Risk Management at Enron

1
Risk Management at Enron

• Presented by
• Tanya Tamarchenko
• Enron Research Group
• 23 February 2001

2
Overview

• Enron today
• Value-At-Risk Introduction
• Enrons Value-At-Risk Model
• Extensions of Value-at-Risk
• Conclusions

3
Enron Today
? Combination of physical presence in the
energy markets with expertise in financial
products ? A market-maker in energy
derivatives ? Three core businesses - Cash and
physical business - Risk management - Finance
4
Top North American Gas Power Marketers(First
Quarter 2000)
Electricity (BKWH)
Natural Gas (BCFD)
5
Enron Today International Portfolio
In Operation Power Plant Pipeline Other EP L
PG Facility LNG Facility Water Gas
Distribution Electricity Distribution
In Construction Power Plant Pipeline Other LPG
Facility LNG Facility
In Development Power Plant Pipeline Other EP
LPG Facility LNG Facility LNG Vessel Barge
Teesside 1,875 MW
United Kingdom London
Offices Office Locations
Poland Elektrocieplownia Nowa Sarzyna 116 MW
Sutton Bridge 790 MW
Germany Bitterfeld 125 MW
Wessex
Jertovec 240 MW
Italy Milan
Croatia Zagreb
China Beijing
Turkey Trakya 478 MW
Western-Eastern China Natural Gas Pipeline
South Korea Seoul
Palestine Gaza Power Project
Italy Sarlux 551 MW
Wuhan Loop
SK-Enron Gas Distribution LPG Import
EOG Sichuan EP
Sichuan-WuhanNatural Gas Pipeline
Dominican Republic Santo Domingo
Puerto Rico San Juan
LNG Vessel 135 Mc3m
EOG - India 3,200 Bbls/d 38 MMcf/d
Dabhol- Talasari Pipeline
United Arab Emirates Dubai
Wuhan Qingshan 443 MW
EcoEléctrica 507 MW LNG Facility
Puerto Plata 185 MW
Guatemala Guatemala City
Chengdu Logun 284 MW
Guam Anigua
Wuhan Wuchang 189 MW
Abu Dhabi 500,000 Tons LNG Export
Puerto Quetzal 110 MW 124 MW
San Juan Gas LDC
Philippines Manila
India Delhi, Mumbai
Oman 1.6 MM Tons LNG Export
Jamaica Kingston
Piti 80 MW
Trinidad and Tobago EOG 115 MMcfd 3,500 Bbls/d
Hainan Island 160 MW
Subic Bay 116 MW
ProCaribe
Thailand Bangkok
Dabhol LNG Import
IGL
Progasco
Vietnam Hanoi
Batangas 110 MW
Nicaragua Corinto 70MW
Nigeria Lagos StatePower Project90 MW Barge 560
MW CCGT
Venezuela Caracas
First Gas Power Co Fuel Supply 30-35,000 Bbls/d
Malaysia 2.6 MM Tons LNG Export
India Dabhol 2,184 MW Phase I 740 MW Phase II
1,444 MW
Ventane
Colombia Bogotá
Citadel Venezolana
Panama Bahía Las Minas 355 MW Phase I 245
MW Phase II 110 MW
Benin Integrated Gas Power Project 80 MW 20
Miles
Singapore
Bachaquero III
Promigas 1,062 Miles
Accro III IV
Gaspart Northeast 5 LDC
CALIFE LDC
Centragas 357 Miles
Pantanal Energía 480 MW Phase I 150 MW Phase II
III 330 MW
Indonesia Jakarta
Bolivia Santa Cruz
Maputo Iron Steel ProjectIntegrated Steel
SlabManufacturing Facility 3.5 MTPY
Brazil Salvador, Sáo Paulo, Rio de Janeiro
Cuiabá Pipeline 385 Miles
TRANSREDES 3,093 Miles
Riogás/CEG LDC
Bolivia to Brazil Pipeline 1,864 Miles
Pande Gas Project378 Miles
Gaspart South 2 LDC
Elektro Eletricidade e Serviços
Mozambique Maputo
South Africa Johannesburg
Obras Sanitarias de Mendoza
Australia Sydney
Argentina Buenos Aires
TGS 4,104 Miles
Enron projects completed or underway in more
than 30 countries
6
Value-at-Risk for Financial Instruments
Definitions
? Value-at-Risk measures exposure of a portfolio
of financial instruments to potential losses
resulting from - fluctuations of market prices,
interest rates, exchange rates, etc. -
non-performance by counter parties ? Value-at-Ris
k technology has been developed for trading
portfolios. Use of Value-at-Risk for
asset/liability management by non-financial
companies is a subject of debate.
7
Value-at-Risk Definition
? Value-at-Risk - dollar loss that may
be experienced in the value of a portfolio
of financial instruments over a defined time
period with a given probability due to
market fluctuations ? Typically calculated for
one day or two calendar weeks (10 trading
days) ? The time period may be selected to
reflect position liquidation time requirements
8
Value-at-Risk Illustration
9
Value-at-Risk Importance

• ? A requirement for any trading organization or
  a
• major user of derivatives
• ? Internal factors
• A summary of risks for senior management
• and the Board of Directors.
• Tool for setting trading limits and for
• performance measurement.
• ? External factors
• Regulatory agencies, credit rating
• agencies, creditors.

