Non-Linear Time Series Models in Empirical Finance PDF Download Ebook. Philip Hans Franses and Dick van Dijk provide in-depth treatment of recently developed non-linear models, including regime-switching and artificial neural networks. This book applies them to describing and forecasting financial asset returns and volatility by using wide range of financial data, drawn from sources including the markets of Tokyo, London and Frankfurt. Through an extensive forecasting experiment (for a wide range of daily data on stock markets and exchange rates), we also demonstrate that linear time series models do not yield reliable forecasts.
Of course, this does not automatically imply that nonlinear time series models would, but, as we argue in this book, it can be worth a try. As there is a host of possible nonlinear time series models, we decide to review in Chapters 3, 4 and 5, the, what we believe, currently most relevant ones and the ones that are most likely to persist as practical descriptive and forecasting devices.
In Chapter 3, we discuss several regime-switching models such as the self-exciting threshold model, the smooth transition model and the Markov switching model. In this chapter we confine the analysis to the returns on financial assets, although they can also be considered for measures of risk (or volatility) like squared or absolute returns. We consider tools for specifying, estimating, evaluating and forecasting with these models. Illustrations for several empirical series show that these models could be quite useful in practice.
In Chapter 4, we consider similar kinds of regime-switching models for unobserved volatility, which in fact amount to various extensions of the basic GARCH model. This well-known and often applied model exploits the empirical regularity that aberrant observations in financial time series appear in clusters (thereby indicating periods of high volatility), and hence that out-of-sample forecasts for volatility can be generated.
The models in Chapter 4 mainly challenge the assumption in the basic GARCH model that the model parameters are constant over time and/or that positive and negative news have the same impact on subsequent volatility. Indeed, the empirical analysis in this chapter shows that a relaxation of these assumptions seems worthwhile to consider. Again, we discuss tools for specification, estimation and evaluation, and we outline how out-of-sample forecasts can be generated and evaluated.
Finally, in Chapter 5, we deal with a currently fashionable class of models, that is, with artificial neural networks. In contrast to the prevalent strategy in the empirical finance literature (which may lead people to believe that these models are merely a passing fad), we decide to `open up the black box', so to say, and to explicitly demonstrate how and why these models can be useful in practice. Indeed, the empirical applications in this chapter suggest that neural networks can be quite useful for out-of-sample forecasting and for recognizing a variety of patterns in the data.
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