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Presented by:

Moderator: Michael Sell, SVP, GARP, ERP® Program Manager 

Speaker: Glen Swindle, Managing Partner, Scoville Risk Partners 

July 9, 2015

GARP Webcast

Power Generation and Related Hedges:  Valuation, Pitfalls and Hedge Ratings

On24 Tech Tips• There is no call in number • Audio is streaming through your computer • Make sure your speakers are on• Hit F5 any time your console freezes• For a LIVE event you should be hearing music now• Use the “Ask a Question” feature to report issues• Webcast starts at the top of the hour

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Michael Sell, SVP, GARP, ERP® Program Manager 

Michael Sell is a Senior Vice President and program manager for GARP's Energy Risk Professional ERP ® global certification program.  Michael has more than 15 years of experience in risk management and fixed income structured product development and management. Prior to joining GARP, he was the Operating Officer of Anchor Capital Group, an investment adviser for tax-exempt investment funds.  Prior to his work at Anchor Capital he was a Vice President and Credit Officer at Citigroup where he was part of a team responsible for managing a multi-billion dollar portfolio of structured tax-exempt products.  Michael earned a BS in finance from Miami University of Ohio and an MBA from New York University.

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Glen Swindle, Managing Partner, Scoville Risk Partners

Glen Swindle is currently Managing Partner at Scoville Risk Partners, a global professional services firm specializing in valuation, risk management and portfolio analytics in the energy and commodities sectors.  Previously, Glen held senior positions at Constellation Energy, where he ran the Strategies Group for the merchant energy business, and at Credit Suisse, where, as Managing Director and Co-Head of Power and Natural Gas Trading, he ran structured trading teams responsible for significant aspects of the North American energy business. Glen is the author of Valuation and Risk Management in Energy Markets (Cambridge Univ. Press 2014).

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Outline

Context The “Usual” Approach

• Filling the gaps.

• Does it work? Alternatives How Good is the Hedge?

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Context

Generation converts “something" to electricity.

“Something" spans a spectrum of inputs:• Flowing things: Hydro and wind.

• Exogenous things: Solar and geothermal.

• Burning things: Coal, natural gas, oil.

Here we will focus on natural gas combined cycle generation.

Why combined cycles?• Large footprint.

• Interesting hedging structures supporting asset finance.

• Complexities at all time-scales.

• Continued debate on valuation methodology.

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Context

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Context - Valuation

CC generation has hourly flexibility and constraints. Valuation involves making as much money as possible.

• Math geeks write this as:

• Q() denotes hourly generation quantity. Q must satisfy constraints on dispatch – local and nonlocal.

• p(h) denotes the hourly spot price at the delivery node.

• g(h) denotes the natural gas price – usually referencing a daily index.

• H*() denotes the unit heat rate – can depend on dispatch and environment.

• V denotes additional costs – can depend on dispatch.

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Context - Goals

The overarching requirement is an analysis of the valuation formula subject to:• Prevailing market prices of relevant tradables.• Consistency with the operational attributes of the asset.

Different situations have different requirements.• Day-to-day management requires a PV and exposures (deltas).• Budgeting and earnings forecasts require distributional results.• Asset finance programs require the joint analysis of an asset and a

hedge based upon a “look-alike” asset.

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Context – Working Example

SRP FairLawn CC: A fictitious asset in PSEG Zone in PJM.

High level summary• Baseload: Capacity 600MW at heat rate 6.9.• Duct firing: Capacity 90MW at heat rate 9.0.• Power price: Fairlawn 138kV.• Fuel Price: First 35% at TETM3; remaining 65% at TZ6NY.

Litany of other features:• Seasonal variation in the attributes above.• Ramp rates.• Min run times.• Hot/warm/cold starts.• Emissions constraints.

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The Usual Approach

The general valuation equation is often thought of in a more palatable way:• Heat rate call options (HRCOs) for each day d and standard bucket B:

• Adjustments to parameters (the blue variables) to account for the “other stuff”.

People like this because:• It seems simple and easy to understand.

• It is easily booked in standard risk systems.

• It seems amenable to standard option valuation methods.

What you need now is a model for price dynamics.

Common choice: Risk neutral valuation via Margrabe.

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The Usual Approach - Inputs

The standard option pricing approach requires:• X Power forward prices –

Need: Fairlawn. Have: PSEG.

• Natural gas forward prices – Need: Sequential ordering of M3 and Z6. Have: Arithmetic combination of M3 and Z6.

• X Power implied volatilities – Need: Fairlawn implied vols (or at least PSEG). Have: PJM Western Hub.

• X Natural gas implied volatilities – Need: M3/Z6 implied vols. Have NYMEX Henry Hub implied vols.

• X Term correlations between each - Need: Term correlations between Fairlawn and M3/Z6. Have: The occasional indication/trade of say PJMWH versus M3.

Now the real work starts.• Each of the X items above requires analysis.

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The Usual Approach – Filling the Gaps

Power forwards• You have exposure to a nodal price: Fairlawn• You can only trade a zonal product: PSEG.• How does the basis behave?

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The Usual Approach – Filling the Gaps

Power forwards – A more refined view.• Price ratios versus temperature.• Motivates a model with weather drivers.• Common approach is to estimate basis and return to Margrabe as a

correction to zonal forwards.

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The Usual Approach – Filling the Gaps

Implied volatilities – extension from the few traded locations:• Regression analysis of historical forward prices.• Replication of target options with hub options.

