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Hidden Markov Models: Applications to Financial Economics (Advanced Studies in Theoretical and Applied Econometrics, 40, Band 40) - Hardcover

Buch 6 von 54: Advanced Studies in Theoretical and Applied Econometrics

Bhar, Ramaprasad; Hamori, Shigeyuki

 
9781402078996: Hidden Markov Models: Applications to Financial Economics (Advanced Studies in Theoretical and Applied Econometrics, 40, Band 40)

Inhaltsangabe

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.

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Über die Autorin bzw. den Autor

Dr. Shigeyuki Hamori is a Professor of Economics at Kobe University in Japan. He received a Ph.D. from Duke University. He is the President of the International Research Institute for Economics and Management, the Distinguished Fellow of the International Engineering and Technology Institute (DFIETI), and the Distinguished Fellow of the Institute of Data Science and Artificial Intelligence (DFIDSAI). His main research interests are applied time series analysis, empirical finance, data science, and international finance. He is the Co-Editor of the Singapore Economic Review, the Associate Editor of the International Review of Financial Analysis, and the Associated Editor of the Eurasian Economic Review. He served as the Editor of special issues of various journals such as  Frontiers in Environmental Science, Energies, Emerging Market Finance and Trade, and Journal of Risk and Financial Management. He has published about 250 articles in international peer-reviewed journals and 20books from Springer, Routledge, World Scientific, etc. Dr. Tadahiro Nakajima is a senior researcher at the Kansai Electric Power Company, Incorporated. He received a Ph.D. from Kobe University. He revceived the Highly Commended Paper of the Studies in Economic and Finance - Literati Award  (Emeral Publishing).  His main research interests are applied time series analysis, energy economics and energy markets. He has published about 20 articles in international peer-reviewes journals and a book from Springer.

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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.

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