Empirics of currency crises: A duration analysis approach

Karimi, Mohammad Reza; Voia, Marcel-Cristian

doi:10.1002/rfe.1056

Cited by 3 publications

(5 citation statements)

References 104 publications

(148 reference statements)

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“…Similarly, variables for the External Sector attribute, such as Trade Openness, FDI and Current Account, were highly sensitive, indicating the significance of the behavior of a country's international trade on its currency price. This is in contrast to the findings of previous studies [1,15,41]. Similarly, the variables Total Debt and Government Spending (related to the accumulation of debt) were also highly significant, showing that high public debt ratios increase the risk of currency crises.…”

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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“…In a multi-country analysis, Komulainen and Lukkarila (2003) and Karimi and Voia (2011) found that the probability of currency crises increases with high unemployment rates, rise in inflation rates, REER, trade linkages, high public debts and trade openness. Frankel and Rose (1996) examined that the possibility of currency crises is likely to increase when the foreign direct investment to debt is low.…”

Section: Review Of Literaturementioning

confidence: 99%

Common Determinants of the Likelihood of Currency Crises in BRICS

Rao

Padhi

2018

Global Business Review

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We consider a panel of quarterly data for five BRICS countries for the period 1996: Q1 through 2015: Q4 to examine the currency crises. In this article, we construct an exchange market pressure index using Kaminsky, Lizondo and Reinhart (KLR) methodology to identify the crisis periods. Upon identifying the crisis periods, we examine the common determinants of the currency crises in these five counties using a panel probit model with random effects. Among the 22 identified periods across five BRICS countries, Russia has seen a currency stress quite often followed by Brazil, South Africa, China and India. Our main finding is that variables such as base money to broad money, broad money growth, current account balance, exports, imports, inflation, real interest rate and real effective exchange rate (REER) work as leading indicators of the crises and major contributing factors in a country’s vulnerability to crisis. We also find that the prediction of crises is robust at cut-off probability of 50 per cent.

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“…For their part, Reference [43] investigates the differences in a common set of indicators used in early alert systems for currency crises in the situation of Jordan and Egypt. Reference [44] empirically analyzes the causes of currency crises for a set of Organisation for Economic Co-operation and Development (OECD) countries.…”

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%

“…From another point of view, taking into account the explanatory variables of the models, the most prominent among the authors have been exports [2,6,33,34,36,43,44,49], the real exchange rate of the currency [33,36,43,44,49,50], relationship between the reserve and the money supply [2,33,34,43,49], current account balance [12,34,51], and GDP growth [12,49,51]. For their part, Reference [42] found that global financial shocks and the growth rate of domestic credit are the main currency crisis indicators.…”

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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Empirics of currency crises: A duration analysis approach

Cited by 3 publications

References 104 publications

Currency Crises Prediction Using Deep Neural Decision Trees

Currency Crises Prediction Using Deep Neural Decision Trees

Common Determinants of the Likelihood of Currency Crises in BRICS

Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques

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