Out-of-sample forecasting performance of single equation monetary exchange rate models in Norwegian currency markets

1999 ◽  
Vol 9 (6) ◽  
pp. 545-550 ◽  
Author(s):  
Harald Reinton ◽  
Steven Ongena
Keyword(s):  
Exchange Rate ◽  
Single Equation ◽  
Currency Markets ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Exchange Rate Models

scholarly journals The out-of-sample forecasting performance of variable parameter exchange rate models in South Africa

1995 ◽  
Vol 26 (2) ◽  
pp. 64-71
Author(s):  
Gilbert Wesso
Keyword(s):  
Exchange Rate ◽  
Variable Coefficient ◽  
Variable Parameter ◽  
Structural Models ◽  
Predictive Performance ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Lagged Dependent Variable ◽  
Exchange Rate Models ◽  
Over Time

In this article the out-of-sample forecasting performance of exchange rate determination is examined without imposing the restriction that coefficients are fixed over time. Both fixed and variable coefficient versions of conventional structural models are considered, with and without a lagged dependent variable. A Variable Parameter Regression (VPR) technique based on recursive application of the Kalman filter is used to improve the predictive performance of a class oi monetary exchange rate models.

FORECASTING PERFORMANCE OF EXCHANGE RATE MODELS WITH HEAVY MOVING AVERAGE OPERATORS

2019 ◽  
Vol 24 (02) ◽  
Author(s):  
L. Espinoza-Audelo ◽  
E. Aviles-Ochoa ◽  
E. Leon-Castro ◽  
F. Blanco-Mesa
Keyword(s):  
Exchange Rate ◽  
Moving Average ◽  
Forecasting Performance ◽  
Exchange Rate Models

Google Trends and Structural Exchange Rate Models for Turkish Lira–US Dollar Exchange Rate

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Levent Bulut ◽  
Can Dogan
Keyword(s):  
Exchange Rate ◽  
Random Walk ◽  
Purchasing Power Parity ◽  
Google Trends ◽  
Power Parity ◽  
Dollar Exchange Rate ◽  
Out Of Sample ◽  
Us Dollar ◽  
Exchange Rate Models ◽  
Turkish Lira

Abstract In this paper, we use Google Trends data to proxy macro fundamentals that are related to two conventional structural determination of exchange rate models: purchasing power parity model and the monetary exchange rate determination model. We assess forecasting performance of Google Trends based models against random walk null on Turkish Lira–US Dollar exchange rate for the period of January 2004 to August 2015. We offer a three-step methodology for query selection for macro fundamentals in Turkey and the US. In out-of-sample forecasting, results show better performance against no-change random walk predictions for specifications both when we use Google Trends data as the only exchange rate predictor or augment it with exchange rate fundamentals. We also find that Google Trends data has limited predictive power when used in year-on-year growth rate format.

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