# Trading commodities for weather patterns

Weather derivatives slowly began trading over-the-counter in As the market for these products grew, the Chicago Mercantile Exchange CME introduced the first exchange-traded weather futures contracts and corresponding options , in Most of these financial instruments track cooling degree days or heating degree days, but other products track snowfall and rainfall in at ten separate U. The CME Hurricane Index, an innovation developed by the reinsurance industry provides contracts that are based on a formula derived from the wind speed and radius of named storms at the point of U.

A major early pioneer in weather derivatives was Enron Corporation, through its EnronOnline unit. In an Opalesque video interview, Nephila Capital 's Barney Schauble described how some hedge funds treat weather derivatives as an investment class.

Counterparties such as utilities, farming conglomerates, individual companies and insurance companies are essentially looking to hedge their exposure through weather derivatives, and funds have become a sophisticated partner in providing this protection. There has also been a shift over the last few years from primarily fund of funds investment in weather risk, to more direct investment for investors looking for non-correlated items for their portfolio.

Weather derivatives provide a pure non-correlated alternative to traditional financial markets. An online weather derivative exchange Massive Rainfall [2] was created in and has been used to bet or hedge on specific temperatures, wind speeds and rainfall for specific days in select cities, however it appears to be only an educational tool for practice accounts in a non-existent currency.

There is no standard model for valuing weather derivatives similar to the Black—Scholes formula for pricing European style equity option and similar derivatives. That is because the underlying asset of the weather derivative is non-tradeable which violates a number of key assumptions of the BS Model.

Typically weather derivatives are priced in a number of ways:. Business pricing requires the company utilizing weather derivative instruments to understand how its financial performance is affected by adverse weather conditions across a variety of outcomes i.

Alternatively, an investor seeking a certain level of return for a certain level of risk can determine what price he is willing to pay for bearing particular outcome risk related to a particular weather instrument. The historical payout of the derivative is computed to find the expectation.

The method is very quick and simple, but does not produce reliable estimates and could be used only as a rough guideline. It does not incorporate variety of statistical and physical features characteristic of the weather system. This approach requires building a model of the underlying index, i.

The simplest way to model the index is just to model the distribution of historical index outcomes. We can adopt parametric or non-parametric distributions. For monthly cooling and heating degree days, assuming a normal distribution is usually warranted. The predictive power of such a model is rather limited.

A better result can be obtained by modelling the index generating process on a finer scale. In the case of temperature contracts, a model of the daily average or min and max temperature time series can be built.

The daily temperature or rain, snow, wind, etc. An online weather derivative exchange Massive Rainfall [2] was created in and has been used to bet or hedge on specific temperatures, wind speeds and rainfall for specific days in select cities, however it appears to be only an educational tool for practice accounts in a non-existent currency. There is no standard model for valuing weather derivatives similar to the Black—Scholes formula for pricing European style equity option and similar derivatives.

That is because the underlying asset of the weather derivative is non-tradeable which violates a number of key assumptions of the BS Model. Typically weather derivatives are priced in a number of ways:.

Business pricing requires the company utilizing weather derivative instruments to understand how its financial performance is affected by adverse weather conditions across a variety of outcomes i. Alternatively, an investor seeking a certain level of return for a certain level of risk can determine what price he is willing to pay for bearing particular outcome risk related to a particular weather instrument. The historical payout of the derivative is computed to find the expectation. The method is very quick and simple, but does not produce reliable estimates and could be used only as a rough guideline.

It does not incorporate variety of statistical and physical features characteristic of the weather system. This approach requires building a model of the underlying index, i. The simplest way to model the index is just to model the distribution of historical index outcomes. We can adopt parametric or non-parametric distributions. For monthly cooling and heating degree days, assuming a normal distribution is usually warranted. The predictive power of such a model is rather limited.

A better result can be obtained by modelling the index generating process on a finer scale. In the case of temperature contracts, a model of the daily average or min and max temperature time series can be built. The daily temperature or rain, snow, wind, etc. ARMA or Fourier transform in the frequency domain purely based only on the features displayed in the historical time series of the index.

We can utilize the output of numerical weather prediction models based on physical equations describing relationships in the weather system. Their predictive power tends to be less than, or similar to, purely statistical models beyond time horizons of 10—15 days. Ensemble forecasts are especially appropriate for weather derivative pricing within the contract period of a monthly temperature derivative.

However, individual members of the ensemble need to be 'dressed' for example, with Gaussian kernels estimated from historical performance before a reasonable probabilistic forecast can be obtained. A superior approach for modelling daily or monthly weather variable time series is to combine statistical and physical weather models using time-horizon varying weight which are obtained after optimization of those based on historical out-of-sample evaluation of the combined model scheme performance.

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