Back in the pre-AI days which I’ll now refer to as B.AI. I would spend hours searching the internet for articles on particular topic that caught my interest. For example, let’s say I read an article about the shortages of corn. We know corn is used as a food source but also an input into ethanol fuel so if there are shortages of corn you can expect impact to food prices and fuel prices. In that scenario there will be winners and losers so placing trades accordingly to make money is an ideal situation.

Case Study (Australia)

On the topic of fuel and energy, I have been reading that some countries around the world will begin to run out of fuel in a few weeks. If this happens, businesses will likely crash so which companies and countries may possibly yield the best opportunities?

In A.AI. (after AI), it’s as easy as testing my thesis with AI bot. I took data on EWA (Australia ETF) and asked AI to examine major drawdown periods.

📉 Drawdown #1 — Mar 2025 → Apr 2025 (≈ -18.9%)

Peak → trough: 2025-03-10 → 2025-04-08
Recovery: by late May 2025

Likely drivers

• 🇨🇳 China growth fears — weak property & industrial data hit demand expectations for iron ore.
• 🪨 Iron ore price drop (Australia heavily mining-exposed; EWA is ~40% financials + materials).
• 💵 Stronger USD / global risk-off — pressured commodity currencies like AUD.
• 🏦 RBA policy uncertainty — market debating rate cuts vs sticky inflation.

Why it hit EWA hard:
EWA is highly sensitive to banks + miners → both sold off when commodity outlook weakened.

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📉 Drawdown #2 — Nov 2025 → Jan 2026 (≈ -9.5%)

Peak → trough: 2025-11-17 → 2025-11-20
Recovery: late Jan 2026

Likely drivers

• 🇺🇸 Global bond yield spike (higher-for-longer narrative)
• 🏦 Banking sector pressure (Australian banks dominate EWA)
• 🇨🇳 Soft Chinese stimulus disappointment
• 💱 AUD weakness reflecting global slowdown fears

This period looked more like a global macro rate-driven drawdown rather than commodity shock.

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📉 Drawdown #3 — Mar 2026 (≈ -9.9%) — ongoing short-term pullback

Peak → trough: 2026-03-13 → 2026-03-26

Likely drivers

• 📉 Iron ore pullback again
• 🌏 China demand concerns resurfacing
• 🏦 Financials consolidation after rally
• 📊 General equity market risk-off rotation

This one appears more tactical / sentiment-driven, not structural.

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Big Picture Pattern

EWA drawdowns typically occur when:

1. China growth weakens
2. Iron ore / commodities fall
3. Global yields rise (hurts banks)
4. USD strengthens

Australia ETF = China proxy + bank ETF hybrid

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Summary Table

Period	Drawdown	Main Theme
Mar–Apr 2025	-18.9%	China slowdown + commodity crash
Nov 2025–Jan 2026	-9.5%	Global rate spike + banks
Mar 2026	-9.9%	Commodity pullback / sentiment

But AI now has an advanced deeper analysis mode so I pressed further….

Scenario Analysis: Iran-Crisis–Driven Oil Shock and a Potential Australian Fuel Shortage — Implications for EWA

Executive summary

I model three oil-shock scenarios tied to the ongoing Iran crisis and translate them into plausible peak-to-trough drawdown ranges for EWA (iShares MSCI Australia ETF) using a transparent, assumption-based channel model. The motivation for explicitly considering a “domestic shortage” tail is that entity[“country”,”Australia”,”country”] is highly import-dependent for transport fuels and has already experienced cancelled shipments and localized outages during the conflict: Reuters reports Australia imports “about 90% of its fuel,” had “39 days’ worth of petrol and 30 days of diesel and jet fuel,” and has seen multiple shipments cancelled and many stations run dry.

My modeled EWA drawdown ranges (USD, peak-to-trough notion; best estimate plus plausible bounds) are:

• Mild shock (oil +10–25% for 1–3 months): ~11% best estimate; ~8–15% plausible bounds.
• Moderate shock (oil +25–75% for 3–9 months): ~30% best estimate; ~23–40% plausible bounds.
• Severe shock (oil +75–200%+ for 6–18 months): ~62% best estimate; ~48–78% plausible bounds.

The model attributes most downside risk to EWA’s Financials and Materials concentration plus AUD/USD depreciation in a risk-off regime. Per entity[“company”,”iShares”,”etf brand”] coverage (as of Mar 26, 2026), I assume EWA sector weights of Financials 42.59%, Materials 21.76%, Consumer Discretionary 6.10%, and Energy 4.57% (remainder “Other”).

I assign scenario likelihood qualitatively: Mild = high, Moderate = medium, Severe = low—based on the scale of global mitigation (IEA stock releases) versus the still-material risk of prolonged disruptions through the entity[“point_of_interest”,”Strait of Hormuz”,”gulf oil chokepoint”].

