A decade of "Rough" storage trading results in the UK NBP gas market:

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1 A decade of "Rough" storage trading results in the UK NBP gas market: Benchmarking storage trading strategies Cyriel de Jong, Alexander Boogert, Christopher Clancy KYOS Energy Consulting How much money can be made? Large investments are made in gas storage facilities in order to balance supply and demand and to make profits in the trading market. Whereas the ultimate use of the storage is to balance physical gas of various users, the majority of storage valuation approaches assumes a market trading strategy. This is for a good reason, because it leads to a consistent valuation framework, more or less available to everyone in the marketplace, and independent from company-specific portfolios. As quantitative consultants and model developers of the UK, Continental, and North American commodity markets, we often get the question whether or not the values from storage valuation models, including ours, should be trusted. In particular, the extrinsic spot-based value is often considered as a mystical number that is too good to be true. In order to find a compelling answer, we therefore performed a large-scale backtesting study of the UK gas market, the most liquid in Europe. In this article, we share the results and our insights. The backtest looks at the performance of different storage trading strategies in the UK NBP gas market. We assess a storage bundle mimicking the characteristics of the Rough storage, the largest in the UK. The backtest period covers 12 years, from 1997 until 28. Every half year, the storage model calculates the expected storage value over the forthcoming 12 months. This 'projected' present value (ppv) is either the intrinsic value (based on the current forward curve), the rolling intrinsic value (based on changes in the forward prices over time), or the spot-based value. In the backtest, we then carry out the underlying trading strategies in the market over the front 12 months. Our results indicate that the profitability of storage trading has varied largely over time, mainly due to variations in winter-summer spreads and price volatilities. When a trader would have relied on a pure spot trading strategy only, he would have done very well in some years, but in fact, often performed below expectation (the ppv being the expectation). This research discusses various explanations. However, a combination of a spot trading and a forward market hedging strategy completely changes the picture. The trader performance then matches closely with the expectation and he can be quite sure to realize both the intrinsic and the extrinsic value. Storage and price model description The Rough storage facility is a depleted offshore gas field with a 3217 MCM working gas capacity. The minimum cycle time of the Rough storage is about 232 days. This can be calculated from the time it takes to fill the storage (161 days) and the time it takes to empty the storage (71 days). In this

2 study we normalize the capacity to 1, and work with rescaled injection and withdrawal rates of.62 and 1.4 respectively. The Rough storage mainly serves as a seasonal storage that balances different summer and winter demand levels. While seasonal storage is certainly the main benefit of the Rough gas storage, adjusting injection and withdrawal rates according to spot prices can yield extra profit. The KYOS storage model uses a 3-factor gas price simulation model that captures the combined movements of gas spot-and forward prices in an intuitive way. The following factors are used: Long-term factor, creating parallel shifts of the forward curve. The annualized volatility of this factor is estimated based on 1 year-ahead calendar gas prices. Seasonal factor, creating changes in the winter-summer forward spread. The annualized volatility of this factor is estimated based on the respective nearby quarter and yearly prices with the smallest time to maturity. Short-term factor, creating changes spot prices and in the short-end of the curve. The spot price is assumed to mean-revert to (roughly) the month-ahead forward price. The estimates of the volatility and mean reversion parameters are estimated using a moving window of 1 year, and updated at the beginning of every calendar month. On 1 April and 1 October of every year, the market parameters and market forward prices are fed into the Least-Squares Monte Carlo algorithm underlying the KYOS storage model. The inventory begin and end levels equal for a storage starting on 1 April and equal 1 for a storage starting on 1 October. Evaluation of trading strategies over 1, price scenarios leads to a projected present value of Rough storage in the spot market. At the same time, the model calculates the intrinsic value and the rolling intrinsic value. See Boogert and De Jong (29) for further details of the storage valuation methodologies. Projected storage value: Winter-summer spread and Spot volatility Profitability of storage trading has varied largely over time. Storage trading value in the period was about four times higher than in between 1997 and 24. The primary explanation is the size of the winter-summer spread in the forward market: between 5 and 1 p/th initially, then rising up to 5 p/th in April 26. This dependence of storage value on winter-summer spread is most explicit for the intrinsic value. Cf Figure XX. Because of the paramount role of winter-summer spreads it is one of the three important risk factors in the KYOS forward curve model, explained further on.

3 Apr-97 Nov-97 Jun-98 Jan-99 Aug-99 Mar- Oct- May-1 Dec-1 Jul-2 Feb-3 Sep-3 Apr-4 Nov-4 Jun-5 Jan-6 Aug-6 Mar-7 Oct-7 Intr value Wi-Su spread The intrinsic value is effectively a lower bound to the fair value of the storage; it can be locked in at the start of the trading period. Generally, more storage value can be captured by exploiting the short-term price volatility and mean-reversion rate (cf De Jong and Walet, Energy Risk). Storage has a high (daily) storage flexibility, so it would be beneficial and prudent to use it. Even with the low-cycle storage under consideration, on average there is about 5% extrinsic value on top of intrinsic (note: total value = extrinsic + intrinsic value). In general, the more volatile the spot market prices, the higher is the extrinsic value. This is evident in Figure XX. 14 Extrinsic storage value % 1% 15% 2% 25% Spot volatility Realized storage value Nevertheless, the extrinsic storage value is not easily realized and trading in the spot market does not provide any guarantee of success. On average, the realized spot value is only 7% of the projected value (ppv), with a stunning standard deviation of 62%. Out of the 22 periods, two even yield negative profits, both in the last 2 years of analysis. To put it another way, not surprisingly, a

