Weather Forecasting for Weather Derivatives

By Sean D. Campbell, Francis X. Diebold. 2005

361 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/bbb62ad3b8a8af16ebd1e8c4add8cd9aa543fda9

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Citations in the corpus

Probabilistic forecasts, calibration and sharpness (2007, positive): “This approach was applied by Diebold, Gunther and Tay (1998), Weigend and Shi (2000), Bauwens et al. (2004) and Campbell and Diebold (2005) , among others.”
Likelihood-based scoring rules for comparing density forecasts in tails (2011, neutral): “…the probability integral transform (PIT) of 1 In addition, density forecasts are starting to be used in other financial decision problems, such as derivative pricing (Campbell and Diebold, 2005; Taylor and Buizza, 2006; Härdle and Hlávka, 2009) and asset allocation (Guidolin and Timmermann, 2007).”
The use of weather forecasts in the pricing of weather derivatives (2003, neutral): “(One could make the comparison quantitative through a cost/benefit analysis in which the benefit of not having to pay for NWP forecasts is balanced against the costs due to the lower skill of the stochastic model, but this is beyond the scope of the present study.) The use of stochastic model foreca”
Testing Forecast Optimality Under Unknown Loss (2007, neutral): “(2003) and Campbell and Diebold (2005), although applications of such results to empirical data will be limited, because currently the vast majority of published forecasts are point forecasts.”
The impact of climate change on the cost of bank loans (2020, neutral): “focused on quantifying the climate risk (Campbell and Diebold 2005; Daniel et al. 2017).”
Temperature stochastic modeling and weather derivatives pricing: empirical study with Moroccan data (2009, neutral): “[10] Campbell, S.”
Photovoltaic and solar power forecasting for smart grid energy management (2015, background): “The persistence approach is always regarded as a naive predictor, widely used for meteorology-related forecasting [22].”
Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Demand (2009, positive): “A similar approach was used by Campbell and Diebold (2005) to model conditional variance in daily average temperature data, and by Taylor (2006) to forecast electricity consumption.”
Climate risks and market efficiency (2017, neutral): “, Roll (1984), Campbell and Diebold (2005)). Our study of climate change risks and market efficiency helps characterize the nature of the potential inefficiencies, which might inform regulatory responses and be useful for practitioners interested in the construction of quantitative risk-management m”