an educational tool for practice accounts in a non-existent currency. Valuation 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 Black-Scholes Model. Typically weather derivatives are priced in a number of ways: Business pricing 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.e. obtain a utility curve with respect to particular weather variables). Then the user can determine how much he/she is willing to pay in order to protect his/her business from those conditions in case they occurred based on his/her cost-benefit analysis and appetite for risk. In this way, a business can obtain a "guaranteed weather" for the period in question, largely reducing the expenses/revenue variations due to weather. 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. Historical pricing (Burn analysis) 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. Index modelling This approach requires building a model of the underlying index, i.e. the one upon which the derivative value is determined (for example monthly/seasonal cumulative heating degree days). 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.) model can be built using common statistical time series models (i.e. ARMA or Fourier transform in the frequency domain) purely based only on the features displayed