A power trading business got a historic, attributable view of position and P&L — one version of the truth.
Market and credit risk, hedge effectiveness, and P&L attribution were scattered and hard to trust. We built an enterprise risk data warehouse — with VaR by Monte Carlo and full position and P&L attribution — as one consistent source across the organization.
Challenge
The organization had no uniform, consistent view of market and credit risk. Short-, mid-, and long-term supply and demand forecasts, price verification, hedge effectiveness, plant performance, and position and P&L lived across separate systems — so no one could get a trusted, historic, time-series view of forward position and P&L, or explain what moved it.
Approach
We built an enterprise risk data warehouse as one version of the truth: supply and demand forecasts across horizons, multi-level price verification, hedge effectiveness, and market and credit risk — with VaR calculated by Monte Carlo simulation — plus a historic, time-series view of forward position and P&L and the attribution analysis that explains the movement.
Technology
An enterprise risk data warehouse with Monte Carlo VaR, multi-level price verification, hedge-effectiveness and plant-performance measures, and time-series position and P&L attribution across the trading organization.
Outcome
Risk, trading, and leadership share one consistent view of market and credit risk — and when position or P&L moves, the attribution analysis says why, instead of leaving analysts to reconstruct it by hand before every meeting.
We publish our case studies anonymized as a matter of policy; this one draws on several enterprise risk-data-warehouse engagements. The work and figures are real — we describe the pattern, not any single client’s name.
Explore the analytics
Synthetic data — a hands-on view of the kind of dashboard this engagement delivered.
Cross-Commodity P&L Attribution
Slice, dice, and drill-through P&L attribution across natural gas, crude, and electricity — synthetic data.