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EUCI Presentation


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Presentation held at an EUCI conference describing how to deal with expected and unexpected uncertainty in the energy markets.

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EUCI Presentation

  1. 1. Managing Physical and Financial Uncertainty By Bjornar Eide Director of Risk Management Sempra Energy Utilities 1
  2. 2. Presentation / Discussion Overview  Framing the issue  Definition of uncertainty  Understanding impact of uncertainty  Structural methods available to quantify impact of uncertainty  Mitigation techniques available to address uncertainty  Strategic approach to “value of hedging” or/and mitigation of uncertainty  Formulation of hedging objective and financial/risk parameters  The importance of the “Cash Flow Base”  Timing and execution/trigger techniques  Hedging philosophy, organizational capability (programmatic, semi-dynamic, dynamic) 2
  3. 3. FRAMING THE ISSUE • Learning how to live with and embrace uncertainty has become an integral part of strategic thinking in the US energy sector over the past 5 years due to the following drivers; • Divergence of business models • State of deregulation / competitive environment • Volatile fuel and regulated markets/environment • Pressure on decreasing foreign energy dependence (physically and financially) • Renewable energy focus • Building business models with sustainable growth potential 3
  4. 4. DEFINITION OF UNCERTAINTY • “The expected and un-expected variability caused by internal and external factors that drive earning and/or rates” – Expected variability refers to normal market events (i.e. change in market prices) quantifiable with standard risk measures at statistical confidence levels (VaR, EaR, CFAR) to portray the probable impact. – Un-expected variability refers to market events (i.e. event of default, price spikes, etc) quantifiable with extreme value theory, stress tests, scenario analysis to describe the potential impact. – Internal factors refers to what can be reasonably controlled through internal governance and pro-activeness in positioning, contractual formulations, etc. – External factors refers to sudden changes in regulatory direction/focus, tax credits, market dynamics, volatility, etc. 4
  5. 5. UNDERSTANDING IMPACT OF UNCERTAINTY • Important to understand the financial impact of expected and un-expected uncertainty on the following; • Business Models / Strategic direction • Earnings • Rates • Major projects • Main Questions – Does our organization / regulated environment have the financial strength to withstand expected uncertainty (probable ) and un-expected uncertainty (potential) ? – How does the risk appetite and financial situation help describe the desired risk philosophy and governance structure of your organization ? 5
  6. 6. Impact of Uncertainty (2) • Potential for adverse outcomes drives how the Risk Philosophy of the organization addresses how to deal with expected and unexpected uncertainty. • Risk adversity/appetite not a goal in itself, but rather a consequence of quantifiable studies, capital allocation and insurance against bad outcome. • Capital markets expectancy for company identity and return generation can help formulate positioning for expected and unexpected uncertainty. • Uncertainty can also lead to higher value if identified and utilized to flexibly respond to unfolding events – real options 6
  7. 7. Cone of Uncertainty Real options View Ownership and Value control of Managerial Options Increase Value Traditional view ___________________________________________ “real options” by Martha Amram and Nalin Kulatilaka Uncertainty 7
  8. 8. Structural methods available to quantify impact of uncertainty • Business Models / Strategic Direction – Constant Financial Benchmarking and scorecards to evaluate strategic flexibility and direction – Identify top strategic dependencies to enhance internal focus – Sustainability testing of Earnings Growth projections • Earnings – Develop robust quantification/standardization of earnings component base through cash flow mapping techniques – Mark to market accounting capability or/and data-ware house capability. – VaR, EaR, CFAR on an incremental basis for each component and across earnings base. 8
  9. 9. Structural Methods (Continued) • Rates – Simulation of rate behavior utilizing Monte Carlo Techniques or/and descriptive inputs (rate matrix) – Identify tolerance band to be managed structurally with the help of standard Risk Measure (VaR, EaR, CFAR) – Monitor or create programs to manage rates • Major projects – Identify project variables – Calculate an actuarial project VaR for top identified variables – Translate project risk measures impact to financial metrics of project – Monitor project VaR 9
