Early Warning Systems for Currency Crises with Real-Time Data

Boonman, Tjeerd M.; Jacobs, Jan; Kuper, Gerard H.; Romero, Alberto

doi:10.1007/s11079-019-09530-0

Cited by 16 publications

(14 citation statements)

References 37 publications

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“…The variable Fixed Capital Formation was significant in all models, showing the significance of the change in the net investment of a country for the risk of a currency crisis. This result contradicts the previous experience in Reference [40], for which this variable was not significant. Regarding the domestic macroeconomic variables, those for monetary supply (M2 Multiplier Growth and M2/Reserves) were also highly significant, showing that a surge in money supply was detrimental to the currency price (also corroborated by the sensitivity of the variable REER Overall in the majority of models).…”

Section: Descriptive Statisticscontrasting

confidence: 99%

Currency Crises Prediction Using Deep Neural Decision Trees

et al. 2019

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Featured Application: The superiority of a novel computational technique (deep neural decision trees) for prediction of currency crises over other methodologies and the construction of new crisis prediction models more precise than existing ones.Abstract: Currency crises are major events in the international monetary system. They affect the monetary policy of countries and are associated with risks of vulnerability for open economies. Much research has been carried out on the behavior of these events, and models have been developed to predict falls in the value of currencies. However, the limitations of existing models mean further research is required in this area, since the models are still of limited accuracy and have only been developed for emerging countries. This article presents an innovative global model for predicting currency crises. The analysis is geographically differentiated for regions, considering both emerging and developed countries and can accurately estimate future scenarios for currency crises at the global level. It uses a sample of 162 countries making it possible to account for the regional heterogeneity of the warning indicators. The method used was deep neural decision trees (DNDTs), a technique based on decision trees implemented by deep learning neural networks, which was compared with other methodologies widely applied in prediction. Our model has significant potential for the adaptation of macroeconomic policy to the risks derived from falls in the value of currencies, providing tools that help ensure financial stability at the global level. Appl. Sci. 2019, 9, 5227 2 of 18 effect of contagion on other countries. However, the event was about more than just speculation on the Thai currency and saw the collapse of Asian stock markets. The financial crisis that began with the devaluation of the Thai baht exchange rate resulted in a sharp increase in interest rates and the collapse of many companies, as well as an increase in the cost of credit and a general fall in GDP in the region [1]. This resulted in foreign and national investors pulling out investment. Not only did this crisis affect Asian countries, but it also had a negative impact on other emerging economies, especially in Latin America, showing that currency crises are not limited to a specific economy. Globalization can increase the economic difficulties of societies and affect the structure of national economies after the real economy has suffered a damaging impact [2]. Figure 1 shows the number of currency crises per year at the international level. Appl. Sci. 2019, 8, x FOR PEER REVIEW 2 of 19of contagion on other countries. However, the event was about more than just speculation on the Thai currency and saw the collapse of Asian stock markets. The financial crisis that began with the devaluation of the Thai baht exchange rate resulted in a sharp increase in interest rates and the collapse of many companies, as well as an increase in the cost of credit and a general fall in GDP in the region [1]. This resulted in foreig...

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Section: Descriptive Statisticscontrasting

confidence: 99%

Currency Crises Prediction Using Deep Neural Decision Trees

et al. 2019

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“…On the other side, the INF variable is not significant, in contrast to works like those of [6,35]. Another variable such as REER has not been refuted as a significant factor either, unlike that shown in the works of [38,45]. Likewise, variables of the foreign sector attribute such as TRO and CACC are shown as more significant variables due to the importance of a country's international trade performance on price, something that refutes the results of the previous works of [2,36].…”

Section: Discussion Of Resultsmentioning

confidence: 87%

“…Following the level of precision of the models, most of the previous studies reach a precision range of 67-85% [2,38,39,46,50,51]. With a higher precision range level (90-97%) we find the investigations of [6,12,[33][34][35][36][37]40,41,48].…”

Section: Currency Crises Predictionmentioning

confidence: 96%

“…The literature that has previously covered the prediction of currency crises has been mostly for emerging economies; therefore, the evidence is poor for advanced economies. Among the research from emerging countries, we highlight those of [2,12,[33][34][35][36][37][38]. Other authors have focused on Asian countries for currency crisis prediction [39][40][41][42].…”

Section: Currency Crises Predictionmentioning

confidence: 99%

“…Regarding the methods used, a considerable number of researchers have applied statistical methods to predict the currency crisis, highlighting Logit [2,12,37,38,43,45], and Probit [44,46,47]. Also, previous studies have developed computational techniques such as RNA [6,33,40,41], auto-organization map [34], support vector machine (SVM) [35], and deep neural decision trees [48].…”

Section: Currency Crises Predictionmentioning

confidence: 99%

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Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques

et al. 2021

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Sovereign debt and currencies play an increasingly influential role in the development of any country, given the need to obtain financing and establish international relations. A recurring theme in the literature on financial crises has been the prediction of sovereign debt and currency crises due to their extreme importance in international economic activity. Nevertheless, the limitations of the existing models are related to accuracy and the literature calls for more investigation on the subject and lacks geographic diversity in the samples used. This article presents new models for the prediction of sovereign debt and currency crises, using various computational techniques, which increase their precision. Also, these models present experiences with a wide global sample of the main geographical world zones, such as Africa and the Middle East, Latin America, Asia, Europe, and globally. Our models demonstrate the superiority of computational techniques concerning statistics in terms of the level of precision, which are the best methods for the sovereign debt crisis: fuzzy decision trees, AdaBoost, extreme gradient boosting, and deep learning neural decision trees, and for forecasting the currency crisis: deep learning neural decision trees, extreme gradient boosting, random forests, and deep belief network. Our research has a large and potentially significant impact on the macroeconomic policy adequacy of the countries against the risks arising from financial crises and provides instruments that make it possible to improve the balance in the finance of the countries.

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Dating currency crises and designing early warning systems: Meta‐possibilistic fuzzy index functions

Tak

Gök

2020

Int. J Fin Econ

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In order to analyse the currency crises in Turkey over the period of January 1990 and October 2019, we first dated currency crises with meta‐possibilistic fuzzy index functions. Then, we determined the significant predictors or leading indicators of currency crisis with logistic regression. Finally, we tried to measure and compare the in‐sample and out‐of‐sample performances of our method with an index generated by principal component analysis (PCA). We found that the models using the currency crisis index generated by our method have higher in‐sample and out‐of‐sample performances than the models using the currency crisis index generated by PCA. We concluded that the change in real exchange rate, bank loans over bank deposits, bank reserves over bank assets, growth in foreign reserves, growth in central bank foreign assets, M2 over foreign assets, change in exports, change in imports, industrial production index, M1 growth, M2 growth, foreign reserves over M1, central bank foreign assets over M1 and public sector credit growth are the leading indicators of currency crisis among 20 explanatory variables. As policy implications, we recommend that government and the monetary authority should strictly monitor the ratio of bank loans over bank deposits, public sector credit growth, the volatility of foreign trade, ratio of foreign reserves and central bank foreign assets over money supply among significant explanatory variables to avoid currency crisis as policy implications.

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Early Warning Systems for Currency Crises with Real-Time Data

Cited by 16 publications

References 37 publications

Currency Crises Prediction Using Deep Neural Decision Trees

Currency Crises Prediction Using Deep Neural Decision Trees

Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques

Dating currency crises and designing early warning systems: Meta‐possibilistic fuzzy index functions

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