Why the Iran crisis can turn into an Australian fuel shortage

Even without “running out of oil” nationally, Australia can face a severe domestic fuel shortage (petrol/diesel/jet) through a combination of import dependence, shipping disruption, and distribution bottlenecks.

Australia’s exposure is visible in both official reviews and current reporting:

• Import dependence and limited buffer. Australia’s Liquid Fuel Security Review states Australia “imports over 90 per cent of the refined products and crude oil we need to meet our demand,” and highlights diesel’s criticality if imports are stopped.
• Real-time stress already occurring. Reuters reports that, amid the Iran war’s supply-chain disruption, Australia had 39 days of petrol and 30 days of diesel and jet fuel, with six shipments cancelled and “several hundred” stations running out of gasoline or diesel.
• Localised shortages can occur even if “the country” has stock. The same government review explains that shortages can happen “even when there is enough stock in the country,” because stocks vary by location and take time to move; areas with single points of failure or limited logistics diversity are especially exposed.
• Spillover into other critical systems (power reliability). entity[“organization”,”Australian Energy Market Operator”,”australia power system operator”] notes that coal/gas/diesel fuel shortfalls can reduce reliability in the National Electricity Market and that some generators have diesel as secondary fuel with limited on-site storage (hours).

On the global side, the chokepoint matters because the Strait of Hormuz is an outsized conduit with limited bypass capacity. The entity[“organization”,”U.S. Energy Information Administration”,”us federal energy statistics agency”] estimates 2024 flows through Hormuz averaged ~20 million b/d, about 20% of global petroleum liquids consumption, and stresses few alternatives if closed. The entity[“organization”,”International Energy Agency”,”energy security agency”] similarly characterizes Hormuz as a critical artery and, in the current conflict, reports export volumes through the Strait are <10% of pre-conflict levels.

Oil-shock scenarios and explicit assumptions

Scenario definitions tied to the Iran crisis

I treat “oil price” as Brent crude (global benchmark) and define shocks as the percent rise above a pre-crisis baseline.

As an anchor for what the market is currently pricing, Reuters reported on March 27, 2026 that Brent settled at $112.57/bbl, up 53% since Feb 27 (the day before the U.S. and Israel launched strikes against Iran); Reuters also reported a Macquarie view that prices could rise to $200 if the war dragged on to end-June. That “already-realized” +53% is squarely within the scenario’s moderate range and supports treating the severe case as a tail but not a fantasy.

I also incorporate IEA emergency actions as a “shock buffer”: IEA member countries agreed to release 400 million barrels from emergency reserves (largest release announced) in response to Middle East conflict disruptions.

Scenario assumptions table

Scenario	Oil price shock (explicit assumption)	Duration (explicit assumption)	Oil-path shape I assume	Qualitative likelihood
Mild	+10% to +25% (mode +15%)	1–3 months (mode 2)	Fast spike, partial mean reversion as shipping/insurance lanes stabilize and IEA barrels arrive	High
Moderate	+25% to +75% (mode +50%)	3–9 months (mode 6)	Elevated plateau with intermittent spikes as flows remain constrained and risk premium persists	Medium
Severe	+75% to +200%+ (mode +150%; capped at +200% in base model)	6–18 months (mode 12)	Prolonged disruption + infrastructure damage + tight supply elasticity; large risk premium remains	Low

Rationale and evidence for these ranges:

• A major “buffer” exists via emergency stocks: the IEA release is large and can be expanded; it is explicitly framed as a response to the conflict-driven disruption.
• However, the crisis can still produce outsized prices: Reuters reports oil could reach $200 under prolonged-war conditions and highlights the scale of volumes missing while Strait flows are restricted.
• Australia’s own liquid-fuels review explicitly contemplates scenarios where global emergency stocks can hold supplies “for several months,” but that longer disruptions require a high price increase to reduce demand.

Illustrative oil-price paths

Model of transmission from an oil shock to EWA

What EWA is exposed to

EWA is effectively a concentrated bet on large-cap Australia, and—crucially for this scenario—its sector mix is dominated by banks and miners. Using iShares’ sector breakdown (as of Mar 26, 2026), I model:

• Financials 42.59%
• Materials 21.76%
• Consumer Discretionary 6.10%
• Energy 4.57%
• Other sectors ~24.98% (residual)

The key transmission channels I model

The channels and why they matter in this specific scenario:

