Advanced Risk Management for Physical Commodities: The Topaz Approach

In risk management, valuations of physical commodity trades and assets are often challenging due to modeling complexity. This complexity arises naturally from the physical characteristics of the commodity, for example, gas storage characteristics or quality parameters in oil or coal, or from delivery and logistics-related complexities.

Physical long-term contracts found in commodity markets like oil, natural gas and LNG usually include optionality in quantity, pricing, and delivery location. Many variables that influence the final PnL of such deals are stochastic and interdependent. Risk managers at commodity trading companies or utilities are experimenting with building appropriate models to value these deals under uncertainty. They tend to develop their own bespoke models and usually do not trust CTRM vendors’ standard offerings.

Topaz, with its focus on risk management and quantitative analytics, has created an environment where each risk manager can represent the complexity of physical trades in line with their own valuation approach, while still working within the system’s standard reporting framework.

The handling of physical assets starts with deal capture, where pricing is the deal component with the most diversification. The invoiced price of a long-term gas or LNG trade may depend on external indices, delivered quantity, location, currency exchange rates, and may be expressed through nonlinear mathematical functions, including min or max operators. Topaz has developed a fully flexible formula language that supports multi-line formulas using both internal and external variables. These formulas can include quantities or other deal properties, support tiered pricing, conditional logic, min and max functions, alternative pricing periods, and more. The development was inspired by LNG pricing but is universally applicable to other commodities, e.g. for complex option payoffs.

The ability to integrate custom valuation models into PnL and risk calculations is a key feature for any risk solution dealing with physical commodities. Topaz takes this functionality a step further by allowing users’ containerized models to be invoked from any report or calculation within the system, ensuring seamless integration of custom logic into its risk framework.

Deal capture and payoffs are only one part of risk calculation; another important part is the simulation of market behavior including multiple stochastic variables such as prices, but also exchange rates, and interest rates. In many CTRM systems, stochastic processes for price simulations are relatively simple and often limited to multivariate normal distribution. For commodities like gas or LNG, this is not sufficient. Topaz provides a price process language that allows users to define custom pricing processes for evaluating gas swings or storage contracts.

It is also possible to calibrate pricing process parameters based on market data. Users can define parameter sets including basket structures with specific statistical characteristics and calibrate them using real market data.

In the future, as Jon Fox, the CEO of Topaz, explained, custom pricing processes will be extended to even more complex cases, including multivariate distributions across arbitrary commodities.

With fully flexible deal-level models and customizable underlying pricing processes, the solution already provides a high degree of flexibility for handling physical complexities, but that’s still not the full picture. Physical assets, including production units, may have a wide range of specific parameters that affect deal valuation. Common approaches for valuing energy production assets such as CCGTs or hydro plants represent these assets as option strips, but such models often neglect physical interdependencies. Topaz enables users not only to build custom models for valuation of these assets, but also to define custom trade types with specific parameters, assign custom pricing models to them, and use these trades within the standard reporting framework as if they were native instruments.

These custom trades can include user-defined parameters, quantity constraints, advanced pricing formulas, and complex payoffs. The integration of custom valuation models and custom pricing processes completes the picture. Jon believes that the features combined present a compelling proposition for companies managing complex physical trades, offering them a flexible and robust framework which ensures an individual valuation approach as well as fast time to market.

 

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