Challenges in Capital Adequacy
UH-GEMI 3rd Annual Energy Trading & Marketing
Conference: Rebuilding the Business
Houston, Texas
January 20, 2005
Laurie Brooks
VP Risk Management and Chief Risk Officer
Public Service Enterprise Group
UNIVERSITY of HOUSTON
Global Energy Management Institute
Capital Adequacy and Capital Allocation
Connected?
• Capital Adequacy
– How much capital is required to achieve
the company’s stated goals and
objectives?
• Capital Allocation
– How should corporations allocate capital
between competing demands?
2
Capital Adequacy for Energy Transactors
1. Capital for what?
Business models: regulated utilities, merchant generators, marketing and trading entities
Economic capital vs liquidity adequacy
Banking models
S&P liquidity survey
Measures - EaR vs CFaR, role of stress testing, market risk vs credit risk trade-offs, role of
ECE and PFE
2. Why energy is different - impact of following on margin/cash requirements:
volatilities
sector ratings
storability
regulatory intervention
age and depth of markets
contract terms
risk mgt tool availability
3. Capital how?
Access to capital markets
Diversification of cash flows
Credit mitigations
role of netting and clearing
stair stepped margining agts.
3
Capital Use by Activity
Utility
Merchant
Generator
Marketer/
Trader
Assets
Pipes & Wires,
Customers
Generating
Facilities
People, IT
Protection
Insurance
Insurance
Insurance, VaR
Plant
Cash collateral
Maintenance Plant, customer
satisfaction
Growth
Acquisition of
service territories
New facilities
New products,
services, markets
Multiple
Venture capital
Venture capital
Venture capital
4
Market Risk – Trading vs. Non-Trading Activities
Non-Trading
Trading
Purpose
•
•
•
Positions generated by asset/customer
business
Strategic “buy and hold” hedges
Liquid, actively funded positions across
many markets
Holding period measured in days/weeks
•
•
Illiquid or “buy and hold” positions
Holding period measured in months/years
Price-driven exchange traded or OTC
options
Short holding period allows linear
approximations
•
•
Asset/customer-driven embedded options
Long holding period makes non-linearity
material
•
•
Short-term volatilities and correlation
Jump diffusion, intra-day VaR –
analytical, simulation
•
•
Long-term volatilities and correlation
Mean reversion, seasonality simulation,
Earnings at Risk
•
VaR limit reduction, stop loss limits,
hedging with traded instruments
•
Structured solutions, contract
renegotiations, asset sales and purchases
Management of regulatory process
•
Liquidity
•
•
Optionality
Valuation
Risk Management/
Intervention
•
•
Positions to facilitate marketing
Proprietary trading positions
•
5
Key Concepts of Capital Adequacy: Three
Risk Types
The framework for determining capital adequacy for economic value
requires an estimation of economic capital and thus quantifying the
following significant risks:
•
Market Risk - Variation of portfolio market value due to a change in a market
price or rate, as well as a change in energy demand
•
Credit Risk - Variation of portfolio market value due to default or a credit
downgrade of an issuer or counterparty
•
Operative Risk (term to address Operations and Operational risk collectively)
– Operations - The risk associated with delivering or producing physical
energy
– Operational - The risk of direct or indirect loss resulting from inadequate or
failed internal processes, people, and systems or from external events
6
Key Concepts of Economic Capital
Adequacy: Market Risk
Modeling
Approaches
Price Behavior
Process
Market
Exposures
Pros/Cons
Comments
Analytical
Closed-form
approach for
modeling price
movements
Works well for linear Pros:
type exposures
• Simple and fast
• Easy to change as assumptions
change
Cons:
• Does not capture optionality well
• Minimal ability to model complexities
over a longer period of time
• Works well for determining
shorter-term price moves
for a trading portfolio
• Can be used as a quick
metric to help manage
portfolio positions
Simulation
Robust
methodology for
mean reversion,
jumps, linking, spot,
and forward prices
Full revaluation at
each price iteration
better approximates
nonlinearity of
asset/option
positions
• As the time horizon is
extended and the need to
model certain energy price
characteristics increases,
simulation becomes a
more suitable solution.
Meanwhile, the technical
difficulties increase and the
model needs to be
modified to fit the long-term
simulation purpose.