• Direct energy-sector effect (small weight, mostly positive): higher oil often benefits Australian upstream energy equities (but Australia’s energy weight inside EWA is small). So this is typically a partial offset rather than the main driver. (Weight source above.)
• Inflation → monetary policy response: higher fuel costs add to inflation pressure and—more importantly—risk “second-round” effects. The entity[“organization”,”Reserve Bank of Australia”,”central bank”] emphasized on March 17, 2026 that petrol prices can add to inflation and that the Bank is focused on preventing inflation expectations from drifting up; it explicitly highlighted second-round effects and the exchange-rate channel as part of policy transmission.
• AUD/USD moves (typically negative for EWA in risk-off): in severe global risk-off, the USD tends to be a safe haven. RBA communications also explicitly describe the exchange-rate channel as an important mechanism (higher rates can support the currency and damp imported inflation), reinforcing that FX is a core part of the macro linkage.
• Diesel costs on miners/materials: mining is diesel-intensive. Reuters reports Singapore diesel swap prices nearly doubled and that Fortescue estimated costs scale quickly: a 10-cent increase in diesel implies about $70m added costs for Fortescue and ~$500m for the top four miners combined.
• Banks/consumer stress: higher fuel and rates compress real household income, raise delinquency risk, and pressure housing/retail activity. This channel is structurally important because Financials are the largest EWA weight.
• Non-linear “shortage” effects: if fuel becomes physically unavailable, not just expensive, activity can contract sharply. An RBA Q&A explicitly notes that a fuel shortage can imply both supply and adverse demand shifts, which can weaken the economy even as inflation dynamics are uncertain.
• Critical-infrastructure amplification: AEMO explicitly treats diesel fuel shortfalls as a reliability risk, and documents limited hours of secondary fuel storage for some generators—i.e., tight diesel availability can create compounding risks beyond transport.

Quantitative inputs I assume by scenario

All numeric inputs below are explicit assumptions (not forecasts). Where user constraints were unspecified, I treat inputs as unconstrained and state them.

Input (mode; min–max)	Mild	Moderate	Severe
Oil price shock	+15% (10–25)	+50% (25–75)	+150% (75–200)*
AUD/USD move (USD per AUD)	-4% (-2 to -6)	-8% (-5 to -12)	-15% (-10 to -25)
RBA cash-rate change from 4.10%	+0–25 bps (mode +10)	+25–100 bps (mode +50)	+100–200 bps (mode +150)
Financials sector return (AUD)	-8% (-5 to -12)	-20% (-12 to -30)	-40% (-25 to -55)
Materials sector return (AUD)	-6% (-3 to -10)	-18% (-10 to -30)	-35% (-20 to -50)
Energy sector return (AUD)	+5% (+2 to +10)	+12% (+5 to +25)	+25% (+10 to +50)
Consumer Discretionary return (AUD)	-10% (-6 to -15)	-25% (-15 to -35)	-45% (-30 to -60)
Other sectors return (AUD)	-6% (-3 to -10)	-17% (-10 to -25)	-32% (-20 to -45)
Stress/correlation “overshoot multiplier” applied to modeled trough	1.10	1.25	1.40

*For the “200%+” severe tail, I cap the modeled range at +200% but discuss the implication: Reuters cites a $200/bbl scenario as plausible if the war drags on, and if baseline is materially lower this can exceed +200%. citeturn3view0

Correlation assumptions (used for bounds)

To produce plausible bounds (not just point estimates), I assume correlations rise with severity:

• Average correlation among non-energy equity sectors rises from ~0.75 (mild) to ~0.92 (severe).
• Correlation between AUD/USD and Australian equities rises from ~0.60 to ~0.80 (risk-off → both down).
• Correlation between oil and non-energy equities is negative (oil up tends to coincide with growth/risk-off pressure), while oil and Energy equities is positive.

These are stylized stress-regime assumptions; they are intended to capture the empirical tendency for correlations to rise in crises, not to be exact estimates.

Estimated EWA drawdown ranges, contributions, and sensitivity

Result table: modeled EWA peak-to-trough drawdowns

These outputs are computed from the assumptions above, using EWA sector weights per iShares (Mar 2026 sector weights).

Scenario	Best estimate drawdown (mode-based)	Plausible bounds (5th–95th percentile)	Primary reasons for the range	Probability (qualitative)
Mild	~11%	~8% to ~15%	FX depreciation risk, bank valuation compression, partial offset from Energy	High
Moderate	~30%	~23% to ~40%	Bank-heavy index in stagflation/risk-off; AUD down materially; miners face large diesel cost pressure	Medium
Severe	~62%	~48% to ~78%	Prolonged shock + policy tightening + recession risk; severe AUD depreciation; cross-sector correlation surge	Low

Why I assign these probabilities:

• Mild (high): IEA has executed a very large stock release and can add more; Reuters/Barclays reporting includes a base case of normalization by early April, implying a shorter-lived dislocation.
• Moderate (medium): current observed pricing already resembles moderate: Brent up 53% since Feb 27; Australia is experiencing cancelled shipments and local outages, consistent with “months, not weeks” tail risks.
• Severe (low): requires extended, compounded disruption (shipping security not restored, significant infrastructure damage, and/or multiple chokepoints), but it is not impossible—Reuters cites a $200/bbl scenario under prolonged conflict conditions.

