Applications of state space models in finance

Preface
The work on this book began in 2004, when equity markets had just overcome the
burst of the dotcom bubble and entered a very different market regime, characterized
by high returns and low volatility. The idea to have a closer look at applications of state
space models in finance grew out of my experience as portfolio manager and strategist for
European equities. While conducting research on a quantitative sector allocation model,
it soon became obvious that traditional time series models with constant coefficients are
not able to capture the dynamics of financial markets.
The key to handling models with time-varying sensitivities is the state space representation
of a dynamic system. The interest in using state space models in fields away
from control engineering and speech recognition, where techniques such as the Kalman
filter and Markov regime switching have originated, has increased in recent years. However,
a transportation of these techniques from theoretical work into applied research in
the context of widely used concepts in finance is still underdeveloped. The presented
material will be useful for financial economists and practitioners who are interested
in taking time-variation in the relationship between financial assets and key economic
factors explicitly into account. As a distinctive feature, the empirical part includes a
comprehensive analysis of the ability of time-varying coefficient models to explore the
conditional nature of systematic risks for European industry portfolios.
This book has been accepted as doctoral thesis by the Faculty of Economics and
Business Administration of the Georg-August-Universität Göttingen for award of the
degree Doctor of Economics in 2008. The original title “Applications of Advanced
Time Series Models to Analyze the Time-varying Relationship between Macroeconomics,
Fundamentals and Pan-European Industry Portfolios” was changed upon publication via
the Göttingen University Press.
Sascha Mergner