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Weather Forecasting for Weather Derivatives

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  • Sean D. Campbell
  • Francis X. Diebold

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 both strong conditional mean dynamics and conditional variance dynamics in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. The approach can easily be used to produce not only short-horizon point forecasts, but also the long-horizon density forecasts of maximal relevance in weather derivatives contexts. We produce and evaluate both, with some success. We conclude that additional inquiry into nonstructural weather forecasting methods will likely prove useful in weather derivatives contexts.

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of the American Statistical Association.

Volume (Year): 100 (2005)
Issue (Month): (March)
Pages: 6-16

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Handle: RePEc:bes:jnlasa:v:100:y:2005:p:6-16

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References

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  1. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  2. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating Density Forecasts," Center for Financial Institutions Working Papers 97-37, Wharton School Center for Financial Institutions, University of Pennsylvania.
  3. M. Davis, 2001. "Pricing weather derivatives by marginal value," Quantitative Finance, Taylor and Francis Journals, vol. 1(3), pages 305-308.
  4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2002. "Parametric and Nonparametric Volatility Measurement," NBER Technical Working Papers 0279, National Bureau of Economic Research, Inc.
  5. Seater, John J, 1993. "World Temperature-Trend Uncertainties and Their Implications for Economic Policy," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 265-77, July.
  6. Hansen, B.E., 1992. "Autoregressive Conditional Density Estimation," RCER Working Papers 322, University of Rochester - Center for Economic Research (RCER).
  7. Hyndman, R.J. & Grunwald, G.K., 1999. "Generalized Additive Modelling of Mixed Distribution Markov Models with Application to Melbourne's Rainfall," Monash Econometrics and Business Statistics Working Papers 2/99, Monash University, Department of Econometrics and Business Statistics.
  8. M. H. Pesaran & R. G. Pierse & M. S. Kumar, 1988. "Econometric Analysis of Aggregation in the Context of Linear Prediction Models," UCLA Economics Working Papers 485, UCLA Department of Economics.
  9. Harvey, Campbell R. & Siddique, Akhtar, 1999. "Autoregressive Conditional Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(04), pages 465-487, December.
  10. Christoffersen, Peter F. & Diebold, Francis X., 1997. "Optimal Prediction Under Asymmetric Loss," Econometric Theory, Cambridge University Press, vol. 13(06), pages 808-817, December.
  11. Sean D. Campbell & Francis X. Diebold, 2002. "Weather Forecasting for Weather Derivatives," Center for Financial Institutions Working Papers 02-42, Wharton School Center for Financial Institutions, University of Pennsylvania.
  12. repec:cup:etheor:v:13:y:1997:i:6:p:808-17 is not listed on IDEAS
  13. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  14. Christoffersen, Peter F & Diebold, Francis X, 1996. "Further Results on Forecasting and Model Selection under Asymmetric Loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 561-71, Sept.-Oct.
  15. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1999. "Multivariate Density Forecast Evaluation And Calibration In Financial Risk Management: High-Frequency Returns On Foreign Exchange," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 661-673, November.
  16. Roll, Richard, 1984. "Orange Juice and Weather," American Economic Review, American Economic Association, vol. 74(5), pages 861-80, December.
  17. Francis X. Diebold & Anthony S. Tay & Kenneth F. Wallis, 1997. "Evaluating Density Forecasts of Inflation: The Survey of Professional Forecasters," NBER Working Papers 6228, National Bureau of Economic Research, Inc.
  18. Tim Bollerslev & Jeffrey M. Wooldridge, 1988. "Quasi-Maximum Likelihood Estimation of Dynamic Models with Time-Varying Covariances," Working papers 505, Massachusetts Institute of Technology (MIT), Department of Economics.
  19. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-54, May-June.
  20. Enric Valor & Hipòlit Torró & Vicente Meneu, 2001. "Single Factor Stochastic Models With Seasonality Applied To Underlying Weather Derivatives Variables," Working Papers. Serie EC 2001-22, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  21. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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