Applications of state space models in finance

Chapter 8
Conclusion and outlook
This thesis deals with the modeling of change in the context of widely established concepts
in finance. Advanced time series models are applied to analyze the time-varying relationship
between systematic risks and pan-European industry portfolios. Three major
research objectives are addressed: (i) to provide a notationally conformable introduction
to the econometric toolbox needed for an elaborate modeling of changing relationships
over time, (ii) to compare the different modeling techniques’ ability to characterize and
predict the time-varying nature of systematic beta risk as a stochastic process, and
(iii) to analyze the practical relevance of taking conditionality of factor sensitivities explicitly
into account. This final chapter reviews the main results and points to possible
future directions to continue research connected to the findings presented in this thesis.
A sound theoretical foundation of how advanced time series techniques can be employed
to model changing relationships over time constitutes a prerequisite to carry out
empirical research on the modeling of change. As we can choose from a wide spectrum
of available modeling techniques, the first step in conducting research is to decide on
how a problem should be approached. A general guide toward the selection and specification
of a model is to look at the empirical stylized facts of the series of interest.
Chapter 2 introduces the set of weekly sector return series used throughout this thesis.
A review of the series’ major empirical properties illustrates why the paradigm of stable
sensitivities has to be questioned: the distributions of the return series at hand are
found to be highly leptokurtic, the squared series are significantly autocorrelated, most
series exhibit a leverage effect and volatilities of different assets are linked to each other.
These empirical properties, especially with regard to the observed volatility clustering
and volatility co-movements, imply that the true degree of the linear association between
sector returns and systematic influences is not constant but changing over time. This
can be confirmed for all sectors by a formal testing procedure for structural breaks.
The stylized facts not only motivate the modeling of change, they also serve as a
guide to which modeling techniques might be relied upon. As a single model usually
cannot capture all identified distributional properties simultaneously, different models
are employed to capture different properties of a series. The finding of volatility clustering
points toward the employment of conditional heteroskedasticity modeling, a key
concept in many areas of finance and financial econometrics that can be used to model