SVM sensitivity analysis: an application to currency crises aftermaths

• Published in: IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 34; no. 3; pp. 387 - 398
• Main Authors: Rueda, I.E.A., Arciniegas, F.A., Embrechts, M.J.
• Format: Journal Article
• Language: English
• Published: IEEE 01.05.2004
• Subjects: Banking; Economics; Exchange rates; Input variables; International trade; Machine learning; Machine learning algorithms; Random variables; Sensitivity analysis; Support vector machines
• ISSN: 1083-4427; 1558-2426
• DOI: 10.1109/TSMCA.2004.824850

A currency crisis is an economic event where a country's fixed exchange rate is under pressure by speculators. In some cases, currency crises are followed by strong recessions (e.g., recent Asian and Argentinean crises), but in other cases they are not. This paper seeks to determine what are the most significant factors in explaining the consequences of currency crises on the economy. This paper collects data on 25 variables for 64 currency crises between 1970 and 1999. This research uses a novel algorithm with support vector machines (SVM) for selecting significant variables. This algorithm works well with datasets characterized by nonlinearity and low variable-observation ratio. Variables of banking size and fragility, international trade, and devaluation were the most significant. Variables of banking supervision, economic development, and IMF intervention were found less significant. The variable selection results of the algorithm were compared with all-best subsets variable selection. The results of our algorithm are more consistent with the economic literature than the results from all-best subsets.