission Mechanisms in Armenia

Poghosyan

Boldea

2011

SSRN Journal

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Opting for structural or reduced form estimation is often hard to justify if one wants to both learn about the structure of the economy and obtain accurate predictions. In this paper, we show that using both structural and reduced form estimates simultaneously can lead to more accurate policy predictions. Our findings are based on using new information criteria whose econometric properties allow us to pick for both methods the impulse responses that are valid and relevant for prediction. We illustrate our findings in the context of analyzing the monetary transmission mechanism for Armenia. Based on picking valid and relevant information from both structural and reduced form matching estimation, our findings suggest that the interest rate targeting and the exchange rate channel are well specified and strongly reinforce each other in promoting the recent double-digit growth Armenia experienced before the crisis.JEL Classification: E52, E58, C36, C52, C53

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“…2 For an overview of the Neural Networks (NN) literature, see Poggio and Girosi (1990), Hertz et al (1991), White (1992), Hutchinson et al (1994). Plasmans et al (1998) and Franses and Homelen (1998) investigated the ability of NN on forecasting exchange rates. The non-linearity found in exchange rates is due to ARCH effects.…”

Section: The Arch Processmentioning

confidence: 99%

Assessing the performance of a prediction error criterion model selection algorithm in the context of ARCH models

Degiannakis

Xekalaki

2007

Applied Financial Economics

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A number of ARCH models are considered in the framework of evaluating the performance of a method for model selection based on a standardized prediction error criterion (SPEC). According to this method, the ARCH model with the lowest sum of squared standardized forecasting errors is selected for predicting future volatility. A number of statistical criteria, that measure the distance between predicted and inter-day realized volatility, are used to examine the performance of a model to predict future volatility, for forecasting horizons ranging from one day to 100 days ahead. The results reveal that the SPEC model selection procedure has a satisfactory performance in picking that model that generates 'better' volatility predictions. A comparison of the SPEC algorithm with a set of other model evaluation criteria yields similar findings. It appears, therefore, that it can be regarded as a tool in guiding the choice of the appropriate model for predicting future volatility, with applications in evaluating portfolios, managing financial risk and creating speculative strategies with options.

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Estimating structural exchange rate models by artificial neural networks

Cited by 34 publications

References 27 publications

Non‐linear, non‐parametric, non‐fundamental exchange rate forecasting

Non‐linear, non‐parametric, non‐fundamental exchange rate forecasting

Structural Versus Matching Estimation: Transmission Mechanisms in Armenia

Assessing the performance of a prediction error criterion model selection algorithm in the context of ARCH models

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