Non-Linear Time Series Models in Empirical Finance
Although 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.
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.
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