A01=Ruey S. Tsay
Author_Ruey S. Tsay
Bayesian inference in finance methods
Category=KCH
Category=KFF
Category=PBT
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
financial econometric models
financial econometrics
financial time series
return series of multiple assets
univariate financial time series
Product details
- ISBN 9780470414354
- Weight: 1111g
- Dimensions: 158 x 236mm
- Publication Date: 10 Sep 2010
- Publisher: John Wiley & Sons Inc
- Publication City/Country: US
- Product Form: Hardback
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This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.
The author begins with basic characteristics of financial time series data before covering three main topics:
- Analysis and application of univariate financial time series
- The return series of multiple assets
- Bayesian inference in finance methods
Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets.
The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.
RUEY S. TSAY, PhD, is H. G. B. Alexander Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. Dr. Tsay has written over 100 published articles in the areas of business and economic forecasting, data analysis, risk management, and process control, and he is the coauthor of A Course in Time Series Analysis (Wiley). Dr. Tsay is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, the Royal Statistical Society, and Academia Sinica.