10
Extension of VAR to the Energy Industry

• ? Value-at-Risk developed originally for trading
• portfolios of financial instruments
• ? Extension to portfolios of liquid, energy
  related
• financial instruments is relatively
  straightforward
• ? Complications arise from inclusion of physical
  transactions and/or extension to physical
  operations of a plant or a pipeline
• ? Complications also arise from differences in
  the way energy prices behave versus the way
  equity prices or interest rates behave
• ? Value-at-Risk for equity stakes in private
  companies is also not well defined
• ? Applicability of Value-at-Risk technology
• to non-trading activities is a subject of a
  debate

11
Backtesting
? Losses should exceed Value-at-Risk once in 20
days on average at the 5 level ? Backtesting
is hard for illiquid commodities
12
Backtesting ENRON V_at_R(1/5/98 to 12/30/99)
Summer 1998 Power Price Spike
Hurricane Mitch
1/5/1998
7/5/1998
1/5/1999
7/5/1999
12/5/1999
13
Energy Value-at-Risk Numerical Techniques
? Analytical Mean/Variance Technique ? Simulati
on - Historical - Monte Carlo ? Stress tests
(predefined scenarios) ? Enrons recommendation
- Monte Carlo simulations - supported by
statistical analysis of historical prices -
full re-pricing of entire portfolio in spite of
computational burden
14
Enrons Value-at-Risk Model
Inputs Positions for each commodity (gamma
delta approach currently) Forward price
curve for each commodity (provided by
traders) Volatility curve for each commodity
to be used in simulations Correlations across
commodities as well as across forward contracts
of different maturity Outputs distribution
of returns for each portfolio in the portfolio
hierarchy
ENRON
GAS
POWER
LIQUIDS
15
VAR Simulation Mechanism

• Portfolio holdings (positions) are represented
  through
• Deltas (?i) and Gammas (?i), i1,2, N.
• Obtain new price, Fi,sim, from Monte Carlo
  simulations
• Change in position value for the contract i is
• ?i(Fi,sim - Fi) 0.5?i(Fi,sim - Fi)2
• A number of simulations of Portfolio Value
  Change are calculated
• The 5th percentile is used to obtain VAR.

16
Modules of Enrons Value-at-Risk Model
? US Gas forward contracts on NYMEX
plus hundreds of basis locations quite
developed market prices are liquid up to a
few years forward history of prices
available for more than 5 years
daily highly seasonal prices ? US power
forward contracts for peak and off- peak power
for different US regions developing market,
deregulation started recently prices
behavior changes from one year to the
next highly seasonal prices low liquidity
beyond 12 months forward
17
Modules of Enrons Value-at-Risk Model (continued)
? Other commodities liquids contracts (Crude
Oil, Unleaded Gasoline, Propane, Butane, etc),
Coal, Pulp and Paper, Condensate ? European
markets UK Gas, UK Power, Continental Gas,
Continental Power, Nordic Power ? MG metals
positions ? Intramonth gas positions ?
Intramonth power positions
18
Main Concepts of Enrons VAR Model (continued)

• Simulation of forward prices term-structure
• A portfolio of commodity contracts has exposure
  the
• entire term structure of forward prices
• Reshaping of the forward price curve may
  significantly
• affect the performance of a portfolio
• Modeling of the term structure requires capturing
  the correlations between different prices along
  the forward
• price curve

19
HJM Model (Example PCA results for Crude Oil)
20
Main Concepts of Enrons VAR Model(continued)
? Statistical analysis of historical forward
prices for selected subset of all traded
curves - recent history of forward prices is
used - parameters are estimated for selected
price processes, the data fit is examined -
correlations are captured across term structure
as well as across different commodities -
analysis is repeated regularly
21
Volatility Implied versus Historical

• The critical input which is not directly
  observable
• Volatility can be inferred from the option prices
  or estimated from historical prices
• Historical volatility standard deviation of
  logarithmic price returns
• In (Pt /Pt-1), annualized through multiplication
  by a relevant factor

22
NG Prompt Month Volatilities (Historical and
Implied) from 1/1/95 to 9/12/00)
23
Extensions of Value-At-Risk Model
? Incorporating different price processes -
Brownian motion (GBM) - Extension of GBM for
forward prices (HJM) - Mean-Reversion models
(floor reverting processes) - Jump-Diffusion
model - Combination of Mean-Reversion and
Jump-Diffusion model - Volatility
switching models - Capturing fat tails
24
Choosing Appropriate Price Process
25
Choosing Appropriate Price Process
26
Risk Analytics Component VaR

• VAR decomposition/dissection by
• time-buckets
• traders
• sub-portfolios
• Useful in identifying risk-contributors and
  hedges in a complex portfolio.

27
Component VaR- Desk
28
ConclusionsChallenges in Implementing Effective
Risk Management System in an Energy Firm

• Calibration of the model choosing appropriate
  price processes and their parameters estimation,
  volatility specification
• Capturing price patterns of energy prices
  seasonality, gapping behavior
• Capturing extreme events, the tails of
  distributions, non-normal returns
• From risk measurement to risk management -
  making risk modeling system an active tool in
  portfolio management process