Effective use of market benchmarks for the value of flexibility28 Nov, 2011
As we presented in a recent article, there are a surprising number of publicly available benchmarks for the value of flexibility in European power and gas markets. A broad range of physical asset operating characteristics and a lack of standardised flexibility terms in contract structures, mean that clean ‘like for like’ comparisons with the publicly available flexibility benchmarks are difficult. As such, the issue is not so much data availability, but designing innovative approaches and interpretations to extract insight and value from the information.
There is no formulaic approach or set of rules that can be applied when using market benchmarks to analyse and value flexibility. However, there are two broad categories of ways this data can be used:
- As a direct market benchmark for the value of flexibility.
- As a source of data to generate implied values for price model input parameters that are not directly observable in the market.
What price does the market place on equivalent flexibility?
The clearest and most accessible use of market flexibility benchmarks is a direct comparison of value against results from bottom-up models. The most obvious comparison is the ‘like for like’ extrinsic value premium for the same type of asset or contract. For example, project developers of seasonal storage assets can use the history of Rough capacity auction prices as an indicator of the market value participants place on the service. This should be viewed in the context of market conditions at the time, but allows a comparison of how the value of flexibility may be influenced by changes in market fundamentals.
Whilst historic pricing should not be used as a direct predictor of value, it facilitates analysis to imply how the value for flexibility may change in relation to the expected market fundamentals. For example, seasonal spreads and volatility are the key value drivers of long cycle gas storage. By examining these parameters, at the time of the Rough capacity auctions it is possible to imply how the value of storage may change in relation to future market fundamentals.
It is more difficult finding market benchmarks for non-traded sources of flexibility such as gas swing contracts. However storage value benchmarks can be used to imply the extrinsic premium value. This is possible as the embedded flexibility in gas storage and swing contracts is similar in structure. In fact, a gas swing contract can be viewed as a special case of a gas storage facility which starts full and has no injection capability. Diagram 1 demonstrates the equivalent volume profiles for a gas storage facility and swing contract.
Diagram 1. Equivalence of gas storage and swing contract profiles.
The injection and withdrawal profile of a storage contract has the same structure as the optimised lift of a gas swing contract. When an optimised swing contract lift pattern is viewed as a movement away from an average daily quantity it has the same structure of an injection and withdrawal profile of a storage contact. The value maximising strategy of injecting during low price periods and withdrawing during high price periods are equivalent to minimising and maximising lift. So by comparing the flexibility of a gas swing contract against an equivalent storage contract it is possible to get a broad expectation of the gas swing contract flexibility value.
Market Implied Values for Model Input Parameters
The second category of potential uses for market flexibility benchmarks is to derive price model input parameters from market data. There is a long established approach of using vanilla option prices from exchange or OTC trades to derive implied volatilities which are then used as inputs for the valuation of option portfolios. The logic here is that using implied volatilities from market data captures the market expectation of future volatility. Whereas, calculating them from historic price movements, only illustrates how volatile prices have been in the (recent) past or a single internal view of future volatility. This has been the case for the UK gas market for many years where broker provided OTC quotes have been used to generate an “At-The-Money” volatility curve. More recently the listing of NBP options on ICE has allowed implied volatility surfaces to be calculated. The same logic and approach can be used for other less intuitive pricing model input parameters, either as the direct source of the values or as an alternative benchmark using a different estimation technique. As with calculating implied volatilities the approach is to hold the known input parameters constant and vary the parameter that is being estimated in a pricing model until the model calculated value is consistent with the available market benchmark. Below are a few examples.
- EdF virtual power capacity can be used to calculate implied volatility for the French power market. With the fixed strike these contracts are equivalent to a strip of hourly call options and would need to be deconstructed as such to estimate absolute hourly volatilities.
- Results of interconnector or capacity valuations, such as the UK-FR or UK-NL power interconnectors, can be used to calculate implied correlations between different regional markets.
- Other more obscure and less intuitive parameters can also be estimated. For example, “mean reversion” used to control how quickly the price returns to some equilibrium level is a common parameter in many complex flexibility valuation models. Use of market benchmarks to estimate these types of parameters is important as appropriate values are less obvious and intuitive.
Market data is not limited to forward curves. The European gas and power market has a broad range of publicly available market benchmarks for the value of flexibility. These provide a valuable source of data to help inform and validate valuation analysis. The challenge is to use the available data in innovative ways to supplement the analysis and extract insight. This can either be as a direct benchmark for equivalent flexibility or as a source to imply values for model input parameters. However, as there is no standard approach there are many pitfalls that can invalidate the analysis and lead to errors or invalid conclusions. Ultimately, a successful outcome requires a deep understanding of commodity price dynamics and the nature of the flexibility being analysed.