How Strat-Oil Analytix
Models the Market

The crack spread module measures real-time refinery profitability by evaluating the difference between the cost of crude oil inputs and the market value of refined petroleum products. This margin — widely tracked by traders and procurement teams — directly signals whether refining operations are economically viable at current market prices.

Both the 3-2-1 and 5-3-2 crack spread formulas are calculated against the WTI benchmark — the standard US Gulf Coast convention, since RBOB Gasoline and Heating Oil are NYMEX contracts priced against WTI, not Brent. Values update continuously using live market data, giving subscribers an immediate view of refinery economics.

Forward price curves are constructed using the standard Cost of Carry model used across CME Group and Platts forward curve methodology: F(T) = S × (1 + r/12) + storage − convenience yield, where the spot price is carried forward at the prevailing risk-free rate, adjusted for physical storage cost and the convenience yield of holding physical inventory.

The risk-free rate input (r) is sourced live from the 13-week US Treasury Bill yield (Yahoo Finance), refreshed automatically in the background — replacing what was previously a fixed 5% assumption — so the curve reflects current money-market conditions. OVX (Oil VIX) and the OPEC Basket price are tracked alongside the forward curve for a full pricing-instrument overview.

The 9-Week Stress Test simulates how a supply disruption event unfolds across Asia-Pacific strategic reserves over a nine-week period. The model incorporates three core variables: the scale of the supply disruption, the severity of the geopolitical risk environment, and the availability of alternative supply sources.

A composite crisis score is calculated by weighting these three dimensions — placing the greatest emphasis on physical supply disruption and price pressure, with secondary weighting on inventory depletion rate. This approach reflects the IEA emergency supply framework, where price escalation and supply shortfall are treated as the primary market stress indicators.

The model projects crude oil price trajectories on a weekly basis, applying a realistic maximum ceiling consistent with observed historical supply shock events. Inventory trajectories are calculated using standard weekly consumption draw rates, adjusted upward to reflect the elevated demand pressure that typically accompanies supply crises. Supply coverage — expressed in days and months of remaining reserves — is updated in real time as slider parameters change.

Two of the three crisis-control inputs can now be informed by live data rather than pure assumption: Alternative Supply Availability is checked against the live EIA-published OPEC spare production capacity (Short-Term Energy Outlook), and Refinery Outage is checked against the live EIA weekly US refinery utilization rate, compared to a realistic non-crisis baseline. Both remain fully manually adjustable — the live reading is a suggested starting point, not a locked value.

The LNG Stress Test models the cascading impact of a Hormuz Strait blockade on Asia-Pacific LNG supply chains. The scenario is anchored on the fact that Qatar — whose LNG export routes transit the Strait — accounts for approximately 20% of global LNG supply (verified against EIA and IEA reporting on the actual 2026 Hormuz Strait disruption, mostly via Qatar's Ras Laffan export facility), making any Hormuz disruption a direct and severe shock to APAC energy security.

An Alternative Supply Cushion parameter represents idle/spare LNG export capacity outside Qatar (US, Australia, etc.) that can soften the price spike. This can be checked against a live proxy — current US LNG export volume versus documented nominal US export capacity (EIA) — though comprehensive global spare-capacity data is only available through paid commercial feeds, so this remains a US-specific indicator rather than a full global figure.

As a blockade scenario intensifies over time, the model projects how European and Asian gas benchmark prices escalate progressively — reflecting both the physical supply shortfall from blocked Qatari cargoes and the demand surge effect as buyers compete for replacement supply from alternative origins including the United States Gulf Coast and Australia. The price escalation ceiling is an internal stress-test parameter calibrated to represent a sustained, full-blockade scenario, informed by the market reaction observed during the actual 2026 Hormuz Strait disruption.

Shipping costs are projected to rise proportionally as tankers are forced to reroute through longer, costlier alternatives such as the Cape of Good Hope, with the escalation ceiling likewise calibrated to a severe-but-plausible rerouting scenario. Henry Hub prices in the United States are modeled with a smaller, secondary sensitivity to global demand surge — real-world 2026 data showed European and Asian benchmarks (TTF, JKM) spiking sharply while Henry Hub itself moved only modestly in the near term, reflecting the limited near-term flexibility of US export capacity. Supply coverage for each APAC country is tracked week-by-week as reserves are drawn down by the combined effect of import shortfalls and demand pressure.

The Risk and Financial module provides institutional-quality risk assessment tools aligned with the methodologies used by major financial institutions and the CFA Institute. MC VaR and MC CVaR are the primary KPIs — computed from Monte Carlo simulation output, not parametric normal distribution. This is the same architecture used by Bloomberg Terminal (approx) and Basel III-compliant bank risk desks. Parametric VaR is retained as a secondary comparison metric only.

Conditional Value at Risk (CVaR / Expected Shortfall) is computed as the mean of MC tail losses beyond the VaR threshold — aligned with the FRTB (Fundamental Review of the Trading Book) and Basel IV regulatory standard, which explicitly requires Expected Shortfall from full simulation rather than parametric approximation.

