Applications of state space models in finance

Chapter 2
Some stylized facts of weekly sector return
series
A sound theoretical background of how advanced time series concepts can be applied to
model changing relationships over time constitutes a prerequisite to conduct empirical
research on the modeling of change. As a wide spectrum of modeling techniques is
available, the first step in carrying out research is to decide how a problem should
be approached. In this respect, the selection and specification of a model is generally
driven by empirical stylized facts of the series at hand. This chapter reviews some
of the properties that are inherent to the sector return series to be used throughout
this thesis. Describing the characteristics of the data will emphasize the necessity of
applying appropriate time series techniques that are capable of modeling the series’
empirical distributional and temporal regularities.
In the financial econometrics literature, it is well documented that financial time series
share a number of common features; see, for example, Ghysels et al. (1996) or Pagan
(1996) for an overview. In particular, following Palm (1996), these include the following
issues:
Asset prices are usually nonstationary while returns are usually stationary.
Returns tend to show an erratic behavior with large outlying observations occurring
more frequently than may be expected under the assumption of a normal
distribution.
Returns are usually not autocorrelated, but squared returns are autocorrelated, a
phenomenon known as volatility clustering.
Some return series exhibit the so-called leverage effect, where large negative returns
are followed by periods of high volatility.
Volatilities of different assets often move together indicating potential linkages.
By employing standard statistical and graphical methods, these properties are illustrated
for some selected weekly return series.
This thesis aims to analyze the time-varying importance of market, macroeconomic
and fundamental factors for pan-European industry portfolios. The two major objectives