Channel contribution view

This chart decomposes the mode-based drawdown estimate into sector-return contributions plus FX and a small interaction effect.

Interpretation:

• In every scenario, the largest negative contributors are Financials and FX (AUD/USD). This is structural: Financials are ~43% of EWA, and FX translation is direct in the USD-listed ETF.
• Energy is a positive offset, but too small a weight to dominate.
• Materials are meaningfully negative in moderate/severe cases, consistent with Reuters’ reporting that diesel costs can surge and materially impact miner cost bases.

Sensitivity tornado

This tornado shows which assumptions most move the Moderate scenario drawdown estimate.

The main takeaways:

• Financials returns and AUD/USD dominate the drawdown uncertainty—consistent with EWA’s weight structure and USD listing.
• Materials are the next tier, reflecting diesel-cost pressure and growth sensitivity.

Investor action checklist for an EWA holder

This is a general risk-management checklist, not individualized advice.

Hedging and rebalancing actions

• Consider whether you want to keep AUD exposure. Because EWA is USD-listed and unhedged, AUD depreciation can materially deepen drawdowns. If your base case is AUD weakness in a prolonged shock, consider partial FX hedging (e.g., via AUD/USD hedges) or reducing allocation size. RBA communications underscore the exchange rate as an important channel in the inflation/monetary policy mechanism.
• Stress-test your portfolio for a bank-led drawdown. Financials are ~43% of EWA; in a stagflationary oil shock, banks can face valuation compression and credit-loss risk.
• Decide whether you want an explicit “oil shock hedge.” Because EWA’s Energy weight is relatively small, EWA can still fall sharply even when oil rises. A hedge can be an offsetting allocation to energy equities/commodities (risk: oil can reverse quickly if diplomacy improves, as recent oil volatility has shown).
• If you use options, pre-define protection rules. In a severe scenario, gap risk and higher implied volatility tend to make ad hoc hedging expensive; rules-based hedging (e.g., rolling puts or collars) can reduce behavioral delay.
• Maintain liquidity buffers. A true domestic fuel shortage can create operational and economic stress that cascades (transport, supply chains, emergency services). Australia’s fuel security review explicitly discusses crisis management and prioritization of critical uses in severe cases.

Monitoring indicators and triggers

I would monitor these as “go/no-go” triggers for escalating from mild → moderate → severe:

• Oil price level and slope: Brent remaining >+25% above pre-crisis levels beyond 1–3 months is a practical threshold. Reuters reports Brent was already +53% (Mar 27).
• Shipping/throughput signals for Hormuz: the IEA states flows are currently <10% of pre-conflict levels; improvement here is a direct de-escalation indicator.
• IEA collective action cadence: announcements of additional coordinated releases (beyond the 400m bbl program) are a sign the shock is persisting and/or worsening.
• Australian domestic fuel logistics: (a) number of cancelled shipments, (b) days-of-cover statements, (c) rationing or emergency procurement policies. Reuters’ metrics (days of petrol/diesel) are a useful template.
• RBA reaction function: watch for emphasis on “second-round effects,” inflation expectations, and ongoing rate moves. The March 17 media conference and March 26 Q&A are explicit that oil-driven price pressures and inflation expectations matter, and that a fuel shortage can become macro-demand negative.
• ABS inflation prints and fuel price components: fuel’s CPI weight is ~3.3%, but it is volatile and can swing headline inflation; ABS also notes fuel price linkages to exchange rates and international refined-product markets.
• AEMO reliability commentary (diesel as a constraint): if diesel scarcity begins to bind for backup generation, that is a “severity amplifier.”

Assumptions and limitations

I make several simplifying assumptions; they materially affect the numbers:

• Linear mapping and simplified factorization: I translate macro shocks into sector returns using assumed ranges; real markets are non-linear and path-dependent.
• No firm-level idiosyncratic shocks: I do not model oil producer windfall taxes, bank-specific funding stress, or company event risk.
• EWA sector weights: I assume sector weights per iShares as of Mar 26, 2026; these weights change over time.
• USD returns modeled via AUD equity + AUD/USD only: in reality, EWA price includes tracking difference, ETF microstructure, and fair-value adjustments.
• Oil-paths are illustrative: the oil-price charts are scenario sketches, not forecasts.
• Correlation regime shift approximated: I explicitly assume higher cross-asset correlation in severe conditions to widen drawdown bounds, consistent with crisis behavior, but I do not estimate correlations from a long historical sample in this report.
• “Domestic shortage” semantics: “running out” is usually localized/functional (distribution and prioritization), not literal zero inventory, as emphasized in government review discussions of localized shortages and allocation priorities.

