A mixed-frequency approach for exchange rates predictions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866911490551840768 |
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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 |
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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 |