portfolio management: an electric retailer's perspective

1Choosing the Right Energy Supplier for Your Business

Portfolio Management: An Electric Retailer’s Perspective

Eric MeerdinkDirector, Structuring & Analytics

Hess Energy Marketing

Best Practices in Portfolio ManagementApril 4-5, 2013


Background on Hess Energy Marketing


One of the Largest Energy Suppliers on the East Coast


Hess Energy: Robust Product Suite

Fuel Oil

Delivery to Commercial &

Industrial customers

Distributor sales from Hess terminals

110K BPD

Natural Gas

Marketing to Commercial &


Wholesale to LDCs

1.5 BCF/day

Electricity

Marketing to Commercial &


4,500 MWs/hr (RTC ) (enough

electricity to power 4 million

average homes)

#2 electric marketer on the

east coast

Green Suite

Reducing electric usage during times

of peak demand with Demand

Response programs

Support renewable energy sources,

such as wind, solar, biomass and hydropower

Balance your carbon impact from oil and natural gas with carbon offsets

Solutions

Providing turnkey solutions for a wide

range of energy projects

Products include fuel conversions,

energy efficiencies, asset optimization

and more

Energy supply and project funding offer

a comprehensive package


Bayonne Energy Center

• 500 MW natural gas powered electric generating station serving New York City.

• Jointly owned by Hess Corporation and ArcLight Capital Partners.

• Located in Bayonne NJ and supplying power through a 6.5 mile 345 kV submarine cable under NYC harbor to Con Ed’s Gowanus substation in Brooklyn.

• Began operation in June 2012.

• 8 Rolls Royce Trent 60 turbines.

• Direct connection to Transco natural gas pipeline.

• Duel fuel plant that can run on ULSD.


Hess Solar Energy

• Hess Solar Field.

• Began operation in summer 2012.

• 1.1MW of renewable energy located in Woodbridge, NJ on 6 acres of land adjacent to our NJ headquarters.

• Supplies 22% of our building’s electricity requirements.

• Annual environmental impact is equivalent to 250 cars off the road.


Portfolio Management


Hess Energy Marketing Risks

• Retail load contracts (MW, et al) - Bilateral contracts between supplier/end user

• Hess is subject to four types of risk in serving our retail and wholesale load:

○ Market Price Risk

■ Positive/negative price exposure

○ Volumetric Risk

■ Longer term positive demand/price correlation risk

■ Under Collection of Fixed Costs

○ Shaping Risk

■ Short term positive demand/price correlation risk

○ Migration risk

■ Wholesale customers


What is the Appropriate Risk Measure?

• Why do firm’s hedge?○ To lock-in margins (all or part) and reduce earnings volatility

• What is risk in an MW portfolio? • How do we measure it?• What are the costs of financial distress?• Need a quantitative measure to evaluate risk and hedge structures• A quantitative measure needs to:

○ Realistically model price and customer load behavior (i.e. Volumetric/Shaping)

○ Fit the structure of the business and industry

○ Accurately model contractual terms• Propose that Gross Margin at Risk (GM@R) and stress testing are the appropriate

risk measures for a retail/wholesale electricity portfolio.


GM@R Superior to VAR

VAR GM@R

Measure Probabilistic Probabilistic

Forward Market Prices Yes Yes

Holding Period 1 day to 2 weeks Through delivery

Spot Market Prices No Yes

Volumetric No Yes

Delivered Quantity Known and fixedEquals contract quantity

Probabilistic and correlated with market prices

Liquidity Market assumed to be liquid and continuous

Retail market not liquid or continuous

Calculation Methods Historic, Delta, Closed-Form, Simulation

Simulation

Appropriate Uses Forwards, Futures, Options (Trading)

Retail electricity contracts


Stress Testing

• GM@R is only as good as our ability to MODEL price-load behavior

• Historic data on the price-load relationship at the hourly level captures the major driver of load and price volatility (i.e. Weather correlation)

• Other drivers exist that can have significant impacts on cash flows, but may have either a low probability of occurring or are difficult to model

○ Economy – Recession/technology/regulatory change during contract period? Load impact?

○ Extreme Events (e.g. Sandy, winter storms in the NE)

• Any hedging plan needs to incorporate STRESS TESTING as a basic tool to judge the effectiveness of the hedge structure and to look for holes in the portfolio


Simple Risk “Profile”

What we know:

1. MTM2. 2 Stress Events3. Daily VAR

MTMStressTest I

StressTest II

BudgetBusiness Plan

(illustrative)

-$10.0 -$5.0 $0.0 $5.0 $10.0 $15.0 $20.0 $25.0 $30.0 $35.0 $40.0 $45.0 $50.0 $60.0 $65.0 PNL


What we gain:

1. PnL Distribution

2. GM@R

3. Swing Risk

4. Better understanding of stress events.

5. Understand the impact of hedge policies.

Ideal Risk Profile

Xth

Percentile

GM@R

Swing Risk

StressTest I

StressTest II

MTM Budget

Probability ofachieving business plan

(illustrative)

