 Forecasting temperature to price CME temperature derivativesDebbie J. Dupuis International Journal of Forecasting, 2011, vol. 27, issue 2, 602-618 Abstract: This paper seeks to forecast temperatures in US cities in order to price temperature derivatives on the Chicago Mercantile Exchange (CME). The CME defines the average daily temperature underlying its contracts as the average of the maximum and minimum daily temperatures, yet all published work on temperature forecasting for pricing purposes has ignored this peculiar definition of the average and sought to model the average temperature directly. This paper is the first to look at the average temperature forecasting problem as an analysis of extreme values. The theory of extreme values guides model selection for temperature maxima and minima, and a forecast distribution for the CME's daily average temperature is found through convolution. While univariate time series AR-GARCH and regression models generally yield superior point forecasts of temperatures, our extreme-value-based model consistently outperforms these models in density forecasting, the most important risk management tool. Keywords: Extreme; value; theory; Density; forecasts; AR-GARCH; models; Regression; models; Weather; forecasting; Financial; markets (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:27:y::i:2:p:602-618 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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