Article: Currency crisis early warning signal mechanisms based on dynamic machine learning Journal: International Journal of Applied Decision Sciences (IJADS) 2024 Vol.17 No.4 pp.466 - 496 Abstract: The primary aim of this study is to investigate whether credit default swaps (CDS) serve as an early warning indicator for currency crises. This is done by examining both stock and flow variables, including the external debt stock and reserves (comprising foreign currency and gold), within the context of free exchange rate regimes. An original aspect of the study, which differs from other studies, is the machine learning methods used and the inclusion into the model of both one lag and lag values of the CDs variable, which is an inclusive crisis indicator. The CDS variable was not detected as a strong signal by the logistic regression model. However, the best-performing XGBoost and GB algorithms show the differenced, and one-lagged values of the CDS variable produce significant signals in forecasting currency crises. Consistent with theoretical underpinnings of study on currency crises, this implies that central banks proactively reacted by increasing monetary policy interest rates and the non-current value CDS but its lagged value performed strong early warning signal that is a follower or supplementary indicator of the credibility of monetary authorities and policies. These results demonstrate that the high and rising interest rate signifies that domestic currencies are being supported against speculative attacks. Inderscience Publishers - linking academia, business and industry through research

Title: Currency crisis early warning signal mechanisms based on dynamic machine learning

Authors: Ömür Saltık; Wasim ul Rehman; Bahadır İldokuz; Süleyman Değirmen; Ahmet Şengönül

Addresses: Economic Research Department, Marbaş Securities, İstanbul, Turkey ' Department of Business Administration, University of the Punjab, Lahore, Pakistan ' Research Department, Info Yatırım (Info Investment), İstanbul, Turkey ' Department of Economics, Mersin University, Turkey ' Department of Econometrics, Sivas Cumhuriyet Üniversitesi, Sivas, Turkey

Abstract: The primary aim of this study is to investigate whether credit default swaps (CDS) serve as an early warning indicator for currency crises. This is done by examining both stock and flow variables, including the external debt stock and reserves (comprising foreign currency and gold), within the context of free exchange rate regimes. An original aspect of the study, which differs from other studies, is the machine learning methods used and the inclusion into the model of both one lag and lag values of the CDs variable, which is an inclusive crisis indicator. The CDS variable was not detected as a strong signal by the logistic regression model. However, the best-performing XGBoost and GB algorithms show the differenced, and one-lagged values of the CDS variable produce significant signals in forecasting currency crises. Consistent with theoretical underpinnings of study on currency crises, this implies that central banks proactively reacted by increasing monetary policy interest rates and the non-current value CDS but its lagged value performed strong early warning signal that is a follower or supplementary indicator of the credibility of monetary authorities and policies. These results demonstrate that the high and rising interest rate signifies that domestic currencies are being supported against speculative attacks.

Keywords: currency crisis; currency pressure index; credit default swap; CDS; panel logistic regression; machine learning classification.

DOI: 10.1504/IJADS.2024.139412

International Journal of Applied Decision Sciences, 2024 Vol.17 No.4, pp.466 - 496

Received: 20 Dec 2022
Accepted: 05 Jan 2023
Published online: 02 Jul 2024 *

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Title: Currency crisis early warning signal mechanisms based on dynamic machine learning

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