2026 Q2 Market Review

The second quarter of 2026 was shaped by three forces: a change in Federal Reserve leadership, an easing of Middle East tensions, and a surge in AI infrastructure spending that reinvigorated equity markets. Global equities closed the quarter with strong gains despite significant volatility, while fixed income delivered modest, resilient returns amid elevated yields.

The Three Pillars of Q2 2026 Market Dynamics

Geopolitical De-escalation (April): The quarter opened with a conditional U.S.–Iran ceasefire and steps to reopen the Strait of Hormuz. This agreement lowered the geopolitical risk premium, sending crude oil prices down sharply and lifting global equities and risk assets.

The Federal Reserve Transition (May): Kevin Warsh was confirmed as Federal Reserve Chair, succeeding Jerome Powell. The leadership change introduced uncertainty around Fed communication, central bank independence, and the path of interest rates.

The Hawkish Pivot & Economic Resilience (June): A stronger-than-expected May jobs report forced markets to recalibrate rate expectations. At Chair Warsh’s first FOMC meeting, the Fed held rates steady but dropped its easing bias, and a meaningful number of policymakers now project a rate hike later in 2026.

International Central Bank Divergence

International central banks also tightened policy. The ECB raised its deposit rate 25 basis points to 2.25% in June on broadening energy-driven inflation, while cutting its 2026 Eurozone GDP growth forecast to 0.8%. The Bank of Japan lifted its policy rate to roughly 1.0% — its highest in decades — continuing policy normalization amid persistent domestic price pressures. The Bank of England held its benchmark rate at 3.75% but turned more hawkish as committee dissent grew.

U.S. Equity Markets: Large-Cap Resilience

U.S. large-cap equities posted strong gains: the Russell 1000 Index rose 15.14% in Q2, lifting its year-to-date return to 10.33%. Growth reasserted leadership over value — the Russell 1000 Growth Index gained 16.74% versus 13.87% for the Russell 1000 Value Index, a 287-basis-point gap. That marks a reversal from the trailing 12-month trend, where value still leads growth by 941 basis points (27.12% vs. 17.71%). Nine of eleven sectors posted positive returns:

  • Information Technology (+32.47%): Tech drove domestic performance, contributing 989 basis points to the index’s 15.13% return, as demand for semiconductors and hardware accelerated with hyperscale AI capex. Semiconductors & Semiconductor Equipment returned 52.63% for the quarter, contributing 693 basis points on a 16.30% average index weight.

  • Industrials (+15.20%): Infrastructure builds, electrical equipment manufacturing, and data center construction drove outperformance, led by Electrical Equipment (+28.18%) and Construction & Engineering (+27.95%).

  • Energy (–13.13%): Energy was the largest drag on the index (–54 bps) as the post-ceasefire oil price collapse compressed upstream margins; Oil, Gas & Consumable Fuels fell 13.54%.

Small-Cap Outperformance Breakdown

Small caps significantly outpaced large caps. The Russell 2000 Index rallied 21.49% in Q2, lifting its trailing one-year return to 40.78%. The rally was even stronger in micro-caps: the Russell Microcap Index gained 25.63%, lifting its trailing 12-month return to 58.55%. Within micro-caps, value nearly matched growth — the Russell Microcap Value Index returned 23.20% versus 28.98% for growth. The Russell 2000’s heavy weighting toward secondary technology suppliers, equipment manufacturers, and emerging biotech drove the rally, with capital concentrated in energy infrastructure, micro-semiconductor manufacturing, and automation:

  • Small-Cap Information Technology (+55.40%): Outperformed its large-cap counterpart as markets re-rated mid-tier hardware and network infrastructure companies.

  • Small-Cap Health Care (+25.07%): Clinical-stage biotech and specialized service names gained amid broad risk-on flows.

  • Small-Cap Energy (–10.04%): The only sector with deeply negative returns, acting as a 61-basis-point drag on the index.

Micro-Cap Capital Structure and Leverage Profiles

A key theme in micro-caps was how highly leveraged balance sheets responded to rate expectations. Despite the Fed’s hawkish shift, stabilizing growth triggered a short squeeze in heavily indebted names: companies with the highest debt-to-equity ratios posted the strongest returns, aided by operational leverage and falling default-risk premiums.

International Developed Markets: Europe and Japan Policy Divergence

Developed international equities gained, but trailed U.S. benchmarks. The MSCI EAFE Index returned 11.08%, 406 basis points behind the Russell 1000. Style trends mirrored the U.S.: the MSCI EAFE Growth Index rose 14.68% versus 7.84% for value. Over the trailing 12 months, however, international value still leads growth by 1,376 basis points (27.75% vs. 13.99%).