Best Options Trade? (Ask AI To Produce Best Trades)

EWA Put Options Under a Moderate Iran Oil-Shock Scenario

Executive summary

I parsed your attached EWA options chain (puts and calls side-by-side, with strikes across four expiries) and built a scenario-based, expiry-specific Monte Carlo to evaluate long-put trades under your moderate oil-shock thesis (oil +50% for ~6 months; EWA peak-to-trough ~30% with bounds 23–40%). I used mid prices for premiums and computed per-put days to expiry, implied volatility, Black–Scholes delta, breakeven, short-put premium yields (two conventions), and open-interest notional.

For the scenario mapping, I anchored the underlying at the EWA Closing Price = 27.21 (as of Mar 27, 2026) from the entity[“company”,”BlackRock”,”asset manager”] iShares product page, and used a “mode” terminal level of ~19.05 (= 27.21 × (1 − 0.30)). I treated the 23–40% drawdown band as uncertainty in the scenario mean, implemented via a triangular distribution (min 23%, mode 30%, max 40%). For volatility at each expiry, I used the expiry-specific ATM implied vol from your chain (strike closest to spot, typically K≈27) so that all strikes at the same expiry share one coherent terminal price distribution (rather than mixing strike-by-strike vols into different underlying distributions).

The trade screens you specified are easy to satisfy under a thesis as bearish as “30% down by option expiry,” so I focused the “top 3” not only on expected annualized return and ITM probability, but also on time-to-expiry alignment with a 6‑month shock and observable liquidity (open interest / bid-ask).

Key recommended candidates (all long puts, max loss = premium paid):

• May 15, 2026 K=28 put: higher-delta hedge-like exposure with meaningful OI/volume; strong modeled risk-adjusted profile under the bearish scenario.
• Jul 17, 2026 K=25 put: medium-dated hedge/spec trade that still benefits from a ~30% down scenario while giving the thesis time to play out.
• Jul 17, 2026 K=20 put: a lower-premium “downside capture” strike near the scenario’s mode terminal level (~19), with lower delta but attractive convexity.

Why this scenario is not abstract: entity[“organization”,”Reuters”,”news agency”] reports Brent at $112.57 and +53% since Feb 27 (start of U.S./Israel strikes), and discusses tails up to $200/bbl if conflict persists. Reuters also reports Australian fuel reserve days (e.g., ~39 days petrol, ~30 days diesel/jet) and cancelled shipments. Those are the macro conditions that can plausibly drive a risk-off, inflationary impulse into Australian equities and the AUD, alongside monetary policy responses by the entity [“organization”,”Reserve Bank of Australia”,”central bank”] (cash rate 4.10% after the Mar 17 hike).

Inputs, assumptions, and method

Market inputs I used

• Underlying spot (valuation price): EWA Closing Price = 27.21 (Mar 27, 2026).
• Dividend yield (for delta only): I approximated continuous dividend yield with iShares’ 12m trailing yield = 2.80% (as of Feb 28, 2026).
• Risk-free rate (for delta only): I used 3‑month U.S. Treasury constant maturity = 3.73% (Mar 26, 2026) from entity[“organization”,”Federal Reserve Bank of St. Louis”,”fred publisher”] FRED.
• Contract size: 100 shares/contract (standard U.S. ETF options; your file does not specify otherwise).
• Premium used: mid = (bid + ask)/2 (per your instruction).
• Implied vol (IV): I read IV from your file as an annualized percentage (typical option-chain convention). Where a row showed IV=0% (data artifact), I filled it with the median non-zero IV for that expiry for the purpose of delta estimation.

Derived per-put fields

For every put, I computed:

• Days to expiry (DTE) from valuation date (Mar 27, 2026)
• Mid price (premium per share)
• Annualized implied vol (as provided)
• Black–Scholes delta (approx.) using (S0, K, T, r, q, IV)
• Breakeven at expiry (long put) = K − premium
• Annualized “yield” (short-put convention, two ways):
• premium ÷ strike, annualized by time-to-expiry
• premium ÷ (strike − premium) (cash-secured “capital at risk” proxy), annualized
  I’m explicit: these yield metrics are most interpretable for selling puts, even though the scenario P/L work below evaluates buying puts (max loss = premium).
• Open-interest exposure:
• OI shares = OI contracts × 100
• OI notional (spot) = OI shares × S0

How I mapped your moderate oil-shock thesis into terminal-price distributions

You specified: moderate scenario mode is ~30% drawdown with plausible bounds 23–40%, and suggested a lognormal terminal distribution with sigma taken from implied vol.

