PORTFOLIO MANAGEMENT
combinations; the best alternatives are a set of portfolios, called Efficient Frontier, that offer the maximum possible return for a given level of risk. An efficient frontier portfolio is one where no added diversification can lower portfolio risk for a given return expectation. Alternately, no additional expected return can be gained without increasing portfolio risk. The estimated risk of efficient frontier portfolios is called ‘systematic’ or ‘market’ risk that cannot be reduced through diversification. After estimating the return/risk situation of an
existing portfolio, managers search for changes to improve their position. They often explore many combinations of assets (i.e. portfolios) that fall inside the green quadrant before they select an alternative that meets corporate requirements. Due to many factors, including non-linear conditions and complicated inter-commodity and inter- temporal relationships, closed form analytical solutions are not adequate to find optimum portfolio alternatives. Full simulation is required to develop an acceptable number of realistic scenarios and to assess risks and returns of portfolio alternatives. This process is quite cumbersome and has been too challenging to be practical since the inception of MPT back in the middle of the last century.
Portfolio Management Applications Portfolio management applications can be divided into two categories: 1. Short Term Applications: Short term portfolio management applications include operation and operation planning activities through which traders and asset managers seek to balance their portfolios, improve their expected profits, and/or reduce their expected risks. Trading decisions that can be optimised through a structured portfolio management process include position management in various markets, commodities, and time frames as well as credit risk management with various counterparties that have different risk profiles and circumstances. Short-term asset management decisions include generation production, outage management, fuel procurement, emission management, electric transmission, and fuel transportation management. 2. Long Term Applications: Long term portfolio management applications include capacity planning decisions (asset acquisition and disposition decisions) and miscellaneous strategic decisions.
Facilitating these decisions requires providing
users with needed results for return, risk, and timeframe metrics.
1. Returns: Results can include financial metrics (e.g. Operating Margin, EBIT, Cash Flow, Net Income, etc.) and volumetric metrics (e.g. energy production, net position, fuel requirements, etc.). 2. Risks: Results can include return volatility, probability of meeting a target, specific percentiles for specific returns, expected loss, expected values of “extreme” outcomes, etc. 3. Timeframes: Metrics include balance of week, balance of month, balance of quarter, and balance of year for short-term applications and next few to 15-20 years for long-term applications. Return, risk, and time metrics are multi- dimensional and vary significantly based on user needs and perspectives. Beside financial and volumetric short and long term measures, metrics span many functional areas including generation, trading, credit, and finance to name a few. Different users can have significantly different metric needs and preferences. Commodity Trading and Risk Management (CTRM) systems should therefore be totally configurable and should allow users to select needed metrics.
Implementation Challenges A number of practical issues have limited the
effective application of portfolio optimization to date in the energy industry. Implementing an efficient frontier portfolio optimization capability requires addressing four key challenges: 1. Broad Capabilities: A portfolio management process requires a broad set of applications to simulate reasonable scenarios, estimate asset risks and returns under different scenarios, estimate the risk/return position of the existing portfolio, and identify decisions to improve the existing risk/return position. Figure 2 overleaf outlines the following needed key applications:
• Parameters calibration to estimate the simulation parameters needed by the stochastic
simulation process.
• Market simulation to simulate forward and spot prices and market values for interrelated markets
and commodities over an extended time period.
• Trade valuation tools to assess alternative return and risk metrics for generation units, trades,
loads, and other assets.
• Generation optimization to simulate the operations of a fleet of power plants under
different market prices and various unit, plant, and portfolio operating limits and availability scenarios.
• Load analysis to estimate gas and electric loads for a set of customer classes in one or multiple
locations for a specific time frame.
• Credit risk management to evaluate the credit rating of counterparties and estimate the
December 2010 25
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96