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Auteurs principaux: Mattera, Raffaele, Misuraca, Michelangelo, Scepi, Germana, Spano, Maria
Format: Preprint
Publié: 2021
Sujets:
Accès en ligne:https://arxiv.org/abs/2106.00283
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author Mattera, Raffaele
Misuraca, Michelangelo
Scepi, Germana
Spano, Maria
author_facet Mattera, Raffaele
Misuraca, Michelangelo
Scepi, Germana
Spano, Maria
contents Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding alternative predictors and statistical models based on temporal aggregation. In this paper, we propose an approach based on mixed frequency models to overcome the lack of information caused by temporal aggregation. We show the effectiveness of our approach in comparison with other proposed methods by performing CAD/USD exchange rate predictions.
format Preprint
id arxiv_https___arxiv_org_abs_2106_00283
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle A mixed-frequency approach for exchange rates predictions
Mattera, Raffaele
Misuraca, Michelangelo
Scepi, Germana
Spano, Maria
Applications
Machine Learning
62J05
Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding alternative predictors and statistical models based on temporal aggregation. In this paper, we propose an approach based on mixed frequency models to overcome the lack of information caused by temporal aggregation. We show the effectiveness of our approach in comparison with other proposed methods by performing CAD/USD exchange rate predictions.
title A mixed-frequency approach for exchange rates predictions
topic Applications
Machine Learning
62J05
url https://arxiv.org/abs/2106.00283