
Hidden Markov Models
Applications to Financial Economics
Springer (Publisher)
Published on 20. July 2004
Book
Hardback
XVIII, 162 pages
978-1-4020-7899-6 (ISBN)
Description
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.
More details
Series
Edition
2004 ed.
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XVIII, 162 p.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 15 mm
Weight
445 gr
ISBN-13
978-1-4020-7899-6 (9781402078996)
DOI
10.1007/b109046
Schweitzer Classification
Other editions
Additional editions

Book
12/2010
Springer
€106.99
Shipment within 15-20 days

E-Book
04/2006
1st Edition
Springer
€65.95
Available for download
Content
Volatility in Growth Rate of Real GDP.- Linkages Among G7 Stock Markets.- Interplay between Industrial Production and Stock Market.- Linking Inflation and Inflation Uncertainty.- Exploring Permanent and Transitory Components of Stock Return.- Exploring the Relationship between Coincident Financial Market Indicators.