-$10.0 -$5.0 $0.0 $5.0 $15.0 $20.0 $25.0 $30.0 $35.0 $45.0 $50.0 $60.0 $65.0 PNL


Volumetric Risk: Swing


Long-Run Correlation Between Price and Load

12-Month Rolling Average of Load and Price in PSE&G Zone

$0.00

$10.00

$20.00

$30.00

$40.00

$50.00

$60.00

$70.00

$80.00

$90.00

4,700

4,800

4,900

5,000

5,100

5,200

5,300

5,400

5,500D

ec-0

4

Mar

-05

Jun-

05

Sep-

05

Dec

-05

Mar

-06

Jun-

06

Sep-

06

Dec

-06

Mar

-07

Jun-

07

Sep-

07

Dec

-07

Mar

-08

Jun-

08

Sep-

08

Dec

-08

Mar

-09

Jun-

09

Sep-

09

Dec

-09

Mar

-10

Jun-

10

Sep-

10

Dec

-10

Mar

-11

Jun-

11

Sep-

11

Dec

-11

Mar

-12

Jun-

12

Sep-

12

Dec

-12

RT

C

LM

P

$/M

WH

Ave

rag

e M

W

Average MW $/MWH RTC


Short-Run Correlation Between Price and Load

$0.00

$20.00

$40.00

$60.00

$80.00

$100.00

$120.00

$140.00

$160.00

$180.00

$200.00

07/12/10 07/13/10 07/14/10 07/15/10 07/16/10 07/17/10

$/M

WH

0

2,000

4,000

6,000

8,000

10,000

12,000

MW

Hourly Load and Price in PSE&G Zone 7/12/10 to 7/17/10

Load (MW)

Price ($/MWH)


Short Retail Sale and Long Hedge

Long Hedge“Delta Hedge”

Short Sale

$/MWH

Short Retail Sale

-

$

Net: Swing Risk “Gamma”

+


Simulated Swing (Gamma) Position

($1,600,000)

($1,400,000)

($1,200,000)

($1,000,000)

($800,000)

($600,000)

($400,000)

($200,000)

$0

$200,000

$400,000

$0 $50 $100 $150 $200 $250 $300 $350

Average On-Peak LMP

To

tal P

&L

Example uses NJ BGS CIEP Load for July.Approximately 80 MWs average load on-peak.


3020100-10-20-30-40-50-60

0.04

0.03

0.02

0.01

0.00

Cash Flow

De

nsi

ty

Distribution: Swing Risk

19

Negative Skew:Swing Risk

Swing Cost

Excluding the expected covariance produces a distributionwith a negative expected value.

Mean


P

Cha

nge

in P

&L

+

-

gamma

HedgeHow do we create this hedge?

Monthly Average

Price $/mwh

P

Short Gamma Hedge


Creating a Gamma Position from Options

P

Use vanilla calls and puts to construct the gamma position.

Cha

nge

in P

&L

+

-

Monthly Average

Price $/mwh

P ˆ


-$2,000

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

$18,000

$20,000

$0.00 $20.00 $40.00 $60.00 $80.00 $100.00 $120.00 $140.00

Market Price

Ch

an

ge

in P

&L

($

00

0)

-Gamma

Estimate

Cost as of February 9, 2009.

Estimated gamma function for July 2010 PSE&G FP load.The option cost equals $1.89/MWH per MWH served.

Example of a Theoretical Gamma Function Estimate


Reduce Cash Flow at Risk

3020100-10-20-30-40-50

0.06

0.05

0.04

0.03

0.02

0.01

0.00

X

Density

Accountnig or Actuarial w ith Options

Accounting Model

Cash Flow

Swing RiskReduced

Delta Hedged

Hedged withOptions


Hedging in Practice

• In practice we cannot charge the theoretical cost of swing○ Too expensive ○ Uncompetitive

• In practice we cannot carry out the theoretical hedge○ Impractical○ Illiquid market

• Our practice for hedging swing risk:○ Calculate the GM@R. Decide on a GM@R percentage (5%)○ Goal is not complete elimination of risk but rather to reduce the GM@R by X%. (50%)○ Experiment with various hedge structures to meet the GM@R goal○ How? Combine options available in the market to achieve our goal at the least cost.

This could involve multiple portfolios■ Monthly Option (straddles and strangles) (Swing Risk)■ Daily Options (straddles and strangles) (Hourly Shaping Risk and Swing Risk)■ Spread Options■ Weather Derivatives

○ Present to management preferred plan/portfolio to reduce risk and the cost per MWH to achieve that reduction

○ Balance the risk-reward tradeoff


Volumetric Risk: Under Collection


Fixed Cost Hedge Example

CDD- CDDbFR L - LFR Collection Over/Under

LFR Collection

LFR FC

nCalculatio Rate Fixed L

FC FR

Customer to Charged Rate Fixed FR

L

ActualDays Degree CoolingCDD

(Expected) Normal Days Degree CoolingCDD

Usage Actual L Usage, Expected L

Costs Fixed Total FC

NANA

A

N

N

A

A

N

AN

NN

A

CDDbaL

CDDba

This looks just like thepayoff of a CDD Swap.