Pros:
• Robust
• Captures optionality
• Provides a full distribution of
outcomes
Cons:
• Complex to construct the simulation
model
• Only as good as model input
parameters
• For historical simulation, values are
constrained to conform to history
which may be irrelevant due to
market, economic, or regulatory
changes
7
Key Concepts of Economic Capital
Adequacy: Credit Risk
Expected Loss
– Represents the average loss that a company could expect to incur over a
given horizon
Unexpected Loss
– Measures the uncertainty of losses around the expected loss
Probability
Portfolio Expected
Loss (Mean)
Expected Loss
(Loss Provisions)
Credit Economic Capital
(Unexpected Loss)
Distribution of Portfolio Credit Losses
Over a One-Year Time Horizon
8
Confidence
Level
CA Framework – Key Concepts
Key Concepts of Economic Capital
Adequacy: Operative Risk – Scorecard
Scorecard Approach
•
Can be used for operations and operational risk to identify risks, determine
frequency and range of costs, and assesses the effectiveness of controls and
mitigation techniques in place. It is subjective, but now that the SEC has
mandated the COSO framework for Sarbanes Oxley 404 compliance,
standards will be set. In particular, the Capability Maturity Model can be
adapted to set standards for a scorecard approach and is already used by
many audit firms. Additionally, a company may want to use CCRO Best
Practices from earlier white papers as a qualitative assessment of where
companies stand with regard to CCRO recommendations.
•
Regardless of the scorecard criteria used, a scorecard approach can form the
basis for continuous improvement processes for internal controls to mitigate
operative risk. It can also reflect improvement in the risk-control environment in
reducing the severity and frequency of future losses.
9
CA Framework – Key Concepts
Key Concepts of Economic Capital
Adequacy:
Operative Risk – Risk Taxonomy
•
The risk taxonomy is a system for organizing types of operative risks
by serving as a family tree, aggregating risks by various characteristics.
The level of aggregation at which each characteristic presents itself
may be determined individually.
•
There is no standardized risk taxonomy, but certain characteristics
should be used to create the groupings:
– Risk classes (people, processes, systems, asset damages) – the broadest
classes of risks
– Subcategories – could include whether the risk is internal or external, a
type of fraud, or a natural disaster
– Risk activity examples – specific activities or events that could cause a
loss, such as rogue trading, hurricane, model risk, or pipeline rupture.
10
Key Concepts of Liquidity Adequacy
•
•
Fixed Payments - This would include, but is not limited to; fixed charges such
as debt service, dividends, debt/equity retirement and current portion of
committed, maintenance and non-discretionary capital expenditures.
Contingent Liquidity – Contingent liquidity is synonymous with unexpected
change or variation in liquidity. While economic capital protects against losses
in the company’s economic value, contingent liquidity is held to support the risk
of unexpected reduction in cash. Includes:
– Cash Flow at Risk
– Trigger events:
• Downgrade event
– Loss of threshold
– Adequate assurance
• Debt/equity trigger
– Contingency events:
• Operational/Operations Risk
• Credit/counterparty termination default
11
CA Framework – Key Concepts
Key Concepts – Combined Capital
Methodology
Description
Advantages
Disadvantages
Assumption
Simple Sum
Derive economic
capital for credit,
market, and operative
risk, then sum them
• Easy to implement
• Most conservative
view of risk
• Overestimates risk
Correlation assumed
• Results in the lowest to be perfect among
level of capital
risk components
adequacy
Modern Portfolio
Theory
From historical data,
determine an explicit
correlation among
credit, market, and
operative risk
economic capital
Attempts to represent
the actual correlation
among risks, rather
than a conservative
assumption
Requires a time series
of credit, market, and
operative risk
economic capital that
is reasonably robust
Assumes that some
risks are uncorrelated,
allowing for lower risk
and improved capital
adequacy
Monte Carlo
Simulation
Using consistent
parameters, simulate
risk factors to produce
a joint distribution of
outcomes
The most robust
perspective of risks
and their interaction if
modeled correctly
• Requires a large
amount of research,
analytical, and
technical resources
• Ensuring
assumptions are
correct is critical
Material risk inputs can
be parameterized
accurately
12
CA Framework – Key Concepts
Key Concepts – Correlation Math Refresher
In a two asset portfolio with equal investment in assets A and B, the VaR of the
portfolio (at 95% confidence) VaRA+B = 1.65 * AB where AB is the standard
deviation of returns of the portfolio:
 AB   A2  2  AB A B   B2
Remember (a+b)2 =a2+2ab+b2 and
Then if AB =1  AB 
where AB is the correlation between A&B
(do the returns move together?)
( a  b) 2  a  b
( A   B ) 2   A   B
So Portfolio VaR = VaRA + VaRB!
2
2
If AB=0,  AB   A   B (Square root sum of squares)
The truth 0 < AB < 1 lies somewhere in between and:
 A2   B2
< AB
Square root sum of squares
13
< A+B
Simple Sum
Example
The Risk Management team at PSEG demonstrated the
CCRO’s framework using a sample asset portfolio.
•
•
•
This example illustrates how the CCRO framework can be used in
practice
We will walk you through the following implementation steps:
– Portfolio setup
– Methodology
– Pre-simulation
– Simulation
– Results
We will also discuss some of the firm and systems resources required
Please refer to pages 61-67 of the white paper for a
full description of the example.
