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.

The platform tracks two industry-standard refinery configurations: a lighter crude refinery model that produces a higher proportion of gasoline relative to distillates, and a heavier crude refinery model suited for complex refinery operations more common across Asia-Pacific facilities. Both models update continuously using live market data, giving subscribers an immediate view of refinery economics across major APAC benchmarks including Chicago and Group 3.

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.

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 30% of global LNG supply, making any Hormuz disruption a direct and severe shock to APAC energy security.

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. Under a full blockade scenario, price escalation is modeled up to a ceiling consistent with IEA Gas Market Report stress scenario limits.

Shipping costs are projected to rise proportionally as tankers are forced to reroute through longer, costlier alternatives such as the Cape of Good Hope. The shipping escalation ceiling is aligned with Baltic LNG Index historical maximum volatility ranges. Henry Hub prices in the United States are also modeled to rise as demand for US LNG exports increases, consistent with EIA Short-Term Energy Outlook methodology. 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.

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 OPEC Basket price is computed as a production-weighted average across ten OPEC member crudes, using official OPEC member production weights. 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 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.