  10. 10. Mitigation techniques available to address uncertainty • When choosing potential mitigation techniques for uncertainty it becomes essential to differentiate the approach between expected uncertainty and un-expected uncertainty. • Mitigation techniques for expected uncertainty (probable) can be considered economic hedging and are typically liquidity intensive and measured on the basis of immediate economic impact/effect on the hedged items (delta, hedge ratio, dollar offset). • Mitigation techniques for unexpected uncertainty (potential) can be considered insurance and hedging are typically liquidity constrained and measured on the basis of insurance coverage / guarantees associated with adverse events and therefore will typically not meet the same stringent GAAP hedge effectiveness rules (Fair Value, Cash-Flow Hedge). 10
  11. 11. Strategic approach to “value of hedging” or/and mitigation of uncertainty • Measurement of success an important issue up front in communication with process stakeholders. • Important aspects to consider are as follows; – Is the hedge in place to secure earnings/rates/asset value / project financials where expected risk could results in adverse outcome ? (to protect fair value) – Is the hedge in place to secure earnings/rates/asset value/ project financials where un-expected risk could result in adverse outcome ? (to protect what if scenarios) – Is it expected over time that hedging/timing decisions should be value added/accretive on a stand alone basis ? • Comparison against alternative portfolio benchmarks • What are the expectations for return on capital allocation / liquidity allocation – Does the portfolio contain type of positions (fair value) that if not actively managed will diminish with time or/and can not be realized in the cash market (theta) 11
  12. 12. Formulation of hedging objective and financial/risk parameters The hedging objective should identify the following elements: 1) What are the financial goals of the program (insurance, economic hedge) ? 2) What level of insurance / economic hedge will be provided (minimum, maximum, average guarantees) ? 3) How will compliance with insurance / economic hedge goals be measured (delta, capped, other) ? 4) Will the program cover all types of basis risk (location differences, cash flow expiry etc) ? 5) What are the funding requirements of the program (Max Liquidity, Credit VaR, Budgets etc) 6) What type of program will be put in place (programmatic, semi-dynamic, dynamic) ? 7) What are the triggers for the program (market, weighted cost of rates etc) ? 8) What are the organization execution guarantees put in place to support type of program ? Formulation of a recommended approach should also include a review of the following:  Earnings/rate aspiration in combination with stakeholder expectations (Markets)  Expected Peer Competitiveness  Liquidity Utilization (Max)  Total Potential Cost/Impact of Program 12
  13. 13. Hedging Alternative Quantification Alternative #1 Alternative #2 Alternative #3 Liquidity $ 10.00 $ 15.00 $ 12.00 (Max in Millions) Delta 12.00 11.00 13.00 (Initial in Bcf) Delta 20.00 20.00 20.00 (Max in Bcf) Hedge Program 12.00 12.00 - 15.00 11.00 - 25.00 (Cost in Millions) Rate Level $ 11.45 $ 12.45 $ 10.85 (Stress + 30%) Rate Level $ 7.45 $ 8.45 $ 7.85 (Stress – 30%) All numbers in the table have been randomly generated 13
  14. 14. The importance of the “Cash Flow Base” • Cash flow mapping of linear and non-linear exposure vital for incremental understanding of potential impact of hedge programs to cash flow base. • Cash flow base capability should enable the organization to break down risk measures into the following risk views; A. Periodic components (buckets of time periods) B. Location break down C. Position type break down (linear/non-linear) • Incremental hedges can be assessed by total portfolio impact, impact on option sensitivities and liquidity (fund usage) to understand impact of proposed hedge programs/mitigation techniques. 14
  15. 15. Timing and execution/trigger techniques Triggers of hedging programs or incremental hedge tranches can be broken down into the following categories; a) Market Price Triggers i. Price Level ii. Price level + Risk Factors (95% confidence, 2 standard deviations etc) b) Fundamental Triggers i. Days of carry (natural gas inventories), by season ii. Number of accumulated heating degrees c) Cost/Revenue Triggers i. Portfolio VaR triggers (10day, TeVaR) ii. Weighted cost of rates / IRR, NPV, RAPM iii. % increase in weighted cost or rates / IRR iv. Weighted cost of rate / IRR, NPV, RAPM plus risk factors d) Technical Triggers i. Momentum indicators (200 Day Moving Average, RSI, Stochastic etc) ii. Price support/resistance triggers 15
