Executive Summary: NOAA – the National Oceanic and Atmospheric Administration, a U.S. scientific agency that studies and predicts changes in weather, climate, oceans, and coasts while managing marine and coastal resources – has officially declared El Niño with a 63% probability of a Super El Niño, marking one of the fastest regime shifts on record and setting up a highly nonlinear global macro shock into 2H 2026 and early 2027. The event interacts with already tight commodity markets and ongoing geopolitical disruptions, amplifying transmission through food inflation, energy systems, logistics bottlenecks, and commodity supply shocks. Emerging markets face asymmetric exposure via inflation, fiscal stress, and external balances, while select sectors benefit from price spikes and benign insurance loss dynamics. The resulting environment creates a high-dispersion credit landscape with pronounced winners and losers across sovereign and corporate risk.

Market Visualizer

SUPER EL NIÑO: THE CLIMATE RISK TRADE

Theme Setup and Probability Skew

• Rapid regime shift: Sea surface temperatures flipped from La Niña conditions in March 2026 to El Niño by June 11, setting a record pace and signaling an aggressive thermal buildup that historically correlates with stronger downstream climate shocks.
• Super El Niño probability: NOAA assigns a 63% probability of ≥+2.0°C by NDJ 2026–27, while some sell side research forecasts a 60% probability of strong-plus events, implying elevated tail risk for severe macro and credit dislocations.
• Subsurface heat reservoir: Temperature anomalies exceeding +6°C below surface levels provide a structural driver of sustained warming, increasing persistence into 1Q 2027 and deepening economic transmission.
• Upper-tail scenario risk: Some models exceed historical intensities seen in the past 80 years, suggesting potential nonlinear impacts that surpass prior episodes such as 2015–16.
• Compounding macro backdrop: Elevated energy prices linked to Iran/Strait of Hormuz tensions increase cost-push inflation risks, reinforcing El Niño’s supply-side shock across EM economies.

Historical Precedent and Structural Differences

• Growth destruction magnitude: The 2015–16 El Niño reduced global output by an estimated $3.9tn over five years, highlighting the medium-term persistence of climate shocks beyond initial weather effects.
• Country-level losses: Peru experienced GDP losses of 6.2–11.6% during past strong events, underscoring sovereign vulnerability in directly exposed coastal economies.
• Commodity disruption track record: The 2015–16 event disrupted ~90kt of copper supply and forced ~300kt aluminium curtailments, demonstrating sensitivity in industrial metals supply chains.
• Food shock evidence: India’s monsoon decline of −13.8% in 2015–16 drove food inflation to 6.2%, illustrating transmission from weather to CPI and policy tightening risks.
• Current cycle tighter starting point: Unlike prior cycles, the 2026 copper market already faces a 300–400kt deficit, amplifying price sensitivity and credit implications from supply disruptions.

Macro Transmission Channels to Credit

• Nonlinear inflation response: Sell side models show inflation more than doubles moving from mild to strong El Niño, with peak effects lagging 4–8 months into H2 2026, creating delayed policy pressure.
• Food CPI transmission: With food comprising 10–35% of EM CPI baskets, any supply shock feeds directly into headline inflation, tightening financial conditions and pressuring sovereign credit.
• Hydro-to-thermal switching: Drought reduces hydro output (e.g., Colombia 60–70% hydro dependence), forcing coal and LNG substitution at higher costs, weakening utility balance sheets and fiscal positions.
• Commodity price amplification: Chile’s 4.2mt copper production (17% global share) faces flood risk, where even a one-week outage (~80kt) materially tightens markets and boosts price-driven earnings volatility.
• Logistics disruption: Panama Canal throughput fell ~29% in 2024 during prior El Niño, indicating global shipping constraints that raise freight costs and delay supply chains, impacting corporate cash flows.

