Interactive

Risk Tools

Parametric VaR & Expected Shortfall, a volatility surface, a correlation matrix and a live markets board.

Live Markets

Benchmarks across energy, metals, FX and equity volatility — each with a 7-day trend. Marked “Live” only when the feed is genuinely fresh; otherwise the last close is shown with the date it was set.

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Prices via Financial Modeling Prep · quotes cached ~30 min · 7-day sparkline from end-of-day data. Cards marked “Prev close” show the last close the feed reported, with its date — the data may lag the market. Indicative only — not for trading or execution.

Risk Analytics

Institutional Risk Analytics

Interactive VaR, a volatility surface and a commodity correlation matrix — plus deep-dive coverage of market, counterparty and model risk.

Parametric VaR Calculator

Interactive

VaR = z_α · σ_daily · √t · V | σ_daily = σ_annual ⁄ √252

Portfolio Value (USD)

Annualised Volatility30%

Holding Period1 day

Confidence Level

Value-at-Risk

—

Expected Shortfall (CVaR)

—

Volatility Surface

Illustrative

Implied vol (%) by option tenor and moneyness — a representative crude-oil surface showing the smile and term structure.

Commodity Correlation Matrix

Illustrative

Representative price-return correlations across the energy complex — the backbone of portfolio VaR diversification.

Go deeperMarket Risk 101 Hedging & Derivatives

Counterparty Credit Risk (CCR)

Framework

The risk that a trading counterparty defaults before settling its obligations — quantified through exposure, default probability and loss severity.

EAD

Exposure at Default

PFE

Potential Future Exposure

CVA

Credit Valuation Adjustment

PD · LGD

Default × Loss Severity

Expected loss on a derivative book is EL = EAD × PD × LGD, with SA-CCR governing regulatory exposure and CVA capital charges under Basel.

Go deeperCredit & Counterparty Liquidity & Funding

Model Risk & Validation

Framework

The risk of loss from models that are wrong, mis-calibrated or misused. Managed through independent validation, backtesting and governance across the model lifecycle.

Validation

Independent challenge

Backtesting

VaR exceptions (traffic-light)

Benchmarking

Champion vs challenger

Governance

Inventory & approvals

Under FRTB, desks failing VaR backtesting are pushed from the Internal Models Approach toward the more punitive Standardised Approach — making validation a capital issue, not just a modelling one.

Go deeperFrameworks & Governance Market Risk 101

Worked Examples

Structured Hedge Examples

Illustrative, backtested hedging scenarios across oil, aviation, gas, LNG and power — showing how plain-vanilla futures & forwards, options & swaps, and dynamic strategies cut exposure. Indicative only — not trading advice.

Airline Jet Fuel — Short ICE Brent Futures Hedge

Scenario: airline with 10,000 MT/month jet-fuel exposure · Backtest: Jan–Dec 2022 (Russia–Ukraine shock)

Plain VanillaFutures

Unhedged

−$28.4M

Brent rose $78 → $123/bbl. Full fuel-cost exposure on 10,000 MT/month. VaR: $4.2M/day.

Hedged — Short Brent futures (80% ratio, 3M rolling)

−$5.7M

80% of volume hedged via 3-month ICE Brent futures. Residual = basis risk only. VaR cut to $0.9M/day.

78.4%

Hedge Effectiveness

−0.82

Correlation (Hedge)

$22.7M

P&L Saved

$1.4M

Hedge Cost (Margins)

LNG Offtaker — TTF-Linked Gas Forward Hedge

Scenario: industrial gas buyer, 50 TJ/month floating JKM exposure · Backtest: Q3–Q4 2021 supply crunch

Plain VanillaForward

Unhedged — Floating JKM spot purchases

+$18.4M cost overrun

JKM spiked $12 → $56/mmbtu (Oct 2021). 50 TJ/month at floating price created a large unbudgeted cost.

