Emerging Practices for Capital Adequacy © Copyright 2003, CCRO. All rights reserved. 19 Table 3: Comparison of Modeling Approaches for Market Risk Modeling Approaches Price Behavior Process Market Exposures Pros/Cons Comments Analytical Closed-form approach for modeling price movements Works well for linear type exposures, but analytical solutions are available for non- linear positions (Taylor series expansion, etc.) Pros: • Simple and fast • Easy to derive • Easy to change as assumptions change Cons: • Does not capture optionality well • Assumes log- normal price distributions • Limited 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 to determine the contribution of the various portfolio components to total risks (e.g., component VaR) • Can be used as quick metric to help manage portfolio positions Monte Carlo Simulation 5000-10000 iterations of potential price outcomes. Robust methodology for mean reversion, jumps, linking spot & forward prices Full revaluation at each price iteration better approximates non-linearity of asset/option positions. Full-blown probability distribution of financial outcomes. Pros: • Robust • Captures optionality • Provides a full distribution of outcomes Cons: • Complex to construct the simulation model • Difficult to derive • Only as good as model input parameters • 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. • Can also be used to model credit and operations/operational risks Historical Simulation Observed set of historical commodity prices Revaluation of instruments based on observed market movements through price history. Easy to explain since “esoteric” models are not used and yet all the peculiarities of the market are captured (spikes, mean reversion, seasonality, etc.) Pros: • Robust • Captures optionality Cons: • The simulated values are constrained to conform to history, which may be irrelevant because of market economic or regulatory changes • Can be modified by appending tails to the price distributions and expanding the simulated possibilities • Can be considered as a form of stress test when the historical data include extreme market episodes • Can provide valuable insights and should complement analytical and Monte Carlo assessments