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Essentials of Time Series for Financial Applications
Describes commonly used time series methods for financial applications that are illustrated and implemented in a common programming language
Massimo Guidolin (Author), Manuela Pedio (Author)
9780128134092, Elsevier Science
Paperback, published 31 May 2018
434 pages
27.6 x 21.5 x 2.7 cm, 1.25 kg
"In addition to providing a rigorous treatment of time series models and their estimation, every definition and statistical method is accompanied by several fully developed examples illustrating the methodologies at work and their limitations. This makes the book unique and particularly valuable for students as well as practitioners." --zbMATH "This is a marvel of a book that seamlessly blends rigorous treatment of classical and state-of-the-art topics from time-series analysis with insightful empirical examples from financial markets. The book is full of practical advice to novel practitioners as well as experts with more extensive experience from analyzing data from financial markets. The book deserves to find widespread use among Master of Finance and PhD students, and among seasoned economists who want to get updated on the most important techniques in empirical finance." --Allan Timmermann, University of California, San Diego "Guidolin and Pedio’s textbook provides a masterful treatment of some of the key methods of financial econometrics. The exposition is remarkably clear and rigorous, and all the main concepts are illustrated by rich practical examples. I highly recommend this book to higher level students and practitioners." --Laurent Calvet, EDHEC Business School "With in-depth treatment of a wide variety of relevant topics, this book is a must-read for academics and practitioners working with quantitative methods in finance." --Dick van Dijk, Erasmus University Rotterdam "Guidolin and Pedio provide a comprehensive overview of time series econometrics starting from the basics and building to more advanced topics including structural breaks, regime switching, range-based and realized covariance estimation. The authors guide the reader from introductory methods to multivariate and nonlinear methods in a succinct and readable manner and, in so doing, provide an excellent resource for both teaching and practice." --Tom McCurdy, University of Toronto
Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs.
1. Review of Key Concepts and Methods in Econometrics: Regressions Analysis2. Autoregressive-Moving Average (ARMA) Models and their Practical Applications.3. Vector Autoregressive Moving Average (VARMA) Models4. Unit Roots and Cointegration Methods5. Univariate Single-Factor Stochastic Volatility Models: Autoregressive Conditional Heteroskedasticity(ARCH and GARCH)6. Multivariate ARCH and GARCH and Dynamic Conditional Correlation Models7. Multi-Factor Volatility Models: Stochastic Volatility8. Models with Breaks, Recurrent Regime Switching, and Non-Linearities9. Markov Switching Models10. Realized Volatility and Covariance
Subject Areas: Business mathematics & systems [KJQ], Finance [KFF], Economic statistics [KCHS], Econometrics [KCH]
