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  3. Non-Linear Time Series Models in Empirical Finance
  • Cited by 457
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    • Publisher:
      Cambridge University Press
      Publication date:
      May 2010
      July 2000
      ISBN:
      9780511754067
      9780521770415
      9780521779654
      DOI:
      https://doi.org/10.1017/CBO9780511754067
      Dimensions:
      (247 x 174 mm)
      Weight & Pages:
      0.74kg, 298 Pages
      Dimensions:
      (247 x 174 mm)
      Weight & Pages:
      0.59kg, 298 Pages
      • Subjects:
      Economics, Econometrics and Mathematical Methods, Statistics for Econometrics, Finance and Insurance, Statistics and Probability
    You may already have access via personal or institutional login
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    Subjects:
    Economics, Econometrics and Mathematical Methods, Statistics for Econometrics, Finance and Insurance, Statistics and Probability
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    Book description

    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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