Linear in CDD

The under/over collection is a function of the deviation of weather from expected.We are naturally long CDD.


Weather Derivative Payoffs

• Weather Swaps. A weather swap is a financially settled derivative written on CDDs and HDDs.

• Settles monthly on actual temperatures. The strike price is a temperature value such as the climatic normal or a temperature forecast (determined by market).

• The swap is converted into dollars using a fixed multiplier known as a tick. One contract has a tick size of $20, but this can be negotiated. Most swaps have a limited payout and loss (caps). Below is an example payoff for a short CDD swap.

• A weather option is a financially settled option written on CDDs and HDDs. They settle monthly (or daily). Monthly options can be European or look back. Daily options are struck like daily power options.

CDD -K tick Payoff

CDD,0 -K maxtick Payoff

tick = FR x b


Model Outline

0

100

200

300

400

500

600

0 20 40 60 80 100

Average Daily Temperature

Av

era

ge

DA

ily M

W

0

100

200

300

400

500

600

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Cummulative %

July

CD

D

(30,000)

(20,000)

(10,000)

0

10,000

20,000

30,000

40,000

200 250 300 350 400 450 500 550

CDD

MW

H D

evia

tio

n f

rom

No

rmal

y = 2075.6x - 821313

R2 = 0.9982

($300,000)

($200,000)

($100,000)

$0

$100,000

$200,000

$300,000

$400,000

200 250 300 350 400 450 500 550

CDD

Ove

r/U

nd

er C

olle

ctio

n

Load –Temperature Model CDD Distribution

MWH Deviation from Normal

Over/Under Collection


Swap Hedge

CDD$0

Net Collection offixed costs

CDD Swap w/ cap and floor

Pa

yoff

+

-

Net


Generation/Tolling


Generation is a Natural Hedge for Retail Load

• Generation - Natural hedge for retail load and visa versa

• High/low market prices

○ Increase/decrease generation profits

○ Retail margins decrease/increase

• Generation attributes other than energy that provide cash flow to retailers

○ Capacity

○ Ancillary services


Generation is a Natural Hedge for Retail Load

• Physical generation also has an impact on collateral requirements

○ Retail LSE’s pay collateral to the ISO

○ Generator receives payments from the ISO

○ An LSE with generation assets can reduce this collateral obligation, i.e. Lower cost to serve

• Physical generation assets

○ Owned or tolled

○ Tolling has all the benefits, including collateral, without the operational requirements of running a plant

○ Financial options written on a physical plant do not have the benefits of a physical toll. No operational risk. Simple.


Benefit of generation as a hedge for load

Reduction in GM@R

Net distribution of PNL has a lowerupper tail on PNL, but has a reducedvariance and a lower GM@R.

This value in reduced GM@R andhedging of swing costs can be calculatedand used in pricing.

Matching Load and GenerationReduction in GM@R

-$10.0 $0.0 $10.0 $20.0 $30.0 $40.0 $50.0 $60.0 $70.0 $80.0 PNL


Appendix


Theoretical Model

• It has been shown that a static hedge of plain vanilla options and forwards can be used to replicate any European derivative (Carr and Chou 2002, Carr and Madan 2001).

• Any twice continuously differentiable payoff function, , of the terminal price S can be written as:

• Our payoff function is the terminal profit. It can be decomposed into a static position in the day 1 P&L, initially costless forward contracts, and a continuum of out-of-the-money options. F0 is the initial forward price.

)(Sf

0

0

0000

F

FdKKSKfdKSKKfFSFfFfSf

InitialP&L

DeltaPosition Gamma Hedge: “Swing Risk”


Theoretical Model, Cont.

• The initial value of the payoff must be the cost of the replicating portfolio.

• Where P(K,T) and C(K,T) are the initial values of out-of-the-money puts and calls respectively.

• Interpretation of term within the integral: Second derivative of the payoff function representing the quantity of options bought or sold.

○ R = Fixed revenue rate○ SF = Shaping Factor○ L(S) = MWH, function of S (spot price of power)○ (1+SF)xL(S) = Cost to serve load

0

0,,

0000

F

FrT dKTKCKfdKTKPKfeFfFV

Margin1 SLSSFRSf

SLSFKf

12


Solving for the Estimated Gamma Function

• Select a series of strikes, Ki , and quantities, , to create a portfolio of puts and calls.

• To estimate the gamma function we need to choose the amount of options for each strike, , so as to minimize the distance between the estimated gamma function and the true gamma function.

• Estimated gamma function equals:

• Choose the optimal quantities by minimizing the sum of the squared errors between the true and estimated gamma function over a set of Q prices.

i

i

i

M

iii

N

ii PKMaxKPMaxP

11

0,0,ˆ

2

1

ˆmin

Q

jjj PP

portfolio management: an electric retailer's perspective

Business

right energy supplier

hess energy marketingchoosing

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bayonne energy center

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types of risk

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