TITLE: Localising Temperature Risk

SPEAKER: Professor Wolfgang Haerdle

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 intertemporal 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 different
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.

Contact: "Wolfgang Haerdle" wolfgang.k.haerdle@me.com