Early Warning System for Financial Crisis: Statistical Classification Approach

Young Min Kim, Kyong Joo Oh, Tae Yoon Kim

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter discusses a statistical classification approach for developing a financial crisis early warning system (EWS). The main aim of this chapter is to introduce a lag-. l forecasting classifier constructed in accordance with the sequential works by Son et al. (2009), Ahn et al. (2011), and Ahn et al. (2012) and to discuss how to implement this classifier in practice. Our EWS issues a warning by classifying and forecasting the future conditions of a market. The core of the procedure is defining the oracle rule that determines future market conditions, tracing the oracle rule (the equivalent of lag-. l forecasting) with a trained machine learning algorithm, and then linking lag-. l forecasters across various ls. Our methodology is based on self-fulfilling financial crisis or crisis contagion theories for emerging markets.

Original languageEnglish
Title of host publicationEmerging Markets and the Global Economy
Subtitle of host publicationA Handbook
PublisherElsevier Inc.
Pages347-369
Number of pages23
ISBN (Electronic)9780124115637
ISBN (Print)9780124115491
DOIs
Publication statusPublished - 2014 Jan 1

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)

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    Kim, Y. M., Oh, K. J., & Kim, T. Y. (2014). Early Warning System for Financial Crisis: Statistical Classification Approach. In Emerging Markets and the Global Economy: A Handbook (pp. 347-369). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-411549-1.00015-6