Weather Forecasting for Weather Derivatives



CFS Working Paper No. 2004/10

Weather Forecasting for Weather Derivatives

Sean D. Campbell* and Francis X. Diebold*
revised version: January 2, 2004

Abstract:

We take a simple time-series approach to modeling and forecasting daily average temperature
in U.S. cities, and we inquire systematically as to whether it may prove useful from the
vantage point of participants in the weather derivatives market. The answer is, perhaps
surprisingly, yes. Time-series modeling reveals conditional mean dynamics, and crucially,
strong conditional variance dynamics, in daily average temperature, and it reveals sharp
differences between the distribution of temperature and the distribution of temperature
surprises. As we argue, it also holds promise for producing the long-horizon predictive
densities crucial for pricing weather derivatives, so that additional inquiry into time-series
weather forecasting methods will likely prove useful in weather derivatives contexts.

Key Words: Risk management; hedging; insurance; seasonality; temperature; financial derivatives

We thank the editor, associate editor, and three referees for insightful comments that improved this paper, and
we thank the Guggenheim Foundation, the National Science Foundation, the Wharton Financial Institutions
Center, and the Wharton Risk Management and Decision Process Center for support. We are also grateful for
comments by participants at the American Meteorological Society’s 2001 Policy Forum on Weather, Climate
and Energy, WeatherRisk 2002, and conferences at the Universities of Florence and Montreal, as well as
Marshall Blume, Larry Brown, Geoff Considine, John Dutton, Rob Engle, John Galbraith, René Garcia, Stephen
Jewson, Vince Kaminski, Paul Kleindorfer, Howard Kunreuther, Yu Li, Bob Livezey, Cliff Mass, Don McIsaac,
Nour Meddahi, David Pozo, Matt Pritsker, S.T. Rao, Claudio Riberio, Til Schuermann, and Enrique Sentana.
None of those thanked, of course, are responsible in any way for the outcome.

Correspondence: F.X. Diebold, Department of Economics, University of Pennsylvania, 3718 Locust
Walk, Philadelphia, PA 19104-6297.

* Brown University

* University of Pennsylvania, and NBER, University of Pennsylvania and NBER, fdiebold@,wharton.upenn.edu



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