4 spot trading strategy is riskier than a rolling intrinsic strategy. A careful investigation of each individual case reveals the patterns leading to this result: Market parameters are estimated with a moving window of one year, so as to match the price dynamics in the preceding year. The volatility and mean-reversion parameters that are actually realized in the trading period, are often quite different. This is a classical example of parameter uncertainty. It creates variations in trading performance, but also makes the spot strategy somewhat less optimal. In many years, the forward prices for delivery in the nearby winter were relatively high. However, when the actual winter drew nearer, in several years the forward prices and realized spot prices collapsed. So, whereas the forward market indicated that the storage had to be filled prior to the winter, the storage trader had no choice other than to sell at a loss in the winter spot market. Likewise, summer forward prices were often underpriced. The relatively systematic nature of these patterns suggests a risk premium in the NBP forward market: a positive premium for forward winter gas and a negative premium for forward summer gas. One such example, for March 98 to March 99, is displayed in Figure XX and XX.

5 Forward price (p/th) Apr1998 Jul1998 Oct1998 Jan1999 Apr Spot price Inventory level rescaled 4 2 Apr1998 Jul1998 Oct1998 Jan1999 Apr1999

6 Luckily, no sensible storage trader will put himself in such a locked-up position in the winter, as in Figure XX. Spot trading will be on top of a customer portfolio or combined with a hedging strategy. We evaluated four different hedging strategies: A 'static' intrinsic hedge and a 'static' delta hedge, implemented on the first day of the storage period. The pay-off of both forward hedges is evaluated separately from the spot trading performance and are deemed pure financial hedges. For example, when the hedge involved a forward sale of 1 million therms in January at a price of 2 p/th, and the average realized January spot price was 15 p/th, the trade leads to a profit of 5 million pence (5, GBP). A 'dynamic' intrinsic hedge and a 'dynamic' delta hedge. Both are implemented on the first day of the storage period for all months, except the most nearby month. One month later, after some spot trading has been carried out, and after forward prices have moved, the optimal hedge is calculated anew and implemented for each month, except (again) the nearby month. So, there is never overlap between the forward position and the period in which is traded in the spot market. All four hedge strategies enhance storage value considerably, and stabilize the profits considerably when measured against the projected value. The realized value is equal to about 1% of the predicted value from the model. This means, the extrinsic value that the storage model predicts, can really be made in practice. Surprisingly, the static hedges achieve about the same performance as the dynamic hedges. This means, it is eventually not hard to make the projected value, as long as there is discipline not to take too large speculative positions. As a final strategy, we also evaluated how much money would have been realized by trading in the forward market only. That could be an even less risky strategy. The rolling intrinsic trading strategy ensures that a trader makes at least the intrinsic value, and in many scenarios more than that as a result of changes in the shape of the forward curve. In our setup, at the beginning of a month a rehedge is allowed. This leads to a value enhancement of 14% on top of the intrinsic value. Compared to the 4% average realized extrinsic value of the hedged spot strategy, only forward trading is clearly suboptimal.

7 Projected value Start End Intrinsic value Rolling intrinsic value Spot value Spot only Spot + Static Intrinsic hedge Realized value / Projected spot value Spot + Static Delta hedge Spot + Dynamic Intrinsic hedge Spot + Dynamic Delta hedge Apr-97 Mar % 14% 87% 95% 15% Oct-97 Sep % 95% 98% 15% 92% Apr-98 Mar % 97% 93% 13% 11% Oct-98 Sep % 83% 83% 95% 88% Apr-99 Mar % 95% 93% 125% 112% Oct-99 Sep % 139% 14% 155% 132% Apr- Mar % 19% 139% 18% 144% Oct- Sep % 92% 13% 74% 95% Apr-1 Mar % 112% 99% 82% 93% Oct-1 Sep % 56% 58% 78% 74% Apr-2 Mar % 9% 92% 12% 13% Oct-2 Sep % 86% 86% 81% 85% Apr-3 Mar % 117% 96% 83% 98% Oct-3 Sep % 11% 11% 81% 95% Apr-4 Mar % 65% 86% 11% 122% Oct-4 Sep % 112% 97% 9% 9% Apr-5 Mar % 131% 138% 111% 129% Oct-5 Sep % 131% 125% 17% 117% Apr-6 Mar % 73% 6% 77% 76% Oct-6 Sep % 79% 61% 81% 57% Apr-7 Mar % 88% 91% 17% 18% Oct-7 Sep % 151% 141% 136% 114% Average % 1% 97% 99% 12% Standard deviation 62% 24% 23% 21% 2% Final remarks The backtesting study provides evidence that a sound storage trading strategy combines spot trading with a forward hedging strategy. The presented results ignore any trading and operational costs or limited market liquidity, which will differ per market and per asset. This does not imply that the presented values are (still) overvaluations. A trader could add value first of all by limiting transaction costs. In addition, a skilled trader will be able to make more profit compared to our pure modelbased strategy by exploiting market intelligence, understanding market sentiment, and benefiting from portfolio effects. Senior management can, however, use a model-based strategy as a benchmark to assess the 'true' performance. The spot trading value coming out of the least-squares Monte Carlo storage model will be a useful and widely deployed benchmark. Note from the authors: The storage model used for the analysis is being used at numerous utilities and trading houses. It is also available online at live.kyos.com. The backtesting functionality is available on request, as is a report with the full backtesting results. Upon request, we can replicate the test on other markets and/or with different storage characteristics. Contact