I implemented this as:

• Scenario drawdown uncertainty: ( d \sim \text{Triangular}(0.23, 0.30, 0.40) )
• Conditional mean terminal price: ( \mathbb{E}[S_T \mid d] = S_0(1-d) )
• Terminal price distribution: ( S_T \mid d \sim \text{Lognormal}(\mu(d), \sigma^2 T) )
  where ( \mu(d) ) is set so the lognormal mean equals (S_0(1-d)).
• Sigma choice: For each expiry, I used ATM implied vol for that expiry (strike nearest S0, typically K≈27), so all strikes share one consistent underlying distribution at that expiry.

This “ATM-sigma-per-expiry” choice is deliberate: using a different sigma for each strike would imply each option is priced off a different underlying distribution, which is incoherent for scenario analysis.

Why the scenario is plausible in current headlines

I treat your oil-shock as grounded by contemporaneous reporting and policy responses:

• Reuters describes Brent at $112.57, up 53% since Feb 27, with tails up to $200 if conflict extends.
• Reuters describes diesel swap prices in Singapore nearly doubling and cites entity[“company”,”Fortescue”,”australian mining company”] warning about large mining cost impacts from diesel.
• Reuters describes Australia’s import dependence and “days of fuel” metrics (petrol/diesel/jet) and cancelled shipments—i.e., a direct domestic transmission channel.
• The RBA has already had to weigh inflation risks and tightened to 4.10% (Mar 17, 2026), which can amplify equity drawdowns through discount rates and growth expectations.
• Policy mitigation exists, but not a guarantee: the entity[“organization”,”International Energy Agency”,”energy watchdog”] announced a coordinated 400 million barrel release (largest ever) to address conflict-driven disruptions.

Options chain analytics

Put chain coverage and liquidity snapshots

Below is a compact summary of your puts by expiry (counts, strikes, and aggregate open interest). “rel_spread_median” is the median (ask−bid)/mid across strikes, a quick proxy for how “mid-executable” the chain is.

expiry	n_puts	strike_min	strike_max	mid_median	iv_median	oi_total	oi_nonzero	rel_spread_median	dte
2026-04-17	21	17	37	0.425	0.430	756	11	0.625	21
2026-05-15	21	19	39	2.725	0.399	592	2	0.435	49
2026-07-17	21	15	35	1.825	0.309	3308	13	1.407	112
2026-10-16	21	19	39	5.250	0.268	15	5	0.667	203

Interpretation I use in trade selection:

• April has more strikes with OI, but is very short-dated relative to a 6‑month oil shock.
• July is the most practically useful expiry in your chain for a “months-long” thesis because it has the highest concentration of OI in puts.
• October exists but appears thin in open interest in your snapshot (so execution risk is meaningfully higher).

Scenario mapping and simulation results

Terminal-price distributions by expiry under the moderate scenario

The table below summarizes the terminal price distribution I used for each expiry, including the sigma term (ATM implied vol for that expiry) and the resulting distribution of (S_T). The mean is ~18.78 because the triangular drawdown distribution has an average drawdown of ~31% (not 30%); 30% is the mode.

expiry	dte	sigma_term	mean_ST	p5_ST	p50_ST	p95_ST	prob_ST_le_70pct	prob_ST_le_60pct
2026-04-17	21	0.349	18.778	15.880	18.708	21.929	0.573	0.086
2026-05-15	49	0.296	18.783	15.298	18.657	22.711	0.568	0.136
2026-07-17	112	0.309	18.776	13.773	18.478	24.771	0.567	0.243
2026-10-16	203	0.268	18.778	13.090	18.389	25.791	0.568	0.282

Two visuals that matter for strike selection are the terminal density vs strikes and where breakevens land relative to the mass of the distribution.

Ranking puts under the scenario

Per your instructions, for each put I simulated:

• expected payoff and expected P/L (per contract)
• probability of finishing ITM
• expected return (P/L ÷ premium paid)
• expected annualized return (I report a simple time-scaled annualization = exp_return × 365/DTE; this is more interpretable than compounding for very short DTE)
• Sharpe-like metric = expected P/L ÷ stdev(P/L)

Because many strikes have OI=0 in your snapshot, I show rankings for OI>0 below (more relevant to real execution). Full ranked tables are in the downloadable CSVs.

Top by expected annualized return (OI>0):

