scholarly journals How Did the Financial Crisis Affect the Forecasting Performance of Time Series Exchange Rate Models? Evidence from Euro Rates

2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Dimitris G. Kirikos
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Financial Crisis ◽  
Forecasting Performance ◽  
Exchange Rate Models

Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them

2021 ◽  
Vol 59 (4) ◽  
pp. 1135-1190
Author(s):  
Barbara Rossi
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Exchange Rates ◽  
Time Variation ◽  
Asset Prices ◽  
Output Growth ◽  
Economic Time Series ◽  
Forecast Uncertainty ◽  
Forecasting Performance ◽  
Economic Time

This article provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007–08, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth, and inflation. In the context of unstable environments, I discuss how to assess models’ forecasting ability; how to robustify models’ estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models’ parameters are neither necessary nor sufficient to generate time variation in models’ forecasting performance: thus, one should not test for breaks in models’ parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models’ forecasting performance are more appropriate than traditional, average measures. (JEL C51, C53, E31, E32, E37, F37)

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

scholarly journals Testing monetary exchange rate models with the Westerlund panel cointegration test

Economica ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 172-179
Author(s):  
Andrea Szabó
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Empirical Support ◽  
Panel Cointegration ◽  
Empirical Literature ◽  
Long Run ◽  
Cointegration Tests ◽  
Panel Cointegration Test ◽  
Exchange Rate Models ◽  
Monetary Models

Time series testing of long-run monetary models of exchange rate determination in most cases fails to support the conjectures of the theory. The empirical literature increasingly uses the panel technique when testing monetary exchange rate models because the power of the panel unit root and panel cointegration tests seems higher than the pure time series tests. In this paper we examine the validity of the monetary exchange rate models over the period 1996Q1-2011Q4 for US dollar exchange rates of 15 OECD countries using Westerlund’s 2007 panel cointegration tests. We found moderate empirical support for monetary exchange rate models.

scholarly journals Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting

2021 ◽  
Vol 11 (12) ◽  
pp. 5658
Author(s):  
Pedro Escudero ◽  
Willian Alcocer ◽  
Jenny Paredes
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Short Term Memory ◽  
Moving Average ◽  
Validation Dataset ◽  
Short Term ◽  
Random Behavior ◽  
Forecasting Performance ◽  
Using Data

Analyzing the future behaviors of currency pairs represents a priority for governments, financial institutions, and investors, who use this type of analysis to understand the economic situation of a country and determine when to sell and buy goods or services from a particular location. Several models are used to forecast this type of time series with reasonable accuracy. However, due to the random behavior of these time series, achieving good forecasting performance represents a significant challenge. In this paper, we compare forecasting models to evaluate their accuracy in the short term using data on the EUR/USD exchange rate. For this purpose, we used three methods: Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network (RNN) of the Elman type, and Long Short-Term Memory (LSTM). The analyzed period spanned from 2 January 1998, to 31 December 2019, and was divided into training and validation datasets. We performed forecasting calculations to predict windows with six different forecasting horizons. We found that the window of one month with 22 observations better matched the validation dataset in the short term compared to the other windows. Theil’s U coefficients calculated for this window were 0.04743, 0.002625, and 0.001808 for the ARIMA, Elman, and LSTM networks, respectively. LSTM provided the best forecast in the short term, while Elman provided the best forecast in the long term.

Sign in / Sign up

Export Citation Format

Share Document