  16. 16. Hedging Methods (programmatic, semi-dynamic, dynamic) • Hedging methods can be broken into many categories, but what distinguishes the methods is how they are constructed and the formulation of how dynamic the execution and maintenance of the programs are. The following categories of hedge methods have been identified for this presentation; • Programmatic: The design of a transaction structure consisting of physical/financial transactions which once executed pose no surprises and are in accordance with the hedging objective where a predetermined outcome and use of funds has been identified through simulation / risk slides. • Semi-Dynamic: The design of programs where the base of the program is static (constant set of transactions) while incremental parts of the program are determined dynamically by market or other types of triggers (VaR, Weighted Cost of rates etc) • Dynamic: The design of programs where the entire program is dynamic and at the discretion of portfolio manager (s) within certain financial constrains 16
  17. 17. Schematic Overview of Hedging Methods Programmatic: Semi dynamic: Dynamic: Characterized by a Places an initial hedge Leaves the timing and mechanical, predetermined tranche with a progressive composition of hedges at the hedge plan execution based ramp up of the hedge volume discretion of the decision on expected effectiveness; dependent on market signals, maker; focuses on after-the- no ongoing maintenance; probability of price level fact success criteria such as either predictable use of and/or expected weighted hedge ratio, dollar offset and funds/liquidity or predictable costs; some focus on cost RAPM measures; manages total cost to customer (i.e. effectiveness and/or use of use of funds with budget, hedge-and-hold). funds. liquidity and/or VaR limit. Passive Semi Active Active 17
  18. 18. Risk Profile (Programmatic) SUMMARY: Buy calls to cap 55% of volume at a predetermined level ($2.50 out of the No Hedging money) portrayed by the risk diagram to the Storage Only left. Storage + Calls Only Customer Rates PROS: Execution at initiation with no maintenance required. Capped liquidity demand. Very easy to explain – does not depart from extreme event “insurance” concept. CONS: Low delta – possible for customers (today) to see material cost increases with no benefit from hedges. Total max rate not guaranteed. Market Prices No ability to react to changing conditions. This is a programmatic example that provides insurance only. Protection primarily or unexpected uncertainty . 18
  19. 19. Risk Profile (Programmatic with financing to cap 55% ) SUMMARY: The program consists of buying calls of hedge volume at a predetermined level ($2.50 out of the money) financed by ($1.50 out of the money puts) and covered downside of (-$2.25) portrayed by the risk diagram to the left. No Hedging Storage Only Similar to Calls only, but: Storage + Calls Customer Rates + Put financing PRO: Significantly lower premium outlay. In turn, call strikes can be reduced and premiums raised, resulting in higher delta (bill protection at lower price moves). Good approach to compare with peers at most market points except the most suppressed prices. (today) CON: Need to monitor liquidity risk during price declines. Total settlement value of Market Prices derivatives unknown. This is a programmatic example of a hedge that provides both economic and insurance protection for both expected and unexpected uncertainty. 19
  20. 20. Risk Profile – Semi Dynamic (Simulating the Market) Build internal capability or procure external analysis Concept: • Hedging as an integral part of the business for adding value and/or reducing risk • Detailed simulation of the entire market assuming competition on variable cost • Simulation of electricity generation, and regional energy flows in the power market in order to satisfy total demand at minimum costs Model requirements: • Detailed description of electricity system • Updated information on all reservoir levels • Marginal costs for all power stations • Detailed and representative inflow statistics • Detailed information on transmission system, regional load characteristics and variations in demand over time 20
  21. 21. Forecasting fundamental value of Natural Gas prices (principle) $/Mwh Today’s physical equilibrium of natural gas prices (Mmbtu Value) Reflect the range of possible future Price paths and spot prices in the market Time 21