Sovereign Credit Differentiation

• Peru stress case: Direct ocean exposure, fisheries disruption, and historical GDP losses up to 11.6% position Peru as the highest-risk EM sovereign under a super El Niño scenario.
• India inflation risk: With an 84% probability of sub-normal monsoon, food inflation could breach the RBI’s 6% ceiling, driving fiscal slippage via INR500bn+ fertilizer subsidies.
• Colombia hydro vulnerability: Heavy reliance on hydropower combined with drought risks increases electricity prices and adds ~465bps to inflation during strong events, complicating monetary policy.
• Argentina upside skew: Soy yields improve ~+7.7% per degree of warming and maize output can rise ~6mt, supporting GDP (+0.31pp) and fiscal revenues via export taxes.
• Panama risk timing: While 2026 conditions remain strong with $5.7bn revenues, the 2027 dry season presents a key inflection point for canal revenue and sovereign balance implications.

Sectoral and Corporate Credit Impacts

• Agriculture input inflation: Cocoa prices previously exceeded $10,000/t and remain volatile, pressuring margins for processors and driving working capital expansion.
• Hydro utilities stress: EBITDA compression emerges as hydro shortfalls force thermal imports at higher marginal costs, particularly in Colombia, Ecuador, and Zambia.
• Metals upside asymmetry: Aluminium curtailments up to 1.3mt in Yunnan and 2.5mt already offline from Iran disruptions support price upside.
• Insurance earnings boost: Reduced hurricanes (−31%) and storms result in 14–20% lower global insured losses, implying $21–31bn pre-tax benefits for insurers.
• Aquaculture margin pressure: Fishmeal price spikes (~$2,500/mt) increase feed costs (40–45% of production), compressing margins for salmon producers unless offset via pricing.

Executive Summary: This week’s special focus examines the seven-decade trajectory of US-Iran relations, culminating in current tensions following Trump’s naval deployment amid Iran’s domestic protests and ongoing nuclear disputes. Understanding this historical context is critical for assessing sovereign credit risks and regional market volatility.

Market Visualizer

*US-Iran Tensions: 70-Year Timeline & Market Impact*

⚠️ Special Topic: US-Iran Tensions Timeline

US-Iran tensions trace back over 70 years, with granular milestones from the 1953 coup to Trump’s 2026 naval deployment amid Iran’s protests and nuclear disputes.

1950s: Coup and Early Ties

• 1951: Iran’s parliament nationalizes oil from British control, led by PM Mohammad Mosaddegh.
• 1953: US/UK Operation Ajax overthrows Mosaddegh, reinstating Shah Mohammad Reza Pahlavi.
• 1957: US signs nuclear cooperation deal under “Atoms for Peace.”

1960s-1970s: Arms and Revolution Buildup

• 1968: Both nations sign Nuclear Non-Proliferation Treaty.
• 1972: Nixon pledges unlimited conventional arms to Shah.
• 1979 Jan: Shah flees amid Islamic Revolution protests.
• 1979 Feb-Apr: Ayatollah Khomeini returns; Iran becomes Islamic Republic.
• 1979 Nov: Students seize US embassy, taking 52 hostages.

1980s: Hostage Crisis and War

• 1980 Apr: Failed US Operation Eagle Claw kills 8 servicemen; ties severed.
• 1980 Sep: Iraq invades Iran; US backs Iraq despite chemical weapons.
• 1981 Jan: Hostages released after 444 days via Algiers Accords.
• 1984: US labels Iran state terrorism sponsor.
• 1986: Iran-Contra scandal exposed.
• 1988 Jul: USS Vincennes downs Iran Air 655, killing 290.

1990s-2000s: Sanctions and Nuclear Revelations

• 1995-96: Clinton imposes oil/trade bans, Iran-Libya Sanctions Act.
• 2002 Jan: Bush’s “axis of evil” speech.
• 2002 Aug: Natanz, Arak sites revealed.
• 2003: Iran suspends enrichment, allows IAEA inspections.
• 2007: US intel says Iran halted weapons program in 2003.
• 2009: Fordow enrichment site exposed by US/UK/France.

2010s: JCPOA and Withdrawal

• 2013: Obama-Rouhani call; interim Joint Plan of Action signed.
• 2015 Jul: JCPOA limits Iran to 3.7% enrichment, 300kg stockpile.
• 2018 May: Trump withdraws, restores sanctions.

