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Hidden Markov Models : Applications to Financial Economics - Ramaprasad Bhar

Hidden Markov Models

Applications to Financial Economics

Hardcover

Published: 20th July 2004
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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.

Dedicationp. v
Acknowledgmentsp. xi
List of Figuresp. xiii
List of Tablesp. xvii
Introductionp. 1
Introductionp. 1
Markov Chainsp. 1
Passage Timep. 5
Markov Chains and the Term Structure of Interest Ratesp. 6
State Space Methods and Kalman Filterp. 11
Hidden Markov Models and Hidden Markov Expertsp. 13
HMM Estimation Algorithmp. 16
HMM Parameter Estimationp. 18
HMM Most Probable State Sequence: Viterbi Algorithmp. 22
HMM Illustrative Examplesp. 24
Volatility in Growth Rate of Real GDPp. 29
Introductionp. 29
Modelsp. 31
GARCH Modelp. 31
Markov Switching Variance Modelp. 32
Datap. 33
Empirical Resultsp. 33
Conclusionp. 38
Linkages Among G7 Stock Marketsp. 41
Introductionp. 41
Empirical Techniquep. 44
Markov Switching Stock Return Modelp. 44
Concordance Measurep. 45
Datap. 46
Empirical Resultsp. 46
Conclusionp. 51
Interplay Between Industrial Production and Stock Marketp. 55
Introductionp. 55
Markov Switching Heteroscedasticity Model of Output and Equityp. 58
Datap. 62
Empirical Resultsp. 63
Conclusionp. 76
Linking Inflation and Inflation Uncertaintyp. 81
Introductionp. 81
Inflation and Inflation Uncertaintyp. 81
Inflation Uncertainty and Markov Switching Modelp. 83
Empirical Techniquep. 85
Markov Switching Heteroscedasticity Model of the Inflation Ratep. 85
Non-Nested Model Selection using Vuong Statisticp. 86
Datap. 87
Empirical Resultsp. 91
Conclusionp. 107
Exploring Permanent and Transitory Components of Stock Returnp. 117
Introductionp. 117
Markov Switching Heteroscedasticity Model of Stock Returnp. 119
Datap. 120
Empirical Resultsp. 121
Conclusionp. 125
Exploring the Relationship Between Coincident Financial Market Indicatorsp. 127
Introductionp. 127
Markov Switching Coincidence Index Modelp. 129
Datap. 131
Empirical Resultsp. 131
Conclusionp. 139
Referencesp. 145
Indexp. 153
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ISBN: 9781402078996
ISBN-10: 1402078994
Series: Advanced Studies in Theoretical and Applied Econometrics
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 162
Published: 20th July 2004
Publisher: Springer-Verlag New York Inc.
Country of Publication: US
Dimensions (cm): 23.4 x 15.6  x 0.64
Weight (kg): 0.97