  22. 22. Fundamental market analyses require $/Mwh extensive back-testing (example) Production 74 TWh Predicted range of spot prices depending Actual production 72 220 000 on production alternatives for entire power system 70 200 000 68 66 180 000 64 62 160 000 Average spot 60 price observed Range of possible in the market inflow alternatives 140 000 58 56 120 000 54 52 100 000 22
  23. 23. Summary of Fundamental Approach • Fundamental view of over-value or under-value against the current market (forward curve) through quantitative forecasting methods forms the basis for hedging decisions and physical optimization. • The approach requires rigid structure and data ware-house capability to support calculation methodology (regression, stochastic) and forecasting equations. • Data series needs to be supplied to modeling phase in a very structured fashion. • The approach requires extensive back testing capability of forecasted data against actual results. Semi-Dynamic 23
  24. 24. Risk Profile SUMMARY: The program consists of buying (Semi Dynamic) OTM calls to bring portfolio capped rate to 55%. Premium cost remains below budget. Calls are converted in 2 stages to FPIs when prices exceed preset market price thresholds, and are re-converted to calls when market prices fall below the same No Hedging levels. Strategy (Backstop) Storage Only PRO: Up front coverage with catastrophe Customer Rates insurance at low cost. Deltas increase with Strategy (Expected) rising markets, yet potential settlement losses on FPIs expected to be less than traditional hedge-and-hold strategy. Converted/re-converted call options expected to result in reduced premium costs. Added FPIs can be designed to exceed 55%. (today) CON: Expected net loss on hedge instruments. Potential for Call-FPI to skip Market Prices widely across the designated threshold levels or otherwise to cycle back-and-forth across the level, increasing settlement Protect against both expected and unexpected uncertainty. losses. 24
  25. 25. Risk Profile (Dynamic) The program consists of allocating a progressive VaR (10$ million) to hedging Prices activity whereby the hedging activity can only lead to a positive delta impact of portfolio, but need to guarantee a linear cap at certain level Unrealized gains of executed hedges a additive to allocated VaR (25%) while $12.50 unrealized losses reduces allowable VaR by 75% stopping out ineffective use of funds and capital for hedging purposes. PRO: Market driven and highly visibility $6.50 strategy to value of timing of hedges with the ability to ramp up program driven by timing success. $5.75 CON: Need to monitor VaR and Liquidity parameters within allocated capital. 25
  26. 26. Designing Hedge Programs Step 1 Frame the Hedging Objective Step 2 Research possible alternatives Step 3 Quantify impact of alternatives Step 4 Recommend best fit alternative 26
  27. 27. Hedging Output (Risk Matrix) Delta : 120,000 Mwh Gamma : 30,000 Mwh Theta :: 250,000 USD VaR : 2.7 Mill 27
  28. 28. Hedging Output (Risk Slide) Proprietary position example P&L movement of portfolio with each The overview can be constructed per strategy (Combination of all strategies winter strip) incremental move of prices or as a combined strategy of all books Data exhibited above for illustrative purposes The left hand column shows the financial parameters of the portfolio while any point within the matrix will provide information about the value for each financial parameter given the price level 28
  29. 29. BIBLIOGRAPHY • Bjornar Eide has been a Director of Risk Management for Sempra Energy Utilities since September 2005. Bjornar oversees the risk governance structure for San Diego Gas & Electric and Southern California Gas Company. He is a member of the Risk Management Committee for each of the utilities, which is responsible for managing each of the utility’s exposure to market, credit, liquidity and operational risk. Bjornar has over 13 years of experience from energy markets, serving in a variety of capacities in an international environment. Prior to joining Sempra Energy Utilities, he worked as an independent strategic risk consultant for a variety of clients in Europe and the US focusing on strategic risk management related issues and the design of risk assessment capability. As a Director of Risk Management for NRG (from 2000 – 2002) he built up the risk management department and during his four year tenure with Statoil A/S as a portfolio manager, he actively managed positions that involved petroleum products, crude, natural gas & electricity including the build- up of the power marketing department. Eide holds an MBA in Finance from San Francisco State University and a BA in Business Administration from California Lutheran University. 29