Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56665
Authors: 
Härdle, Wolfgang Karl
López Cabrera, Brenda
Okhrin, Ostap
Wang, Weining
Year of Publication: 
2010
Series/Report no.: 
SFB 649 discussion paper 2011-001
Abstract: 
On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. In order to apply pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and inter temporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic pattern. The object of this research is to estimate this heteroscedastic function so that after scale normalisation a pure standardised Gaussian variable appears. Earlier work investigated this temperature risk in dfferent locations and showed that neither parametric component functions nor a local linear smoother with constant smoothing parameter are flexible enough to generally describe the volatility process well. Therefore, we consider a local adaptive modeling approach to find at each time point, an optimal smoothing parameter to locally estimate the seasonality and volatility. Our approach provides a more flexible and accurate fitting procedure of localised temperature risk process by achieving excellent normal risk factors.
Subjects: 
weather derivatives
localising temperature residuals
seasonality
local model selection
JEL: 
G19
G29
G22
N23
N53
Q59
Document Type: 
Working Paper

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