Financial Crisis Modeling and Prediction with a Hilbert-EMD-Based SVM Approachs

Financial Crisis Modeling and Prediction with a Hilbert-EMD-Based SVM Approachs

Lean Yu (City University of Hong Kong, Hong Kong), Shouyang Wang (Chinese Academy of Sciences, China) and Kin Keung Lai (Chinese Academy of Sciences, China)
DOI: 10.4018/978-1-59904-982-3.ch017
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Abstract

Financial crisis is a kind of typical rare event, but it is harmful to economic sustainable development if occurs. In this chapter, a Hilbert-EMD-based intelligent learning approach is proposed to predict financial crisis events for early-warning purpose. In this approach a typical financial indicator currency exchange rate reflecting economic fluctuation condition is first chosen. Then the Hilbert-EMD algorithm is applied to the economic indicator series. With the aid of the Hilbert-EMD procedure, some intrinsic mode components (IMCs) of the data series with different scales can be obtained. Using these IMCs, a support vector machine (SVM) classification paradigm is used to predict the future financial crisis events based upon some historical data. For illustration purposes, two typical Asian countries including South Korea and Thailand suffered from the 1997-1998 disastrous financial crisis experience are selected to verify the effectiveness of the proposed Hilbert-EMD-based SVM methodology.
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Introduction

In the past decades, some important economic and financial events like the Black October 1987, the December 1994 devaluation of the Mexican peso, and the October 1997 Asia financial turmoil after the devaluation of the Thai baht justify the considerable concerns with financial and currency crisis in the financial literature. These unprecedented and peculiar crisis events brought large change not only to their economy but also to their society, and since then much attention has been focused on study of the financial crisis from the theoretical and empirical standpoints (Flood & Marion, 1999; Goldstein, 1996; Kaminsky, Lizondo, & Reinhart, 1998; Sachs, Tornell, & Velasco, 1996). The main reason for much research attention is that financial market practitioners have different reasons for undertaking financial crises analysis. For example, macro policymakers are interested in leading indicators of pressure on economic growth; market participants are increasingly concerned to measure and limit their risk to large currency fluctuation; and financial regulators are keen to understand the currency risk exposure of the institutions they supervise (Yu, Lai, & Wang, 2006).

In order to predict whether one country will reach financial crisis level, it is important to be clear exactly how a crisis is defined. Much relevant literature looks at crisis indices (termed currency pressure indicators) defined as weighted sums of percentage change in exchange rates, interest rates and foreign currency reserves (Kumar, Moorthy, & Perraudin, 2003). Use of such indices is appropriate if one views crises from the view of a macro policymaker and is equally interested in “successful” and “unsuccessful” speculative attacks. From the standpoint of an investor, manager of foreign reserve positions, or a macro policymaker who cares primarily about a “successful attack” on the currency, it is more appropriate to consider a simpler definition of financial crisis based on large depreciations of currency exchange rates. To distinguish our research from those of studies which employ currency pressure indicators, a large devaluation of currency which far exceeds previous devaluations is defined as a financial crisis event.

In the existing studies, much effort has been made to build an appropriate model that could detect a possible crisis in advance. Accordingly various financial crisis forecasting models and early warning systems (e.g., Edison, 2000; Frankel & Rose, 1996; Goldstein, Kaminsky, & Reinhart, 2000; Kim, Hwang, & Lee, 2004; Kim, Oh, Sohn, & Hwang, 2004; Yu et al., 2006) have been constructed. In general, there are four types of financial crisis modeling and analysis model. The first type is to use structural models to analyze the financial crisis. There are case studies into specific financial episodes, often employing explicit structural models of balance of payments crises. Notable examples include Blanco and Garber (1986), Cumby and van Wijnbergen (1989), Jeanne and Masson (2000), Cole and Kehoe (1996), and Sachs et al. (1996). These studies are informative about the episodes in question and revealing with regard to structural model proposed by some theorists (Kumar et al., 2003).

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Table of Contents
Preface
Hsiao-Fan Wang
Acknowledgment
Hsiao-Fan Wang
Chapter 1
Martin Spott, Detlef Nauck
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Chapter 2
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Chapter 3
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Chapter 4
Erica Craig, Falk Huettmann
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Chapter 5
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A New Approach to Classification of Imbalanced Classes via Atanassov's Intuitionistic Fuzzy Sets
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Chapter 6
Arun Kulkarni, Sara McCaslin
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Fuzzy Neural Network Models for Knowledge Discovery
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Chapter 7
Ivan Bruha
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Genetic Learning: Initialization and Representation Issues
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Chapter 8
Evolutionary Computing  (pages 131-142)
Thomas E. Potok, Xiaohui Cui, Yu Jiao
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Evolutionary Computing
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Chapter 9
M. C. Bartholomew-Biggs, Z. Ulanowski, S. Zakovic
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Particle Identification Using Light Scattering: A Global Optimization Problem
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Chapter 10
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Chapter 11
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Chapter 12
Malcolm J. Beynon
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Chapter 13
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Chapter 15
Fei-Chen Hsu, Hsiao-Fan Wang
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Chapter 16
Francesco Giordano, Michele La Rocca, Cira Perna
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Neural Networks and Bootstrap Methods for Regression Models with Dependent Errors
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Chapter 17
Lean Yu, Shouyang Wang, Kin Keung Lai
Financial crisis is a kind of typical rare event, but it is harmful to economic sustainable development if occurs. In this chapter, a... Sample PDF
Financial Crisis Modeling and Prediction with a Hilbert-EMD-Based SVM Approachs
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Chapter 18
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