Financial Econometrics: From Basics to Advanced Modeling Techniques
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Editorial ReviewsProduct Description
A comprehensive guide to financial econometrics Financial econometrics is a quest for models that describe financial time series such as prices, returns, interest rates, and exchange rates. In Financial Econometrics, readers will be introduced to this growing discipline and the concepts and theories associated with it, including background material on probability theory and statistics. The experienced author team uses real-world data where possible and brings in the results of published research provided by investment banking firms and journals. Financial Econometrics clearly explains the techniques presented and provides illustrative examples for the topics discussed. Svetlozar T. Rachev, PhD (Karlsruhe, Germany) is currently Chair-Professor at the University of Karlsruhe. Stefan Mittnik, PhD (Munich, Germany) is Professor of Financial Econometrics at the University of Munich. Frank J. Fabozzi, PhD, CFA, CFP (New Hope, PA) is an adjunct professor of Finance at Yale University’s School of Management. Sergio M. Focardi (Paris, France) is a founding partner of the Paris-based consulting firm The Intertek Group. Teo Jasic, PhD, (Frankfurt, Germany) is a senior manager with a leading international management consultancy firm in Frankfurt.
From the Back Cover
Financial econometrics combines mathematical and statistical theory and techniques to understand and solve problems in financial economics. Modeling and forecasting financial time series, such as prices, returns, interest rates, financial ratios, and defaults, are important parts of this field. In Financial Econometrics, you'll be introduced to this growing discipline and the concepts associated with it—from background material on probability theory and statistics to information regarding the properties of specific models and their estimation procedures. With this book as your guide, you'll become familiar with:
- Autoregressive conditional heteroskedasticity (ARCH) and GARCH modeling
- Principal components analysis (PCA) and factor analysis
- Stable processes and ARMA and GARCH models with fat-tailed errors
- Robust estimation methods
- Vector autoregressive and cointegrated processes, including advanced estimation methods for cointegrated systems
- And much more
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