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, first published in
2000, 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.
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