Applications of state space models in finance

4 1 Introduction
missing. Chapter 6 aims to close this gap by investigating the time-varying behavior of
systematic risk for eighteen pan-European sectors, also referred to as industry portfolios.
Using weekly data over the period 1987–2005, time-variation in beta is modeled by a
bivariate t-GARCH(1,1) model, a bivariate stochastic volatility model estimated via efficient
Monte Carlo likelihood, four Kalman filter based approaches as well as two Markov
switching models. The main purpose of the chapter is to compare the performance of
these modeling techniques including non-standard procedures such as Markov switching
and stochastic volatility. Overall, the respective ex-ante forecast performances of
the various modeling approaches indicate that the random walk process combined with
the Kalman filter is the most accurate in describing and forecasting the time-varying
behavior of sector betas in a European context.
The analysis of the dynamics in the relationship between financial variables represents
an exciting area of research, motivated by both economic arguments and empirical findings.
However, it is not yet clear to what degree the modeling of change is relevant in
practice. The third contribution of this thesis is an empirical analysis of the practical
relevance of taking time-variation in factor loadings explicitly into account. A multifactor
pricing framework that relates pan-European industry portfolios to systematic
macroeconomic and fundamental risks is considered. Chapter 7 focuses on the question
whether an explicit consideration of the changing importance of macroeconomics and
fundamentals on European sectors can be exploited in a profitable way, either from a risk
management or from a portfolio management perspective. The selection of common factors
is based on the latest findings of the anomalies literature. Covering an out-of-sample
period of ten years, the proposed conditional multifactor pricing model employs a market
factor, three macroeconomic factors and two fundamental factors, which are motivated
by the well-known three-factor model by Fama and French (1993). The stochastic process
of conditional betas is modeled as individual random walks. Estimation of the model
over time using the Kalman filter yields series of estimated conditional factor loadings.
These are used as explanatory variables in a cross-sectional regression approach that
follows Fama and MacBeth (1973). Traditionally, the Fama-MacBeth approach utilizes
constant coefficients as instruments. In order to get an indication of the importance
of conditional betas for the pricing of risk, in this thesis a modified formulation with
time-varying sensitivities as instruments is employed. The question whether employing
conditional factor loadings to derive return forecasts leads to more profitable portfolios
is examined based on a series of backtests. The main results of this chapter indicate
that time-varying betas carry implications for the prediction of returns, and less so for
the analysis of risk.
1.3 Organization of the thesis
This thesis deals with the modeling of change in the context of widely used concepts in
finance. To link the theoretical and practical parts, Chapter 2 introduces the set of sector
return series and reviews their major empirical properties. The stylized facts reveal
that the paradigm of stable sensitivities has to be questioned. This motivates the use
of more advanced time series concepts to analyze the time-varying importance attached