Learning to Forecast the Exchange Rate: Two Competing Approaches

Paul De Grauwe and Agnieszka Markiewicz ()

No 367, Computing in Economics and Finance 2006 from Society for Computational Economics

Abstract: In this paper, we investigate the behavior of the exchange rate within the framework of an asset pricing model. We assume boundedly rational agents who use simple rules to forecast the future exchange rate. They test these rules continuously using two learning mechanisms. The first one, the fitness method, assumes that agents evaluate forecasts by computing their past profitability. In the second mechanism, agents learn to improve these rules using statistical methods. First, we find that both learning mechanisms reveal the fundamental value of the exchange rate in the steady state. Second, both mechanisms mimic regularities observed in the foreign exchange markets, namely exchange rate disconnect and excess volatility. Fitness learning rule generates the disconnection at different frequencies, while the statistical method has this ability only at the high frequencies. Statistical learning can produce excess volatility of magnitude closer to reality than fitness learning but can also lead to explosive solutions

Keywords: Exchange Rate Economics; Adaptive Learning; Behavioral Finance (search for similar items in EconPapers)
JEL-codes: F31 F41 (search for similar items in EconPapers)
Date: 2006-07-04
New Economics Papers: this item is included in nep-cba, nep-cbe, nep-fmk, nep-for, nep-ifn and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Related works:
Journal Article: Learning to forecast the exchange rate: Two competing approaches (2013)
Working Paper: Learning to Forecast the Exchange Rate: Two Competing Approaches (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecfa:367

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