A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China

This paper combines artificial neural networks (ANN), fuzzy optimization and time-series econometric models in one unified framework to form a hybrid intelligent early warning system (EWS) for predicting economic crises. Using quarterly data on 12 macroeconomic and financial variables for the Chinese economy during 1999 and 2008, the paper finds that the hybrid model possesses strong predictive power and the likelihood of economic crises in China during 2009 and 2010 remains high.Computational intelligence; artificial neural networks; fuzzy optimization; early warning system; economic crises

The importance of ideas: an a priori critical juncture framework

This paper sets out an improved framework for examining critical junctures. This framework, while rigorous and broadly applicable and an advance on the frameworks currently employed, primarily seeks to incorporate an a priori element. Until now the frameworks utilized in examining critical junctures were entirely postdictive. Adding a predictive element to the concept will constitute a significant advance. The new framework, and its predictive element, termed the “differentiating factor,” is tested here in examining macro-economic crises and subsequent changes in macro-economic policy, in America and Sweden

A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China

This paper combines artificial neural networks (ANN), fuzzy optimization and time-series econometric models in one unified framework to form a hybrid intelligent early warning system (EWS) for predicting economic crises. Using quarterly data on 12 macroeconomic and financial variables for the Chinese economy during 1999 and 2008, the paper finds that the hybrid model possesses strong predictive power and the likelihood of economic crises in China during 2009 and 2010 remains high

Identifying Critical Junctures in Macroeconomic Policy - The Cases of Mexico and Sweden in the Early 1980s

Abstract: This paper utilizes a new critical junctures framework to help understand the nature of the changes in macroeconomic policy. The framework consists of three elements which must be identified in sequence to be able to declare, with some certainty, if an event was a critical juncture. These are crisis, ideational change, and radical policy change. Utilizing the critical juncture framework, we will determine whether changes to Mexican and Swedish macroeconomic policy in the early 1980s constituted clean breaks with the past, or were continuations of previously established policy pathways, and why that was

Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques.

This research was funded by Cátedra de Economía y Finanzas Sostenibles, Universidad de Málaga, Spain. Partial funding for open access charge: Universidad de MálagaSovereign 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

Modelos de predicción de crisis financieras internacionales con técnicas de aprendizaje automático: aplicaciones a la reputación país

El presente estudio ha escogido tres de los tipos más importantes de crisis tratadas en las finanzas internacionales: la crisis de deuda soberana, la crisis de divisas y la crisis sistémica bancaria. Por tanto, se trata de responder a la cuestión de investigación de si es posible mejorar la precisión de los modelos globales de predicción de crisis existentes en la literatura previa. Para responder a esta cuestión se ha tenido en cuenta no solo técnicas estadísticas, sino también técnicas computacionales que han arrojado excelentes resultados de clasificación en las últimas décadas en cuestiones de predicción económica. Para ofrecer una mayor diversidad explicativa y comparativa, se han utilizado tanto modelos globales como modelos regionales para África y Oriente Próximo, Asia, América Latina y Europa. Los resultados obtenidos han permitido constatar una mayor precisión de los métodos computacionales frente a las técnicas estadísticas tradicionales. Incluso técnicas computacionales muy novedosas han mostrado un potencial interesante en la precisión de estos eventos de crisis. Estos modelos de predicción de crisis de deuda soberana, divisas y sistema bancario pueden ser de utilidad para valorar de una manera más precisa la reputación de un país frente al mundo. La reputación país explica cómo las características más importantes de un país, por ejemplo, factores sociales y económicos, influye en la imagen o marca en la que el país se proyecta al mundo. El concepto de reputación país es análogo al de reputación corporativa, y de hecho, algunos autores han demostrado la influencia que tiene la reputación país en las empresas y en la forma en la que comercializan sus productos. Por este motivo, la presente tesis estudia el factor de estabilidad financiera en la reputación país a través de los modelos de predicción de crisis de deuda soberana, divisas y sistema bancario

Empirical Analysis of Natural Gas Markets

Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shift away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability make liquefied natural gas (LNG) a popular alternative, during this time of transition between traditional fuels and newer options. In the United States, the shale gas revolution has made natural gas a game changer. In this book, we focus on empirical analyses of the natural gas market and its growing relevance worldwide

CURRENCY CRISIS FORECASTING WITH GENERAL REGRESSION NEURAL NETWORKS

The main purpose of this study is to devise a general regression neural network (GRNN)-based currency crisis forecasting model for Southeast Asian economies based upon the disastrous 1997–1998 currency crisis experience. For this some typical indicators of currency exchange rates volatility are first chosen, then these indicators are input into GRNN for training, and finally the trained GRNN is used for future crisis prediction. To verify the effectiveness of the proposed currency crisis forecasting approach, four typical Southeast Asian currencies, Indonesian rupiah, Philippine peso, Singapore dollar and Thai baht, are selected. Meantime we compare its performance with those of other forecasting methods to evaluate the forecasting ability of the proposed approach. Empirical results obtained reveal that the proposed currency crisis forecasting model has a surprisingly high degree of accuracy in judging the currency crisis level of each country in specified time period, implying that our proposed approach can be used as a feasible currency crisis early-warning system to predict currency crisis level for other countries around the world.Currency crisis forecasting, general regression neural network (GRNN), exchange rate volatility, currency crisis early-warning system