Early warning of exchange rate risk based on structural shocks in international oil prices using the LSTM neural network model

Yinglan Zhao, Chen Feng, Nuo Xu, Song Peng and Chang Liu

Energy Economics, 2023, vol. 126, issue C

Abstract: The Russia–Ukraine conflict has caused an external shock to the oil market as the exchange rate risk increased with significant depreciation of major currencies relative to the U.S. dollar (USD). In this paper, structural shocks to oil prices calculated by the Ready (2018) method are introduced in addition to traditional exchange rate predictor variables such as oil prices. Utilising a long short-term memory neural network model, we verify the early warning effect of structural shocks to international oil prices on exchange rate risk in the context of the Russia–Ukraine geo-conflict. We have several empirical findings. First, the variance decomposition method indicates that oil price shocks affect the euro-dollar exchange rate risk. After the conflict between Russia and Ukraine, the euro exchange rate risk continued to rise significantly, mainly affected by supply shocks. Second, compared with traditional prediction models, the LSTM neural network model has a higher prediction accuracy, which is conducive to improving the early warning performance of exchange rate risks and maintaining the stability of the foreign exchange market. Third, the introduction of oil price shock can fully extract and use the information from oil price changes, which significantly improves the learning performance of the oil price early warning model of exchange rate risk. Fourth, the introduction of oil price risk shocks in the whole sample, the introduction of oil price demand shocks before the Russia-Ukraine conflict, and the introduction of oil price supply shocks, especially negative supply shocks, after the conflict between Russia and Ukraine, can warn exchange rate risks more effectively. This study proposes a new method for early warning of exchange rate risk that can help cross-border enterprises and government policymakers to respond to unexpected events and effectively mitigate associated risks.

Keywords: Russia–Ukraine geopolitical conflict; Oil price shocks; EUR/USD exchange rate risks; LSTM neural network model (search for similar items in EconPapers)
JEL-codes: C45 D74 E44 F31 F51 Q43 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:126:y:2023:i:c:s014098832300419x

DOI: 10.1016/j.eneco.2023.106921

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