Hidden Markov Models

1. Front Matter

   Pages i-xviii

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2. Introduction

   Pages 1-27

3. Volatility in Growth Rate of Real GDP

   Pages 29-39

4. Linkages Among G7 Stock Markets

   Pages 41-53

5. Interplay between Industrial Production and Stock Market

   Pages 55-79

6. Linking Inflation and Inflation Uncertainty

   Pages 81-115

7. Exploring Permanent and Transitory Components of Stock Return

   Pages 117-126

8. Exploring the Relationship between Coincident Financial Market Indicators

   Pages 127-144

9. Back Matter

   Pages 145-158

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Markov chains have increasingly become useful way of capturing stochastic nature of many economic and financial variables. Although the hidden Markov processes have been widely employed for some time in many engineering applications e.g. speech recognition, its effectiveness has now been recognized in areas of social science research as well. The main aim of Hidden Markov Models: Applications to Financial Economics is to make such techniques available to more researchers in financial economics. As such we only cover the necessary theoretical aspects in each chapter while focusing on real life applications using contemporary data mainly from OECD group of countries. The underlying assumption here is that the researchers in financial economics would be familiar with such application although empirical techniques would be more traditional econometrics. Keeping the application level in a more familiar level, we focus on the methodology based on hidden Markov processes. This will, we believe, help the reader to develop more in-depth understanding of the modeling issues thereby benefiting their future research.

• Inflation
• Markov Chain
• Markov Chains
• econometrics
• linear optimization
• modeling
• paraplupub
• production

• School of Banking and Finance, The University of New South Wales, Sydney, Australia

  Ramaprasad Bhar

• Graduate School of Economics, Kobe University, Japan

  Shigeyuki Hamori

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