Hedged — 3M TTF forward at $15/mmbtu

+$1.2M cost overrun

TTF–JKM basis locked with a 3-month forward at $15/mmbtu. Saved $17.2M vs unhedged floating purchases.

93.5%

Hedge Effectiveness

−0.91

Correlation (JKM/TTF)

$17.2M

Cost Avoided

$0.4M

Forward Spread Cost

Crude Oil Producer — Zero-Cost Collar (Buy Put / Sell Call)

Scenario: E&P producer, 100,000 bbl/month, 6-month tenor · Backtest: H2 2023 oil-price correction

OptionsCollar

Unhedged

−$13.2M revenue loss

WTI fell $92 → $70/bbl in H2 2023. 100,000 bbl/month × $22 drop × 6 months = $13.2M below budget.

Collar — Buy $72 put / Sell $90 call (zero net premium)

−$1.2M residual

Floor at $72/bbl. Put pays $2/bbl on 600K bbl = $1.2M received. Upside capped at $90. Net premium ≈ zero.

$12.0M

Revenue Protected

$72/bbl

Floor (Put Strike)

$90/bbl

Upside Cap (Call Strike)

~$0

Net Premium (Zero-Cost)

Industrial Gas Consumer — Fixed-Float TTF Commodity Swap

Scenario: European manufacturer, 5 MW continuous gas load · Backtest: 2022 EU energy crisis

SwapCommodity

Unhedged — Floating TTF spot purchases

+€22.8M cost overrun

TTF spiked €35 → €310/MWh (Aug 2022). Gas bill rose 8× vs budget — a cash-flow crisis for unhedged buyers.

Hedged — Pay-fixed €40/MWh, receive floating TTF (12M swap)

+€0.6M cost overrun

Pay-fixed €40/MWh, receive floating TTF on a 12-month swap. Net cost capped — saved €22.2M vs spot.

97.4%

Hedge Effectiveness

€22.2M

Cost Avoided

€40/MWh

Fixed Rate Locked

€0.3M

Swap Bid-Offer Cost

LNG Trading Book — Rolling Monthly Futures Hedge

Scenario: LNG desk, 6-cargo portfolio, monthly delta rebalancing to 70% target · FY2022

DynamicRolling Hedge

Static Hedge (Set-and-Forget at 80%)

−$8.4M residual MTM

Static 80% hedge set Jan 2022. As the forward curve restructured, the effective hedge ratio drifted to ~55% — leaving unintended exposure.

Dynamic — Monthly rebalance to 70% delta target

−$1.9M residual MTM

Futures re-struck monthly to hold a 70% delta, with the forward curve re-evaluated each cycle. Residual risk cut 77% vs static.

77.4%

Risk Reduction vs Static

70%

Target Delta (Monthly Reset)

12×/yr

Rebalancing Frequency

$6.5M

Saved vs Static Approach

Gas Peaker Plant — Spark-Spread Cross-Commodity Dynamic Hedge

Scenario: 400 MW CCGT buying TTF gas, selling EEX power · Backtest: 2021–2022 European markets

DynamicCross-Commodity

Unhedged Spark Spread

€8.4M P&L volatility

Power–gas correlation broke down in 2022. The spark spread swung €8 → −€14/MWh in six weeks — unbudgeted dispatch losses with no protection.

Dynamic — Buy TTF swap + sell EEX power forward (weekly rebalance)

€1.4M P&L volatility

Matched TTF buy-swap + EEX power sell-forward, reweighted weekly as heat-rate assumptions shifted. Spark-spread P&L volatility cut 83%.

83.3%

P&L Vol Reduction

Weekly

Rebalancing Frequency

TTF + EEX

Instruments Used

€7.0M

P&L Volatility Saved

Hedge effectiveness across all six examples

Unhedged exposure / loss vs hedged outcome for each scenario (local currency, $M or €M).

Illustrative backtested scenarios · figures indicative and not for trading or execution.