Trading & Hedging Decisions
Figure 1: Dynamic Risk Framework Applications Market, Credit,
Collateral, Cashflow & Operational Risk
Oil & Gas Production Fuel Hedging
Power Generation
Long-Term Contract
Negotiation
Power, Gas & Oil Marketing Pipeline Transportation
Cargo Trading & Arbitrage Natural Gas Storage
Senior Management & Board Decision Support Tools
Source: NQuantX LLC
Dynamic risk simulation tools can be applied to complex decision or problems involving multiple sources of risk and state variables and can serve as the basis to calculate accurate and useful metrics versus those from traditional risk analysis based on static portfolios, relatively short time horizons and unrealistic risk factor scenarios. Dynamic risk simulation involves modelling the variability
of one or more metrics (e.g., cash flow, earnings, Mark-to- Market, liquidity, etc.) based on realistic potential changes in a set of key state variables, as well as the firm’s response to those changes (e.g., operating, hedging and trading strategies). The analysis consists on “leaping forward” at various points in time in the future. In order to capture the multiple risk dimensions involved in hedging and trading, a dynamic simulation framework with three critical components is needed: Ability to handle multiple risk factors (e.g. commodities, credit events, operational issues), multiple instruments (e.g. physical contracts, derivatives), as well as the ability to capture events taking place at multiple steps in time. An added benefit of using dynamic simulation-based risk
Valuation & Financial Reporting
Hedging
Strategy Design & Execution
strategies based on the evaluation of risk-return tradeoffs. It can also help finance groups creating forward-looking earnings projections by ensuring they are consistent with the risk appetite of the firm. Another critical area is the evaluation of important investment and divestment decisions from a marginal and stand-alone risk point of view (Figure 1). New advances in financial engineering and
computational finance such as Least Squares Monte Carlo (LSCM) and Dynamic Programming allow for the widespread use of dynamic risk simulation solutions in energy firms (Figure 1). In the remaining sections of this article, we introduce some areas where dynamic risk models can be applied to improve decision-making at various levels of the firm.
Bringing Boards & Senior Management Into the Hedging Strategy Design Process
The chain of events in energy and financial markets since
2008 has again shown the importance of having a robust and comprehensive risk management and hedging strategy in place. Actively hedging market or credit risk requires board and senior management support as well as a strong discipline. Without sufficient backing, many firms fall into the inaction trap, and when market moves against them, they blame poor results on “unforeseen market conditions” – or even Black Swans. Boards and senior management teams are gradually getting
more involved in setting clear goals for the risk function that are aligned with the business objectives of the firm. They are also taking a more active role to ensure that the risk groups have the independence, stature and adequate resources to fulfil their responsibilities. We believe that one of the major gaps that currently exists
... one of the major gaps that currently exists in the risk management process ... is the lack of communication between risk groups and senior management
tools is the potential for risk management to play a larger role in strategic business decisions at various levels of the firm. For example, simulation analysis can assist trading and operating groups in developing asset optimisation and hedging
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in the risk management process at many firms is the lack of communication between risk groups and the senior management team, which means substantial amounts of resources are wasted implementing models and creating reports without actionable information.
A paradigm shift is needed. Risk groups should realign their
resources and put greater emphasis on implementing risk models that can serve as decision-support tools to design and evaluate hedging and trading strategies based on potential variability of cashflow, earnings and other metrics (Table 2).
worldPower 2010
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