Ramaprasad Bhar

Hidden Markov Models: Applications to Financial Economics - Advanced Studies in Theoretical and Applied Econometrics Ramaprasad Bhar Softcover reprint of the original 1st ed. 2004 edition

Marc Notes: Includes bibliographical references and index.; Markov chains have increasingly become a 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, its effectiveness has now been recognised in areas of social science research as well. Table of Contents: List of Figures. List of Tables. Dedication. Acknowledgements. 1: Introduction. 1. Introduction. 2. Markov Chains. 3. Passage Time. 4. Markov Chains and the Term Structure of Interest Rates. 5. State Space Methods and Kalman Filter. 6. Hidden Markov Models and Hidden Markov Experts. 7. HMM Estimation Algorithm. 8. HMM Parameter Estimation. 9. HMM Most Probable State Sequence: Viterbi Algorithm. 10. HMM Illustrative examples. 2: Volatility in Growth Rate of Real GDP. 1. Introduction. 2. Models. 3. Data. 4. Empirical Results. 5. Conclusion. 3: Linkages among G7 Stock Markets. 1. Introduction. 2. Empirical Technique. 3. Data. 4. Empirical Results. 5. Conclusion. 4: Interplay between Industrial Production and Stock Market. 1. Introduction. 2. Markov Switching Heteroscedasticity Model of Output and Equity. 3. Data. 4. Empirical Results. 5. Conclusion. 5: Linking Inflation and Inflation Uncertainty. 1. Introduction. 2. Empirical Technique. 3. Data. 4. Empirical Results. 5. Conclusion. 6: Exploring Permanent and Transitory Components of Stock Return. 1. Introduction. 2. Markov Switching Heteroscedasticity Model of Stock Return. 3. Data. 4. Empirical Results. 5. Conclusion. 7: Exploring the Relationship between Coincident Financial Market Indicators. 1. Introduction. 2. Markov Switching Coincidence Index Model. 3. Data. 4. Empirical Results. 5. Conclusion. References. Index. Publisher Marketing: Markov chains have increasingly become a 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, its effectiveness has now been recognised in areas of social science research as well.

Contributor Bio:  Bhar, Ramaprasad Bhar is an Associate Professor in the School of Banking and Finance at The University of New South Wales in Australia. Contributor Bio:  Hamori, Shigeyuki Hamori is a Professor in the Graduate School of Economics at Kobe University in Japan.