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The Basics of Financial Econometrics
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Econometrics is the branch of economics that draws heavily on statistics for
testing and analyzing economic relationships. Within econometrics, there
are theoretical econometricians who analyze statistical properties of estimators
of models. Several recipients of the Nobel Prize in Economic Sciences
received the award as a result of their lifetime contribution to this branch of
economics. To appreciate the importance of econometrics to the discipline of
economics, when the first Nobel Prize in Economic Sciences was awarded in
1969, the co-recipients were two econometricians, Jan Tinbergen and Ragnar
Frisch (the latter credited for first using the term econometrics in the sense
that it is known today). The co-recipient of the 2013 Nobel Prize was Lars
Peter Hansen who had made major contributions to the field of econometrics.
Further specialization within econometrics, and the area that directly
relates to this book, is financial econometrics. As Jianqing Fan writes, the
field of financial econometrics
uses statistical techniques and economic theory to address a variety
of problems from finance. These include building financial models,
estimation and inferences of financial models, volatility estimation,
risk management, testing financial economics theory, capital asset
pricing, derivative pricing, portfolio allocation, risk-adjusted returns,
simulating financial systems, hedging strategies, among others.1
Robert Engle and Clive Granger, two econometricians who shared the 2003
Nobel Prize in Economics Sciences, have contributed greatly to the field of
financial econometrics.
Why this book? There is growing demand for learning and teaching
implementation issues related to the deployment of financial econometrics
in finance. The unique feature of this book is the focus on applications and
implementation issues of financial econometrics to the testing of theories
and development of investment strategies in asset management. The key
mes�sages expressed in this book come from our years of experience in designing, developing, testing, and operating financial econometric applications
in asset management.
In this book we explain and illustrate the basic tools that are needed
to implement financial econometric models. While many books describe the
abstract mathematics of asset management, the unique feature of this book is
to address the question of how to construct asset management strategies using
financial econometric tools. We discuss all aspects of this process, including
model risk, limits to the applicability of models, and the economic intuition
behind models. We describe the critical issues using real life examples.
We start by discussing the process of applying financial econometrics
to asset management. The three basic steps of model selection, estimation,
and testing are discussed at length. We emphasize how in this phase economic
intuition plays an important role. Before designing models we have to
decide what phenomena we want to exploit in managing assets.
We then discuss the most fundamental financial econometric technique:
regression analysis. Despite its apparent simplicity, regression analysis is a
powerful tool the application of which requires careful consideration. We
describe different types of regression analysis, including quantile regressions
and regressions with categorical variables, their applicability, and the conditions
under which regression fails. We discuss the robustness of regression
analy�sis, introducing the concept and technique of robust regression. All
concepts are illustrated with real-life examples.
Next, we analyze the dynamic behavior of time series, introducing vec�tor
and scalar autoregressive models. We formalize mean-reversion, intro�ducing
the concept of cointegration, and describe the heteroscedastic behav�ior of
financial time series. We discuss the economic intuition behind each model,
their estimation, and methods for parameter testing. We also analyze the
limits of the applicability of autoregressive techniques, the advantage of
exploiting mean reversion when feasible, and the model risk associated with
autoregressive models. We again use real-life examples to illustrate.
Subsequently, we move to consider large portfolios and discuss the techniques
used to model large numbers of simultaneous time series, in particular
factor models and principal components analysis. The issues associated
with the estimation and testing of large models and techniques to separate
information from noise in large sets of mutually interacting time series are
discussed.
Finally, we discuss the specific process of implementing a financial
econometric model for asset management. We describe the various steps of
this process and the techniques involved in making modeling decisions.
One important characteristic of model development today is the availability
of good econometric software. Many building blocks of the process
of implementing a financial econometric application are available as off-the-shelf software. Most technical tasks, from optimization to the
estima�tion of regression and autoregressive models, are performed are performed
by econometric software. Using these software tools has become
common practice among those who develop financial applications. For this
reason we do spend much time discussing computational issues. These are
highly technical subjects that are handled by specialists. The general user
and/or developer of econometric applications do not spend time in rewriting
appli�cations that are commercially available. For this reason we focus on
the process of designing financial econometric models and we do not handle
the computational aspects behind basic techniques.
Frank J. Fabozzi
Sergio M. Focardi
Svetlozar T. Rachev
Bala G. Arshanapalli
978-1-118-72743-0
NONE
Management
English
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