Correlations:• Estimation of historical returns.• Inference from trades and indications.• Something fancier.

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The Usual Approach – Filling the Gaps

By the time the dust settles several “mini-models” have been built.• If well designed these capture realistic price dynamics.

The results are used to modify inputs to Margrabe:• Forwards:• Vols:• Correlation: Modeled or inferred from “related” transaction.

Why do this?• Trade capture: You can book it in your system.• Extrinsic value: The results prescribe your delta hedging regimen.• Volatility hedging: Vegas tell you how to manage vol risk.

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The Usual Approach – Capturing Extrinsic Value

Gamma mirage• 50MW blocks limit your ability to capture the modeled extrinsic value• The plots illustrate 400MW heat rate 9 (!!!) unit for Jul2010.

• Left: Change in delta (MW) versus change in heat rate.• Right: Heat rate history for Jul10 and distribution of 5 day changes.

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The Usual Approach – Vega Hedging

Fixed strike options are of limited value to hedging spread options.• Optionality is in a different “direction.”• Mismatch between size of fixed price and spread changes means costly rebalancings.

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The Usual Approach – Summary

The process:• Morph your asset into a spread option + “corrections.”• Invoke standard option pricing methodology (Margrabe or variants).• Note that the required market data is not available.• Sequence of “mini-models” to fill the gaps.

• At this point you probably had interesting results on price dynamics.• Return to Margrabe with pseudo-market data.

• At this point you have reverted to GBM.• Try to capture modeled value in spite of frictions.

What you lost in the process• Realistic price distributions.• Accurate representations of the “corrections.”

• Asset constraints and hourly flexibility.• The ability to view your asset with other parts of your portfolio.

• Load portfolios.• Effectiveness of structured hedges.

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Alternatives

Thesis ─ What matters

• Including drivers that affect various trades and assets in a portfolio.• Forward prices.

What matters less:• Standard fixed strike options.• Notions of delta hedging in anything approaching a continuous fashion.

Spot Price Simulation Framework• Quasi-stationary weather data sets.• The hard part of the design and calibration of

price relationships.• Hierarchical regressions/simulations increase

tractability.• Each coupling is just a souped up “mini-model.”

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Alternatives

Analysis can be on bucket prices – this is what trades in the markets.• For electricity hourly structure is also “hierarchical”:

You choose your regression approach – e.g.

• Where:

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Alternatives

A sample calibration is shown below for TETM3 versus Henry Hub.• Large events are excluded from the “normal” regression.• How you handle residuals is a modeling choice.• Calibration to forwards by transformation.

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Alternatives

Hierarchical organization allows a broad set of questions to be addressed.

• Simulations show the structure for our sample asset.

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How Good is a Hedge

Asset Finance• Debt financing typically provided by a lending consortium.• Lenders require a hedge to stabilize cash flows.• Term is usually five years – often starting several years forward.

Structured hedges reference a “synthetic asset.”• Your asset will generate a payoff in a given year.• The synthetic asset is simpler than the physical asset• The synthetic payoff is often a minor variation of:

• Hedge effectiveness depends upon: There are two common structured hedges.

• Heat rate call options (HRCOs).• Revenue puts.

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How Good is a Hedge

HRCOs:• Sell in return for a monthly capacity payment C.• If C supports the debt payment and is deemed “close enough" to then the

lender is happy. • Some noteworthy hedge malfunctions have rendered HRCOs currently out of

vogue.

Revenue puts• Pay an option premium in return for a floor on• If K supports the debt payment and is deemed “close enough" to then the

lender is happy.• Comments:

You are long an option (the asset) and buying the put option. There is a perception that revenue puts are safer than HRCOs.

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How Good is a Hedge

Performance of a revenue put also depends upon• Left: Perfect match.• Right: Synthetic asset value exceeds asset value.

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How Good is a Hedge

Hedging the Fairlawn Asset• The following revenue put was analyzed on 15June2015 in conjunction with the

hypothetical asset. June2018-May2019. Strike: $60mm. Printing off of PSEG with basis adders. No emissions constraints. Slightly simplified dispatch constraints.

• Dealer exodus has substantially reduced liquidity in power markets. Harder to achieve Hedges typically reference liquid zones or hubs. Synthetic unit generally simpler.

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How Good is a Hedge

Hedging the Fairlawn Asset - Current Marks• Note: This required that all relevant indices be jointly simulated.• Left: Synthetic payoff versus asset payoff . • Right: Asset and hedged portfolio payoff distributions.

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How Good is a Hedge

Hedging the Fairlawn Asset – Things Can Change• Left: What if the new asset degrades nodal basis?• Right: What if something tectonic occurs and heat rates collapse?

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Punchline

Modeling choice: Salvaging the option valuation paradigm is almost as much work as deploying a cohesive framework.

Gamma mirage: High correlations between power and natural gas prices in tandem with high

volatility decouples single-commodity options from spread options. Power markets are more than just a little incomplete.

Proxy hedges: In a world of reduced liquidity hedge slippage is a primary concern.

Details matter: Assessing the reliability of a hedge requires inclusion of all potentially relevant features of the asset.

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Questions about the presentation or the ERP Program® can also be sent via email to:

Michael Sell - Michael.Sell@GARP.com

OR

Glen Swindle - glenswindle@scovilleriskpartners.com

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