Non-Linear Time Series Models in Empirical Finance
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Non-Linear Time Series Models in Empirical FinanceAlthough many of the models commonly used in empirical finance are linear, the nature of financial data suggests that non-linear models are more appropriate for forecasting and accurately describing returns and volatility. The enormous number of non-linear time series models appropriate for modeling and forecasting economic time series models makes choosing the best model for a particular application daunting. This classroom-tested advanced undergraduate and graduate textbook - the most up to-date and accessible guide available - provides a rigorous treatment of recently developed non-linear models, including regime-switching and artificial neural networks. The focus is on the potential applicability for describing and forecasting financial asset returns and their associated volatility. The models are analysed in detail and are not treated as 'black boxes'. Illustrated using a wide range of financial data, drawn from sources including the financial markets of Tokyo, London and Frankfurt.
Table of contents:
1. Introduction; 2. Some concepts in Time Series analysis; 3. Regime-switching models for returns; 4. Regime-Switching models for Volatility; 5. Artificial neural networks for returns; 6. Conclusion.
The most up-to-date and accessible guide to one of the fastest growing areas in financial analysis by one of Europes's leading teaching and researching teams. This classroom-tested advanced undergraduate and graduate textbook provides an in-depth treatment of non-linear models, including regime-switching and artificial neural networks.
Reviews recently developed non-linear time series models, and their applications to financial markets.
- Verlag: CAMBRIDGE UNIV PR
- Ausstattung/Bilder: 296 pages - 51 tables 44 figures
- Seitenzahl: 298
- Abmessung: 245mm x 174mm x 19mm
- Gewicht: 590g
- ISBN-13: 9780521779654
- ISBN-10: 0521779650
- Best.Nr.: 21400652
2. Some concepts in time series analysis
3. Regime-switching models for returns
4. Regime-switching models for volatility
5. Artificial neural networks for returns