Ricerc@Sapienza

This paper introduces a new temperature index, which can suitably
represent the underlying of a weather derivative. Such an index is
defined as the weighted mean of daily average temperatures measured
in different locations. It may be used to hedge volumetric risk, that is
the effect of unexpected fluctuations in the demand/supply for some
specific commodities – of agricultural or energy type, for example –
due to unfavorable temperature conditions.
We aim at exploring the long term memory property of the volatility
of such an index, in order to assess whether there exist some long-run
paths and regularities in its riskiness. The theoretical part of the paper
proceeds in a stepwise form: first, the daily average temperatures
are modeled through autoregressive dynamics with seasonality in mean
and volatility; second, the assessment of the distributional hypotheses
on the parameters of the model is carried out for analyzing the long
term memory property of the volatility of the index. The theoretical
results suggest that the single terms of the index drive the long memory
of the overall aggregation; moreover, interestingly, the proper selection
of the parameters of the model might lead both to cases of persistence
and antipersistence. The applied part of the paper provides some insights
on the behaviour of the volatility of the proposed index, which
is built starting from single daily average temperature time series.