expiry	strike	dte	mid	bid	ask	open_interest	prob_itm	exp_return	exp_ann_return_simple	sharpe_like	delta	breakeven	rel_spread
2026-04-17	24	21	0.250	0.000	0.500	12	0.995	19.942	346.613	2.739	-0.195	23.750	2.000
2026-04-17	23	21	0.225	0.000	0.450	85	0.984	17.854	310.322	2.221	-0.160	22.775	2.000
2026-04-17	26	21	0.425	0.100	0.750	131	1.000	15.983	277.804	3.690	-0.276	25.575	1.529
2026-04-17	25	21	0.375	0.200	0.550	9	0.999	15.554	270.346	3.170	-0.189	24.625	0.933
2026-04-17	22	21	0.200	0.000	0.400	33	0.954	15.378	267.289	1.756	-0.132	21.800	2.000
2026-04-17	18	21	0.175	0.000	0.350	55	0.352	1.290	22.427	0.746	-0.080	17.825	2.000
2026-07-17	22	112	0.400	0.000	0.800	30	0.813	8.024	26.115	1.187	-0.171	21.600	2.000
2026-07-17	15	112	0.175	0.000	0.350	1364	0.030	-0.242	-0.789	-0.075	-0.056	14.825	2.000
2026-07-17	16	112	0.600	0.000	1.200	1203	0.061	-0.146	-0.476	-0.040	-0.114	15.400	2.000
2026-05-15	19	49	0.050	0.000	0.100	55	0.291	19.210	143.190	0.742	-0.039	18.950	2.000

Top by Sharpe-like (OI>0) tends to favor more reliable, higher-delta puts:

expiry	strike	dte	mid	bid	ask	open_interest	prob_itm	exp_return	exp_ann_return_simple	sharpe_like	delta	breakeven	rel_spread
2026-04-17	30	21	2.850	2.500	3.200	1	1.000	2.941	51.106	4.546	-0.956	27.150	0.246
2026-04-17	29	21	1.975	1.650	2.300	14	1.000	4.178	72.590	4.475	-0.822	27.025	0.329
2026-04-17	28	21	1.200	0.950	1.450	66	1.000	6.676	116.334	4.354	-0.652	26.800	0.417
2026-04-17	27	21	0.800	0.550	1.050	339	1.000	9.268	161.205	4.052	-0.443	26.200	0.625
2026-04-17	26	21	0.425	0.100	0.750	131	1.000	15.983	277.804	3.690	-0.276	25.575	1.529
2026-05-15	28	49	1.600	1.400	1.800	509	1.000	4.756	35.438	3.384	-0.584	26.400	0.250
2026-07-17	29	112	2.300	1.500	3.100	11	1.000	3.447	11.223	2.384	-0.730	26.700	0.696
2026-07-17	31	112	4.250	0.000	8.500	13	1.000	1.867	6.082	2.335	-0.814	26.750	2.000
2026-07-17	30	112	3.400	2.800	4.000	10	1.000	2.300	7.489	2.322	-0.744	26.600	0.353
2026-10-16	38	203	10.950	8.800	13.100	1	1.000	0.759	1.364	2.144	-0.926	27.050	0.393

A quick visual of where expected annualized returns cluster (top 15, OI>0):

Recommended trades and conservative structures

My top 3 “viable” put buys under the moderate scenario

Your viability rules:

• expected annualized return > 20%
• probability ITM ≥ 20%
• max loss limited to premium (true for long puts)

All three below satisfy those screens under the scenario distribution I described. I’m also surfacing bid/ask spread and open interest because they dominate real-world implementability.

Recommended trade details (per 1 contract = 100 shares):

expiry	strike	mid	premium_paid_per_contract	breakeven	delta	prob_itm	exp_pl	exp_return	exp_ann_return_simple	sharpe_like	pl_mode	pl_p5	pl_p95	open_interest	rel_spread
2026-07-17	20	0.250	25.000	19.750	-0.090	0.646	66.322	2.653	8.639	0.838	70.300	-25.000	599.616	220	1.200
2026-07-17	25	0.975	97.500	24.025	-0.289	0.936	432.932	4.440	14.460	1.688	497.800	-76.158	1023.959	365	0.462
2026-05-15	28	1.600	160.000	26.400	-0.584	1.000	761.013	4.756	35.438	3.384	735.300	368.987	1109.556	509	0.250

How I interpret these three:

• Jul 17 20P: a low-premium way to express “terminal lands near ~19”; its success depends on a meaningful drawdown magnitude, not just mild weakness.
• Jul 17 25P: a more forgiving strike; captures downside earlier and is less “all-or-nothing,” but costs more.
• May 15 28P: behaves like a high-delta hedge (already ITM at spot); it’s more “reliable payoff” if the scenario is near-term, but it does not give the full 6 months for the thesis to mature.

“Mode” P/L shown above is computed at S = 19.05 (30% down from spot). The 5th/95th are scenario percentiles from simulation.

