Abstract
Given that the evolution of the Emerging Markets Bond Index (EMBI) can lead to a great impact of investor’s decision- making, the forecasted values may have on the business plan or the investment portfolio an idea of what the trend of the index will be based on its historical values. This article presents a new method to provide the short- and long-term forecast of EMBI measurements from Latin American countries by using artificial neural networks to model the behavior of the underlying process. Motivated by the risk in the decision-making concept, the algorithm can effectively forecast the time-series data by stochastic analysis of its future behavior using fractional Gaussian noise. Relative advantages and limitations of the algorithm by showing the performance of the roughness of the series (Hurst parameter) in its statistical sense are highlighted.
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Acknowledgments
The authors wish to thank Universidad Nacional de Córdoba (UNC) and Universidad Nacional de Catamarca (UNCa) for their financial support of this work. The authors also would like to thank the reviewers for their thoughtful comments and efforts toward improving the manuscript.
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Pucheta, J., Alasino, G., Salas, C., Herrera, M., Rivero, C.R. (2020). Stochastic Analysis for Short- and Long-Term Forecasting of Latin American Country Risk Indexes. In: Arabnia, H.R., Daimi, K., Stahlbock, R., Soviany, C., Heilig, L., Brüssau, K. (eds) Principles of Data Science. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-43981-1_12
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