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Last Look - Oil prices rise, but post weekly loss on tariffs and OPEC+ production plans
SituationOur customer asked for insight into whether their hedging tactics were appropriate for their order sizes, and if anything should change in the way they solicit bids from their counterparties, which were exclusively banks. Like many oil producers, this company preferred to hedge when prices rose to a recent high, or at the top end of a previous range of prices. Further, the customer sought to provide a fair opportunity to each bank to bid for the hedging trades. |  |
SolutionAEGIS traders encouraged the customer to continue, but augment, their current practice of trading large, opportunistic hedges. Why continue this practice, in part? AEGIS held proprietary data that showed the amount of slippage due to liquidity was smaller than the customer expected when the market was well bid – exactly the times when the customer sought to transact. Further, AEGIS’s online, live Request-For-Quote system (Marketplace & Trade Launcher) could effectively benchmark banks’ prior performance and select a few high-quality and skilled banker/ traders for efficient and discreet transactions. Yet, AEGIS did see room for improvement. The customer began offering smaller trades to generate benchmark data and measure counterparty effectiveness. Using AEGIS’s Trade Insights capability, the customer launched its own benchmarking system for a round-robin, head-to-head data set. Meanwhile, it began following AEGIS’s recommendations to conduct smaller trades with carefully selected counterparties (via Trade Insights), and conduct time-bound RFQs to encourage competition. |
OutcomeSeveral benefits emerged. First, the customer was able to avoid some risk of bid-ask (or bid-mid) slippage by avoiding large trades on days when the market held lower liquidity. Second, the customer generated data to show to banks who were not awarded trades why they were not selected. This data showed actual dollars of opportunity cost that the customer would have incurred had the inferior bid been selected. Third, the customer was able to improve its initial strategy of using large trades, because it now could confidently select the best-performing counterparties for the situation. Fourth, more counterparties were able to participate, because the less capable banks’ trading desks could participate in smaller trades (with low cost to the customer), while the top-performing banks were much less likely to be omitted from a deal. Finally, trade analytics tracked dealer participation rates and performance for stronger dealer relationships. |
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