2020s: Strikes, Talks, Protests

• 2019: IRGC designated terrorist group.
• 2020 Jan: US drone kills Qasem Soleimani; Iran missiles US bases.
• 2021: Biden Vienna talks fail.
• 2023: Prisoner swap releases $6B Iranian funds.
• 2025 Feb-Jun: Trump demands new deal; US/Israel strike Fordow/Natanz/Isfahan; Iran hits Qatar base.
• 2025 Dec: Protests erupt over currency collapse, demanding Khamenei ouster.
• 2026 Jan: Crackdown kills ~6,000, arrests 26,000+; Trump deploys USS Abraham Lincoln fleet to Gulf, threatens action.

DISCLAIMER

This document is strictly confidential and is being provided to you for informational purposes only. It does not constitute an offer to sell or a solicitation of an offer to buy any securities or financial instruments. The information contained herein has been obtained from sources believed to be reliable but is not guaranteed as to accuracy or completeness. Opinions and estimates constitute our judgment as of the date of this material and are subject to change without notice.

AI Generation Notice: Portions of this content may have been generated or assisted by Artificial Intelligence (AI) technologies. Users should independently verify critical data points. Past performance is not indicative of future results.

2026 Strategy

AI-led insights into emerging market credit

1. Illustrative 2026 Political Risk Calendar

Note: Election dates reflect official commissions as of January 11, 2026.

2. Sovereign Liability Management and the Restructuring Cycle

The “Sweet Spot” for tactical opportunity lies in B and CCC-rated sovereigns that completed restructurings between 2023-2025.

3. The Structural Shift: EV Transition and Tech Debt

EV Market Adoption

Global plug-in sales reached nearly 22 million units in 2025 (~25% market share). China notably exceeded a 50% EV sales share in late 2025.

The Oracle Proxy: Tech Debt Risk

Tech sector bond supply remains high. Oracle (ORCL) is the primary risk proxy for AI-driven leverage:

4. Revised Commodity Bull Cycle (2025-2026)

Projections reflect 2025 realized peaks and updated January 2026 house forecasts.

5. Geopolitical Spheres: The “Trumpian” Influence

6. Regional Real Estate and Corporate Resilience

Hong Kong Property

Shifting to a “volume-led” recovery. Developers face 27,000 unsold units, but rental yields for small units (Class A) have stabilized near 3.7%.

Brazil Corporates

Despite sovereign fiscal rigidity, JBS, Vale, and Itaú show robust fundamentals. Approximately 60% of Brazilian corporate debt remains Selic-linked, making 2026 rate cuts a primary catalyst for credit rotation.

The December Paradox: Structural Seasonality in EM Sovereign Credit

Executive Summary
The prevailing market narrative regarding Emerging Market (EM) credit suggests that December is a “soft market”—defined by widening bid-ask spreads and a cessation of price discovery. However, a forensic analysis of nearly two decades of data from the iShares J.P. Morgan USD Emerging Markets Bond ETF (EMB) indicates this hypothesis is inconsistent with historical volume and volatility patterns [1, 2, 3]. The data reveals that while liquidity often thins, this scarcity frequently amplifies, rather than dampens, directional volatility [13].

1. Liquidity Analysis: Crisis vs. Normalcy

Historical volume analysis challenges the assumption of a dormant December market. While the “Ski Lodge” theory suggests a universal decline in activity, the data reveals a bifurcated liquidity landscape depending on the macro environment [15]. In “Normal Years” (such as 2019, 2022, 2023, and 2024), trading volume does indeed contract by approximately 10-40% relative to the January-November average [17].

Figure 1: December Liquidity – Drought or Flood? Analysis highlights the volume disparity. While benign years see volume drops, crisis years record massive spikes: +116% in 2008, +49% in 2014, and +53% in 2018.

However, during stress events, the “holiday” effect is negated. In 2008, following the global financial crisis, December volume spiked 116%. Similarly, during the 2014 oil shock, volume increased by 49%, and during the 2018 Fed tightening scare, it rose by 53%. This indicates that during regime changes, liquidity is available but comes at the cost of widened spreads due to high turnover.

