End-of-day forward curves are widely used for marking-to-market all trading positions and are therefore a fundamental piece of information for any company active in the energy and commodity market.
A good forward curve not only includes seasonal patterns, but also deviates between weekdays and weekends. It should furthermore incorporate public holidays, and contain the right level of variation between the hours. KyCurve performs all these tasks automatically when new settlement data arrive. Use the KyCurve model to construct detailed curves for power, gas and other commodity markets.
it is possible to set the optimization and valuation models of the KYOS Analytical Platform in such a way that these models recalculate automatically. Each time KyCurve creates a new end-of-day market price forward curve, it will subsequently trigger the models. As a result, you will always have optimal pricing and hedging information, ready to use for trading and reporting purposes. Ideal for your decision making and price setting in energy and commodity markets!
The KyCurve model can be delivered as a model in the KYOS Analytical Platform. This allows you for instance to schedule calculations based on your own settings and data feeds. Alternatively, KYOS offers price forward curves for many European power and gas markets as a service via www.pricecurves.com. Depending on your preference, you can choose between both possibilities.
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KyCurve uses advanced statistics to make an end-of-day forward curve based on available settlement data.
Of course we have fully integrated KyCurve in the KYOS Analytical Platform. Automated data feeds with exchanges and data providers ensure you always have the most recent end-of-day prices, which you can immediately use for your contract pricing, power plant dispatch or MtM calculations.
KyCurve applies an advanced statistical methodology to calculate detailed price curves. Seasonal shapes are found based on historical relationships in forward markets. On the other hand, historical time series form the basis to filter weekday patterns. Furthermore, an innovative statistical routine determines the optimal daily and hourly shaping. And not to forget, the optimization algorithm takes the impact of public holidays and bridge days into account.
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