14
Example – Setup
We chose to model the asset-level impacts over a year
•
•
•
of different risks on a company over time.
We modeled market, credit and operative risks jointly in one simulation versus separately
– Felt there was better intuition and that we could better justify a choice of the
assumptions
– Calculation process seemed clear based on this approach
– Used a 1-year holding period and ran 5,000 trials with a 95% CI
We modeled a five-year time horizon, with price changes modeled as follows:
– Year 1: spot
– Year 2-5: forward prices
We chose a variety of assets and parameters.
– Three different generating assets and fuel types
– Assets are in three different pools
Generating Plant
Gas-fired combined
cycle
Coal-fired, base
load
Jet kero-fired
peaking
Power Pool
Capacity
VOM
Heat Rate
Fuel Type
Book Value
ECAR
850
3.98
7.25
Natural Gas
$510,448,931
NEPool
375
2.51
10.3
Coal
$49,720,351
PJM
500
34.48
15.7
Jet Kero
$11,094,684
15
Example – Setup
Market Risk Calculations
• Unhedged market risk
– Minimum [(realized generation over 12 months) + (Expected
generation value of the remaining term)] – (Initial expected
value of the generation)
• Hedged market risk
– (Unhedged market risk) + (Realized and unrealized trading
profit or loss)
16
Example – Setup
Credit Risk Calculations
Counterparty A
Counterparty B
CCC
BBB
1-Year Probability
of Default
27.87%
0.34%
Counterparty C
BB
1.16%
Counterparty
Rating
Commodity
Fuel – coal, natural gas, jet kero
Power – NEPool, PJM, Cinergy
Fuel and power
• Calculated as the sum of credit loss across the twelve months of
simulations, as a function of counterparty risk and power pool risk
• The company has three counterparties
–
–
–
–
Counterparty A is used for fuel procurement
Counterparty B is used for power sales
Counterparty C is used for speculative trading.
The recovery rate is assumed to be 10%.
• Each power pool has collateral requirements that are a function of
the company’s credit rating, tangible net worth and activity in the
pool
– Value is calculated under two potential ratings, BBB (credit limit
$80,000,000) and BB (credit limit $4,000,000)
17
Example – Setup
Operative Risk Calculations
• Operations loss
– Sum of lost profit from plants not running at full capacity
• Operational loss (if applicable)
– Hidden trade on the books whose value is set to the largest
negative value of all the trading positions on the book.
18
Example – Setup
Liquidity calculations
Liquidity risk is defined as the minimum cash flow point in a simulation.
 Prior month realized P/L (retained
earnings)
 Current month generation P/L
 Collateral posted
 Accounts receivable
 Accounts payable
 Full margin on mark-to-market
 Credit loss
 Operations loss
 Operational loss
Monthly
cash flow
19
Example – Setup
Hedging affects liquidity in offsetting
ways.
• Liquidity risk is increased by hedging in the following ways
– Creates cash outflows due to full margining on mark-to-market
– Creates the possibility of credit loss
• Liquidity risk is decreased by hedging in the following ways
– Decreases the amount of cash needed to be posted to power pools
since that is determined by net activity.
– Decreases the distribution of realized P/L from generation
The net effect of hedging was a decrease in the liquidity risk.
20
Example – Methodology
Three key methodology choices drive our model
Method
Risk modeling
Energy forward
prices
Daily power prices
Pros
Joint simulation of
• Consistency
credit, market, and
• More data available to check
operative risks (versus micro relationships rather than
assumed correlations)
portfolio relationship
• Can change micro assumption
and rerun
• Are not assuming answer
Correlated Brownian
Motion for Energy
Forward Prices
Cons
• Increases memory need and
computer time
• Necessitates more simplifying
assumptions, leading to less
accurate estimates of
component risks
• Most practical method with 3
• Easier to believe for forward
power pools and 3 types of fuel
prices rather than spot prices
for 5 years
still oversimplifies reality
• Would be difficult to jointly
• Probably overstates volatility for
calibrate more complex model for
longer-dated contracts
diversity and tenure of portfolio
Daily power prices are • Allows for analytical
normally distributed
determination of MWs of
with mean equal to
generation and generation value
forward price and
• No need to do daily simulation
standard deviation
equal to historical
daily spot standard
deviation
21
• Ignores operating constraints
on plants
• Splitting monthly prices into two
normal distributions (normal
and extreme days) captures
peaking value more accurately
• Does not allow for fuels to vary
by day
Example – Pre-Simulation
Pre-Simulation: prior to running our simulations,
we calculated a number of initial values.
Pre-Simulation Calculations
• Initial expected value of the assets
– Calculated based on the current forward prices for fuels and power
• Expected fuel purchases and expected output to be sold to counterparties
– Calculated based on current forward prices
• Randomly-generated positions in power and fuels
– Constrained to be a quarter of the size of outright positions
– Used to simulate a speculative trading operation
22
Example – Simulation
Simulation: we generated the inputs to credit and
operational performance.