2. Volatility Skew: The Cost of Liquidity

The relationship between market depth and price action in December exhibits high convexity. Quantitative analysis shows that while average liquidity depth decreases by roughly 18% (48M shares vs. a 59M average), the resulting returns are approximately 3x higher than the annual monthly average (1.28% vs 0.36%). This inverse relationship aligns with broader academic findings on liquidity constraints [12, 14].

Figure 2: The December Paradox Mechanism. The data illustrates an inverse correlation between liquidity depth and price impact. Thinner markets in December (Liquidity Dip ~18%) coincide with significantly higher average returns (1.28%), indicating that order flow has an outsized impact on price.

This “thinner market” dynamic means price discovery must travel further to find equilibrium. In 2014, this resulted in a price crash where the ETF opened at 66.87 and fell to a low of 62.08—a decline of over 7% in a single month.

3. “Window Dressing”: A Conditional Phenomenon

The data suggests that “Window Dressing”—the practice of bidding up assets at year-end—is highly correlated with the prevailing trend rather than serving as an independent catalyst [4, 6]. In bull markets, momentum tends to accelerate; in bear markets, it fails to materialize [5].

Figure 3: The Window Dressing Trap. Performance data contradicts the reliability of the “Santa Rally” in down years. While 2008 saw a +17.2% rally and 2023 a +4.2% gain, bear market Decembers saw capitulation: -3.3% in 2014, -2.0% in 2015, and -1.7% in 2022.

As shown in Figure 3, the divergence is stark. In positive momentum years like 2023, December delivered a +4.2% return. Conversely, in years characterized by fundamental stress, such as 2014 and 2022, prices collapsed. This decline is likely exacerbated by tax-loss harvesting rather than window dressing, as investors sell losing positions to offset gains [8, 9, 11].

4. Long-Term Trajectory

Despite seasonal volatility, the long-term trend of the asset class remains constructive. The EMB ETF has recovered from a Global Financial Crisis low of $32.23 to reach an all-time high of $96.15 in November 2025 [1, 3].

Figure 4: EMB: The Long Ascent (2007-2025). The structural recovery from 2008 lows to 2025 highs (+200% recovery) highlights the long-term beta of the asset class, despite the recurrence of Q4 volatility.

Analytical Observations

Based on the statistical review of the 2007-2025 dataset, the following structural patterns emerge:

• Volume Inversion: While November typically exhibits higher volume than December in the post-2016 era, this trend inverts during crisis years [16].
• The January Effect Correlation: Data indicates a zero-sum relationship between December and January returns. Strong Decembers are often followed by flat or negative Januarys, while weak Decembers often precede January rebounds [18, 19].

5. Historical Performance Matrix

The heatmap below provides a granular visualization of the month-over-month percentage change for the EMB ETF over the past decades. This matrix highlights the seasonal clustering of volatility, particularly showcasing how December returns swing violently during crisis years (e.g., the +17.2% rebound in 2008 versus the -3.3% drop in 2014).

Figure 5: Historical Monthly Returns Matrix. A color-coded view of monthly performance, allowing for rapid identification of outliers and seasonal trends.

AI Methodology & Risk Statement
This document is primarily generated by an artificial intelligence (AI) system for informational purposes. While Gembridge Capital utilizes advanced models to synthesize and analyze market data, AI systems are susceptible to “hallucinations”—the generation of plausible but factually incorrect or misleading information. Users should be aware that the analysis herein may contain unverified assertions or errors. This report serves as an automated market monitor and should not be relied upon as a primary basis for investment decisions without independent verification.

Gembridge Capital Management

Important Disclosures: The data presented is derived from historical analysis of the iShares J.P. Morgan USD Emerging Markets Bond ETF (EMB). This report does not constitute financial advice, a recommendation to buy or sell any securities, or a solicitation of any offer. Gembridge Capital makes no representation or warranty of any kind, express, implied or statutory regarding this document or any information contained or referred to in the document. Past performance is not indicative of comparable future results. All statistics cited are based on the provided dataset spanning 2007-2025.

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