Risk Exposure Assessment

We start by aggregating all positions a client holds – physical inventory, forward contracts, futures, options, swaps, etc. – across all commodity classes. For example, a diversified energy company might have crude oil futures, natural gas forward sales, coal inventory, and power purchase agreements.
Our dashboard consolidates these into one portfolio view, normalizing units and aligning maturities.
This alone is a game-changer for organizations that previously managed risk in fragmented spreadsheets. With a centralized book, we can then apply analytics uniformly.

The dashboard calculates VaR over chosen horizons (e.g., 1-day, 10-day) at specified confidence levels (95%, 99%). It uses advanced methods beyond variance-covariance, including Monte Carlo simulation, which generates thousands of random price paths per commodity to capture non-linear behaviors like option payoffs. Historical VaR is also offered, revaluing the portfolio against actual past market moves to include events like the 2008 crisis or 2022 volatility. The output is a clear figure-for example, “95% VaR = $5 million,” meaning there is a 5% chance losses exceed $5 million over the horizon.
This metric provides a common risk language for management and meets regulatory or policy requirements.

A key feature of our risk engine is its dynamic handling of commodity correlations. Unlike static correlation matrices, we use DCC-GARCH models to update correlations as volatility changes and incorporate copula models to capture extreme co-movements and tail dependencies during market stress. This approach reflects scenarios where commodities move together more than average correlations suggest, such as during crises or liquidity crunches.
As a result, our risk estimates account for worst-case common shocks, often producing slightly higher but more prudent VaR or expected shortfall figures compared to simpler methods, embodying a conservative, data-driven risk approach.

RiskView 360 includes a library of historical (e.g., “Lehman Collapse,” “COVID Outbreak”) and hypothetical stress scenarios (e.g., “Oil $150,” “Global Recession -20% demand”). Applying a scenario re-values the entire portfolio and shows profit/loss impacts by position and overall, answering “What happens if X occurs?”
These insights let risk managers and executives identify vulnerabilities. If a stress test shows an outsized loss beyond tolerance, they can proactively adjust (hedge more, reduce positions, diversify).
Essentially, the scenario tool operationalizes contingency planning.

All our analysis is shown in an intuitive web interface with interactive P&L distribution charts, commodity VaR contributions (like tornado charts), and correlation heat maps that update with market regimes. A “risk by business unit” view allows drilling down to desks or divisions. Users can set alerts for VaR breaches or commodity exposure limits, triggering email, SMS, or on-screen notifications.
Unlike typical systems, ours links alerts to analytics, showing causes like market moves or correlation changes, providing essential context for swift action.

The dashboard generates automated risk reports-daily summaries, regulatory (e.g., Basel commodity risk), and management reports with clear commentary. Quantitative results are explained plainly, such as: “Our 99% ten-day VaR is $12M, driven mainly by long copper and aluminum exposure. Under a hypothetical Chinese demand shock, losses could reach $20M (mostly copper), but aluminum hedges limit downside.”
This narrative, alongside charts, helps non-quant executives and boards understand the risk, building trust in rigorous risk management.