書籍詳細

書籍詳細




洋書

時系列分析の金融への応用

Essentials of Time Series for Financial Applications

Guidolin, Massimo   Pedio, Manuela

Academic Pr 2018/05
417 p. 27 cm   
装丁: Pap   
版表示など: pap.    装丁について
テキストの言語: ENG    出版国: US
ISBN: 9780128134092
KCN: 1029586876
紀伊國屋書店 選定タイトル
標準価格:¥14,732(本体 ¥13,393)   
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納期について
DDC: 330
KDC: E210 金融理論
F181 金融数理・金融工学
E112 数理・計量経済
関連書リスト: NB4357
NB4460
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Full Description

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.

Table of Contents

1. Review of Key Concepts and Methods in Econometrics: Regressions Analysis 2. Autoregressive-Moving Average (ARMA) Models and their Practical Applications. 3. Vector Autoregressive Moving Average (VARMA) Models 4. Unit Roots and Cointegration Methods 5. Univariate Single-Factor Stochastic Volatility Models: Autoregressive Conditional Heteroskedasticity(ARCH and GARCH) 6. Multivariate ARCH and GARCH and Dynamic Conditional Correlation Models 7. Multi-Factor Volatility Models: Stochastic Volatility 8. Models with Breaks, Recurrent Regime Switching, and Non-Linearities 9. Markov Switching Models 10. Realized Volatility and Covariance