The Monte Carlo engine runs 100,000 simulations using a Schwartz Ornstein-Uhlenbeck mean-reverting process combined with Merton Jump Diffusion — capturing fat tails, volatility clustering, and sudden price shocks that standard GBM cannot model. This simulation count meets and exceeds the Basel III minimum of 10,000 and is consistent with the benchmark used by MATLAB Financial Toolbox and major quant hedge funds (10,000–100,000 sims).

Sharpe and Sortino Ratios measure risk-adjusted returns on oil price exposure. The Sharpe Ratio penalizes all volatility equally, while the Sortino Ratio penalizes only downside volatility — a more appropriate measure for energy market participants who are primarily concerned with adverse price movements. Both ratios are annualized using the standard 252 trading-day convention. The risk-free rate input is sourced live from the 13-week US Treasury Bill yield (Yahoo Finance), refreshed automatically, rather than a fixed assumption — reflecting current money-market conditions rather than a stale estimate.

VaR Backtesting — Three-Level Protocol:

Level 1 — Basel II Traffic-Light: Breach count vs expected — GREEN (model accurate) / YELLOW (review required) / RED (model failure) — per BIS regulatory guidance, the minimum standard required of all bank internal models.

Level 2 — Kupiec Proportion of Failures (POF) Test: Exact likelihood ratio test — LR_PoF = −2·log[((1−α)^(T−x)·α^x) / ((1−x/T)^(T−x)·(x/T)^x)] — chi-square distributed with 1 degree of freedom. ACCEPT/REJECT at p > 0.05. This is the same formula implemented in MATLAB varbacktest() and used by Bloomberg Terminal (approx) backtesting engines.

Level 3 — Christoffersen Conditional Coverage (CC) Test: Extends Kupiec by testing not just the count of breaches but whether they cluster in time — a critical distinction for energy markets prone to volatility regimes. LR_CC = LR_PoF + LR_Independence, chi-square with 2 degrees of freedom. This is the Basel III / academic finance standard for complete model validation.

Both MC VaR and Parametric VaR are independently backtested across all three levels, providing full dual-model auditability. Every output carries a data provenance stamp — recording the data source, formula applied, and timestamp — ensuring full traceability for procurement and compliance teams.

The Oil Intelligence module calculates the Gross Product Worth (GPW) netback value of crude oil — the standard metric used by Platts and Wood Mackenzie to determine the true economic value of a crude grade to a refiner. The calculation works backwards from the market value of refined products — gasoline, diesel, and residual fuel — applying standard yield ratios that reflect typical Asia-Pacific refinery configurations, then subtracting transportation, freight, and refining costs to arrive at the landed netback value.

Per-country freight multipliers reflect actual shipping distances and route costs from major loading terminals to each of the 15 APAC destination countries tracked by the platform. Crude price differentials between Brent, WTI, and Dubai benchmarks are applied using Platts and Argus published differential standards. The freight input can be checked live against the platform's tanker-rate proxy (converted from a $/day VLCC charter rate to $/bbl using standard vessel-capacity and voyage-length assumptions), and the gasoline and diesel premiums over Brent can be checked live against the RBOB-Brent and Heating Oil-Brent futures spreads (Yahoo Finance) respectively.

The OPEC Basket price is sourced through a resilient multi-source live data feed, pulling the actual published OPEC Basket value directly rather than approximating it — ensuring subscribers see the real current benchmark. A verified trend-model fallback, fitted to recent confirmed OPEC-Brent premium data, maintains uninterrupted price continuity on the rare occasion all live sources are temporarily unavailable. The arbitrage signal compares the landed cost of Middle Eastern crude versus US crude at each APAC destination — immediately identifying which supply origin offers the most economical barrel for each country.

The LNG Price Monitor tracks the live spread between US Henry Hub and European TTF gas prices — the core signal for whether shipping US LNG to Europe or Asia is currently economical. TTF is quoted in EUR/MWh and converted to USD/MMBtu for direct comparison with Henry Hub.

The EUR/USD exchange rate used in this conversion is sourced live from Yahoo Finance, refreshed automatically, ensuring the spread reflects current currency conditions rather than a stale manual rate.

The LNG User Price calculator builds the complete delivered cost of LNG from the US Gulf Coast to any of thirteen Asia-Pacific destination countries. The calculation chains together every cost component in the LNG supply journey: the base Henry Hub gas price at the wellhead, liquefaction costs at the export terminal, ocean freight from the US Gulf to the destination port, regasification costs at the receiving terminal, and a country-specific regional freight adder that reflects local distribution economics.

This full chain cost methodology — aligned with Wood Mackenzie LNG netback standards — produces the true all-in delivered price of US LNG at each APAC destination. The platform then compares this delivered cost against prevailing European TTF benchmark prices to generate an arbitrage signal that immediately tells a trader or procurement officer whether shipping US LNG to a given destination is currently profitable, marginal, or uneconomical.

No free, reliable live data feed exists specifically for LNG shipping/freight cost — the real industry benchmarks (Platts LNG Freight Assessment, NGI Shipping Costs, Spark Commodities) are paid commercial subscriptions. Rather than presenting a fabricated "live" number, shipping cost is set via destination-based presets plus a Market Regime selector (Normal / Elevated / Crisis), with multipliers anchored to dated, real 2025-2026 LNG carrier day-rate observations (Bloomberg, Fearnleys) converted to $/MMBtu — an honest, order-of-magnitude scenario band rather than a precise live price.