Preface
This technical document details the long term forecasting and
scenario generation methodologies in LongRun. It contains
two sets of techniques for computing forecast values and confidence
intervals for asset prices and a procedure for generating
scenarios for use in Monte Carlo. In some circles of the economics
and finance professions, forecasting is not a highly regarded
activity.
For some, it evokes images of speculators, chart analysts and
questionable investor newsletters; for others, memories of the
grandiose econometric forecasting failures of the 1970's. There is
nonetheless a need for forecasting in risk management. A prudent
corporate treasurer or fund manager must have some way of
measuring the risk to earnings, cash flows or returns. Any measure
of risk must incorporate some estimate of the probability
distribution of the future asset prices on which financial
performance depends. Forecasting is an indispensable element of
prudent financial management.
How should corporate treasurers and fund managers approach
forecasting? Forecasting accuracy per se is not the object of the
exercise: every currently known forecasting tool often falls wide
of the mark. In a risk management context, the forecasts should
rather be practical, based on objective techniques, it must be
possible to examine how the methodologies would have performed had
they been applied in the past, and it should be possible to
articulate the techniques to shareholders, investors, and
regulators. It is also desirable to have available different,
methodologically independent forecasting techniques. The risk
manager can then compare the results with one another and with his
own judgements about future asset prices. We believe that
LongRun meets these criteria for forecasting techniques.
The RiskMetrics Group's policy is to make public its risk
management methodologies. In doing so we aim to foster pubic
discussion of our approach, to help our clients grasp the
methodologies which underline our products, and more generally to
promote public understanding of risk management issues. We hope
that interested practitioners and scholars will examine the
LongRun methodology and look forward to studying their
criticisms, alternative approaches, and suggested applications.
The authors have enjoyed support and constructive comments from
a number of colleagues. We would particularly like to thank Ethan
Berman, Alvin Lee and Jim Ye of the RiskMetrics Group, Mark Everson
of Ford Motor Company, and John Byma of the Procter & Gamble
Company for their detailed comments on several drafts of this
document. Christopher Finger of the RiskMetrics Group helped
formulate LongRun's simulation procedures and had many useful
suggestions throughout. Peter Zangari was instrumental in the early
stages of this project. We would also like to thank Tatiana
Kolubayev for editing and producing this document.
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