Tue, Feb 7, 2017

Quantifying and Modelling the Optionality of Energy Assets

The dramatic drop in energy prices since 2014, and the largely abysmal returns faced by a record number of commodity-focused hedge funds in 2015 (net returns for commodity hedge funds were -7.24% in 2015), led to a lot of energy firms being under capitalized at best, and insolvent at worst.

Financing pressure on commodity trading companies have attracted hedge fund, private equity funds investors who are chasing for well-priced energy assets. This transpired in conjunction with a lot of Investment Banks exiting the commodities sector, given high regulatory capital requirements and regulatory restrictions on physical trading. 

Potential and new investors in the space, especially those without a commodity portfolio management background often face the difficult task of valuing the real option aspect of energy assets, and most importantly optimizing and monetizing their flexibility.

Energy assets and their operators possess at an individual level, an inherent optionality or flexibility to adjusting their production capacity and thereby modifying the supply and/or availability of the underlying commodity (power, gas and oil) in response to a price change or other external events, on a collective or market level. Some assets – gas storage assets, swing contracts, power generation assets for instance - require the daily, sub daily volume optimization or dispatch decisions that will significantly affect valuation and risk management. The volume optionality, “flexibility” of an energy asset also called extrinsic value can be monetized and can at times represents a key component of asset value.

The Asset value can be decomposed in two components:

  • Intrinsic value: the optimal discounted value of calendar spreads intrinsic value is the value that can be obtained from structural seasonal price spreads: for instance, Injecting gas during periods of low demand and price and withdrawing during periods of market tightness and high prices
  • Extrinsic value: all the other value that can be generated, ascribed to the asset using the flexibility of the asset, and its real option to dispatch, operate, or produce output if economical relative to market at the time, but that cannot be completely observed, forecasted or hedged at the time of the valuation (esp. if the usable life of the asset is long lived and beyond traded markets – which is often the case)

The ability to monetize the value of an asset with real optionality will largely depend on quantifying the value of that optionality (extrinsic value) and also the ability to decompose the risks of the asset or deal structure so as to be able to hedge for changing market conditions. Drivers of the extrinsic value or optionality include:

  • The shape of the forward curve. For e.g. the wider and more variable the summer-winter gap, the greater the potential value
  • Parameters such as volatility mean reversion, correlation (between forward contracts if a spread based option)
  • Physical constraints for e.g. Storage facility’s high or low turn (operational flexibility) will obviously impact both intrinsic and extrinsic values
  • Time to expiration being considered, as part of useful life of asset, ‘hedge-able’ portion of the tenor etc.


Optimization Strategies

In order to capture the extrinsic value of the asset described above, there are few common strategies.

Static Intrinsic (also called Forward Optimization)
This strategy consists of capturing the intrinsic value of the asset, the extrinsic value is not considered. The asset is optimized and hedged using the current Forward curve and the physical constraints of the asset on a “one off” basis.

Static Intrinsic and Extrinsic
Same as above but this time the extrinsic value is partially captured through the selling of the asset optionality to a third party.

This method is probably the most commonly used in the industry. The asset is hedged against the forward curve. The hedges are re-adjusted only if it is profitable preserving the owner from any market downside. This method allows capturing some of the extrinsic value and limits any earnings downside risk. Upside earnings remain variable and exposed to market price dynamics.

Spot Optimization
Under this strategy no forward hedges are undertaken. This strategy will rely on a spot price model to analyze price behavior and take decisions, resulting in riskier earning profile. This method is used for highly flexible asset with an important extrinsic value. Delta-Hedging:

It combines a spot optimization with the calculation of forward delta exposures that are hedged using available traded contracts.


Valuation Methodologies

Choosing the Valuation methodology depends on the type of asset and its characteristic, the actual portfolio management strategy, and optimal approach as defined by a portfolio manager or Trading Desk.

Intrinsic Valuation (Forward Optimization)
The Intrinsic Valuation’s value comes from seasonal or time spreads in the price of gas forward contracts. The intrinsic value is fixed on the first day, ignoring the extrinsic value that can be captured from changes in market conditions.

It uses a simple linear optimizer that looks for the combination of forward spreads that maximize the total value under some physical constraints imposed by the asset.

Basket of Spread Options
As above, this is an optimizing problem but instead of using forward contracts, the strategy aims to find an optimal portfolio of calendar spread options, subject to the asset constraints.

Rolling Intrinsic
The rolling intrinsic method is an extension to the intrinsic one that captures the changing value in the intrinsic spreads as the forward curve moves. It is a simulation based valuation. Monte Carlo simulations of the forward prices are performed for each trading day leading to hedge adjustments (if optimal). The valuation might be suboptimal because the solution does not take into account the dynamics of the forward curve that might lead to more profitable spreads.

The rolling basket of spreads strategy is developed similarly.

Spot Optimization
The decisions are made based on the spot price hence require to build a spot price model. Various numerical methods are available for this methodology:

  • Trinomial Trees
  • Least Squares Monte Carlo

While the previous strategies rely on taking positions in the forward market, in the spot optimization approach we model and optimize the value that can be obtained from making daily decisions of spot prices (power, gas). In order to ensure that the value obtained is consistent with the forward strategies described above, this method uses a spot price model calibrated to the market forward curve.

Trinomial Trees

Flexible assets can be seen as American style and path dependent options.

At each point in time, there is a decision to me made, which will an impact on future decision (path dependency). For e.g.:

  • Withdraw Gas, sell oil or increase power production
  • Inject Gas, buy oil or decrease power production

As for American Option pricing, the methods use backwards recursion to determine the maximizing action.

Least Squares Monte Carlo

N sample paths are simulated using Monte Carlo simulations

Using backwards recursion, at each point in time, a regression function is determined using least squares. This function will give the continuation value at time t-1 from the value at time t (lookback process).



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