Payoff diagrams for the recommended long puts

Conservative alternatives using available strikes

Because buying naked puts can be premium-intensive (especially for higher-delta strikes), I also built two conservative structures using strikes present in your chain:

• Put spread (defined risk, cheaper premium): buy Jul17 25P, sell Jul17 20P
• Collar (for an investor already long EWA shares): long 100 shares, buy Jul17 25P, sell Jul17 29C (near-zero-cost collar in your snapshot)

Modeled results under the same July terminal distribution:

structure	expiry	max_loss_$	max_profit_$	exp_pl_$	pl_p5_$	pl_p50_$	pl_p95_$	prob_profit
Jul17 25/20 put spread (buy 25, sell 20)	2026-07-17	72.500	427.500	351.446	-48.828	427.500	427.500	0.937
Jul17 collar: long 100 shares + buy 25 put (mid 0.975) + sell 29 call (mid 0.900)	2026-07-17	228.500	nan	-220.786	-228.500	-228.500	-228.500	nan

Important nuance on the collar: the collar’s role is loss-limitation, not generating positive expected P/L in a scenario where the stock is expected to be down ~30%. In my simulation, the collar improves expected P/L relative to unhedged stock by hundreds of dollars per 100 shares, but the overall position still loses money if EWA is indeed down sharply.

Payoff diagrams:

Monitoring, exit rules, and limitations

Monitoring triggers tied to the oil-shock thesis

I would track the oil shock along three “gates,” each mapped to why EWA could gap down:

• Oil price level & persistence: Reuters reports Brent at $112.57 and +53% since Feb 27; if Brent stays elevated or spikes again, the inflation impulse and risk-off regime are more likely to persist.
• Diesel/transport stress: Reuters reports diesel swaps in Singapore nearly doubled and highlights miners’ diesel sensitivity, including Fortescue’s statements about cost impacts.
• Domestic fuel availability signals in Australia: Reuters’ reporting on fuel reserve days and cancelled shipments is a direct indicator of whether the shock is staying “macro-only” or becoming a real-economy supply disruption.
• Policy response and rates: the RBA’s hike to 4.10% underscores that monetary policy may tighten into the shock, worsening equity drawdowns through discount rates and demand impacts.
• Mitigation via emergency stocks: the IEA collective action (400 million barrels) is the institutional “shock absorber.” Further releases or expansion may damp volatility, while lack of additional action may signal tightening conditions.

Practical option-trade exit rules I would use

Because long puts are convex and decay with time, I would pre-commit to rules in three dimensions:

• Price-based (EWA):
• Take partial profits if EWA drops through your put breakeven (K − premium) and IV is elevated; keep a runner for tail risk.
• If EWA mean-reverts quickly and the thesis weakens, exit before theta dominates (especially the May expiry).
• Volatility-based:
• If IV expands sharply, consider monetizing even without reaching the full price target (IV expansion alone can drive large gains).
• Thesis invalidation:
• If oil reverses meaningfully and evidence of fuel availability normalization appears (IEA flows, shipping normalization, fewer cancellations), reduce or close positions even if down modestly.

Mermaid timeline linking expiries to scenario milestones

timeline
  title Options expiries vs 6-month moderate oil-shock thesis
  2026-03-27 : Valuation date (EWA close 27.21)
  2026-04-17 : Option expiry (Apr chain)
  2026-05-15 : Option expiry (May chain)
  2026-07-17 : Option expiry (Jul chain)
  2026-09-27 : ~6 months after start (scenario duration marker)
  2026-10-16 : Option expiry (Oct chain, thin OI in snapshot)

Assumptions and limitations

• Not financial advice: this is scenario research and modeling, not a recommendation tailored to your circumstances.
• Scenario compression: I effectively model “30% down by each listed expiry.” If the drawdown is slower (e.g., occurs later than May/July expiries), shorter-dated puts can expire worthless even if the multi-month thesis is right.
• Lognormal simplification: real crisis distributions have skew, jumps, and volatility clustering beyond a lognormal assumption. The Iran crisis can produce gap risk that either helps (down gaps) or hurts (sudden peace headline) long puts.
• Using ATM implied vol for sigma: this is coherent for an underlying distribution, but it may understate tail risk if the surface steepens or if the market is mispricing jump risk. I kept drawdown-mean uncertainty (23–40%) to partly address this.
• Delta is approximate: I used European Black–Scholes with a continuous dividend yield proxy; actual ETF options are American-style, and early exercise is possible (especially deep ITM options near expiry). The delta estimates are therefore directional, not exact.
• Liquidity reality vs “mid”: many strikes show wide relative spreads or one-sided quoting; “mid” may not be executable without slippage. I included rel_spread and OI specifically because they can dominate realized trade outcomes.

My Thoughts

This level of research would have taken me days but AI did it all in a few minutes and it has given me some options trades at the end of all the research. Don’t forget that AI predicted a market correction not too long ago and here we are now but will we actually hit a much larger correction in June or July now?

After several repeated fails I did manage to buy a few put options, ending up overpaying for them because there was so little liquidity in this equity. A lesson I learned here is to stick with equities with liquid volumes market. I’ll write a follow up in May and July on the outcome of these trades.

As always, don’t take anything you read in this post as financial or trading advice and do your own due diligence or get AI to do it for you before you invest in anything!

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