Market risk
simulation*
Correlated
forward prices
- power
Correlated
forward
prices - fuel
Generation
model
Marginal cost
of fuel (VOM
& heat rate)
MTM - A/R A/P on trading
contracts
Credit risk
simulation**
Market
risk
Credit
excess/loss
Probability
of default
Operational
profit/loss
Probability
of outage
Operative risk
simulation**
Probability
of trader
misconduct
* 60 product months x 6 products x 12 monthly steps of random standard normal pulls
** 7 risks x 12 monthly steps of uniform random variables pulled
23
Example – Results
Results – Unhedged vs. Hedged Assets
Unhedged
Available vs. Required Capital ($ millions)
BBB Rated
BB Rated
Available Capital
Debt
Required Economical Capital
Market Risk
Credit Risk
Operative Risk
Diversification Effect - Across Risks
Total Required Economic Capital
571
286
571
286
23
0
22
-11
35
23
0
22
-11
35
Economic Capital Adequacy
251
251
Sources of Liquidity
Fixed Payments
Contingent Liquidity
600
200
27
400
200
27
Liquidity Capital Adequacy
373
173
Note: the simulation was also run with all counterparties set
at BBB to reflect the average rating of many portfolios. The
credit risk remained at zero with a 95% confidence level, while
market risk was reduced from $23 million to $6 million.
By hedging assets, market risk is
reduced by less than the
additional economic capital
required for credit risk, increasing
economic capital adequacy.
Hedged
Available vs. Required Capital ($ millions)
BBB Rated
BB Rated
Available Capital
Debt
Required Economical Capital
Market Risk
Credit Risk
Operative Risk
Diversification Effect - Across Risks
Total Required Economic Capital
571
286
571
286
6
16
22
-13
30
6
16
22
-13
30
Economic Capital Adequacy
255
255
Sources of Liquidity
Fixed Payments
Contingent Liquidity
600
200
0
400
200
7
Liquidity Capital Adequacy
400
193
24
Example – Results
Results – Portfolio Effect
Illustration of the mathematical fact:EC = 0 (square root sum of squares) < EC <  < 1 (Monte Carlo simulation) < EC=1 (simple sum)
Available vs. Required Capital
($ millions)
Sq. Root
Sum of Squares
Net Assets - Debt
Monte Carlo
Simulation
Simple Sum
285.6
285.6
285.6
Market Risk
22.5
22.5
22.5
Credit Risk
0.0
0.0
0.0
23.2
23.2
23.2
-13.4
-11.8
0.0
32.3
33.9
45.7
253.3
251.7
239.9
Required Economical Capital
Operative Risk
Diversification Effect - Across Risks
Total Required Economic Capital
Economic Capital Adequacy
Disclaimer: the
closeness of the
Monte Carlo
(MC) and Square
Root Sum of
Squares is not
representative.
In general, one
shouldn’t assume
that SRSS is a
good proxy for
MC.
Available vs. Required Capital
($ millions)
By analyzing capital requirements for
unhedged assets as part of a portfolio
vs. individually, the example illustrates
how diversification reduces the
economic capital required for market
and operative risks.
Total Individual
Peaking
Assets Total Portfolio
Diversified
Component
Risk
Coal
CombinedCycle
Net Assets
49.7
510.4
11.1
571.3
571.3
Debt
24.9
255.2
5.5
285.6
285.6
Market Risk
7.0
27.6
3.5
38.1
22.5
-15.7
Credit Risk
0.0
0.0
0.0
0.0
0.0
0.0
22.3
3.4
2.3
27.9
23.2
-4.7
-11.1
-2.9
-1.6
-15.6
-11.8
3.8
18.2
28.1
4.1
50.5
33.9
-16.5
6.6
227.1
1.4
235.2
251.7
Required Economical Capital
Operative Risk
Diversification Effect - Across Risks
Total Required Economic Capital
Economic Capital Adequacy
25
Example – Results
Why Emerging Practices?
•
•
•
These are recommendations for internal use and experimentation for companies
to better understand and quantify the capital and cash requirements of the
merchant energy business; these are not recommendations for external
communication or new disclosure.
No one is going to implement all of these recommendations over night.
Most of us have some capability to begin looking at the components of Capital
Adequacy and liquidity requirements through the use of tools that we already
have in place but which require extension and modification to achieve the more
sophisticated views that result from the white paper recommendations. This
should be a controlled evolutionary process - in most cases, the less
sophisticated tools that we already have in place generate more conservative
answers than the sophisticated approaches do.
Why we will implement these ideas over time:
• Better than what we have now
• Emphasize need to look both long term and short and to look at cash flow as
well as earnings and value
• Ideas and methodologies useful in decision making
26