The Eurozone faced continued pressure from early-2026 energy shocks, which weighed on corporate margins and consumer demand. The April ceasefire briefly eased energy prices, but headline inflation stayed sticky and broadened across services and industry. In response, the ECB raised its deposit rate 25 bps to 2.25% in June, cut its 2026 GDP growth forecast to 0.8%, and projected full-year inflation near 3.0%. Higher borrowing costs combined with slower growth drove sharp performance dispersion across the region.

Japan led developed-market performance. The MSCI Japan Index rose 14.24%, a 23.31% weight within EAFE. The Bank of Japan raised its policy rate to roughly 1.0% in June — its most decisive policy shift in decades — after lifting its inflation outlook earlier in the quarter. Markets read the move as confirmation of durable inflation and domestic demand rather than a shock. A weak yen further supported corporate profitability by boosting export competitiveness for automakers, machine-tool builders, and semiconductor suppliers.

Emerging Markets: Broad-Based Asset Allocation

Emerging market equities led all major regions. The MSCI Emerging Markets Index surged 24.15% in Q2 — 1,307 basis points ahead of developed international markets and 901 basis points ahead of U.S. large caps — lifting its year-to-date return to 24.02% and trailing 12-month return to 44.18%. Growth and value converged: the MSCI EM Growth Index rose 26.50% versus 21.81% for value. The rally was driven by capital rotating into East Asian (South Korean KOSPI Index +54.7%) hardware supply chains and Latin American commodity exporters, aided by easing Middle East tensions, which lowered shipping costs and boosted export margins for manufacturing hubs.

Fixed Income Markets: High-Quality and High-Yield Performance

High-quality fixed income delivered modest gains despite crosscurrents. The Bloomberg U.S. Aggregate Bond Index returned 0.67%, supported by stable credit spreads and income that offset Treasury yield volatility. Longer-duration bonds outperformed: securities with maturities of 10 years or more beat the broader index, aided by institutional demand and steady inflation expectations.

Within investment-grade credit, lower-rated issues outperformed — A- and BBB-rated bonds beat the benchmark, while AAA and AA lagged, reflecting steady fundamentals and demand for yield. Investment-grade spreads tightened 4 basis points, from 30 to 26.

High yield fully rebounded from Q1 losses: the Bloomberg U.S. Corporate High Yield Index returned 2.47%. Spreads had widened to one-year highs in late March amid regional conflict and shipping-disruption fears, then tightened steadily after the April ceasefire, compressing 54 basis points from 317 to 263. Risk appetite favored lower-quality credit — CCC-rated bonds returned 3.40% versus a combined 2.40% for BB/B-rated issues. The asset class saw net inflows, and rating upgrades and downgrades stayed balanced. New issuance reached $105 billion, increasingly directed toward M&A and strategic capital projects rather than refinancing.

Broad Commodities Complex: Supply-Side Elasticity Meets Easing Risk Premiums

Commodities saw sharp intra-quarter swings that gave way to broad-based adjustment. The S&P GSCI and Bloomberg Commodity Index returned +14.4% in aggregate, masking wide divergence between industrial metals, agriculture, and energy. Despite the Fed’s hawkish shift, structural demand from AI infrastructure and grid modernization put a floor under critical raw materials.

Energy was the most volatile component. The quarter opened with maritime traffic through the Strait of Hormuz effectively halted, pushing crude above $100 per barrel and prompting talk of $200 oil. The April ceasefire reversed the risk premium, and front-month prices fell back to roughly $70 by late June. Avoiding worse outcomes required heavy drawdowns of commercial inventories, while China cut crude imports by roughly 50% between February and June, helping stabilize global pricing. Shipping through Hormuz recovered to about half of prewar volumes by quarter-end. Despite the late-quarter retreat, crude still finished the period up 53.9%.

Industrial metals — copper, aluminum, and specialty alloys — rose a steadier 11.8%, driven by hyperscaler capex on grids, microgrids, and data centers. Tightening mining and smelting constraints kept copper supply constrained.

Precious metals corrected sharply after a multi-quarter rally: spot gold fell 7.0% and spot silver fell 17.0%. Both were technically overbought entering Q2; the April de-escalation reduced hedging demand, prompting funds to rotate proceeds into equities. Central bank tightening compounded the pullback:

  • The Federal Reserve Effect: Under Chair Warsh, the Fed held rates at 3.50%–3.75% while signaling a strong bias toward a later-2026 hike, pushing real yields — and the opportunity cost of holding metals — higher.

  • Global Monetary Tightening: The ECB’s rate hike to 2.25% and the BoJ’s normalization to roughly 1.0% lifted nominal yields globally, reducing bullion’s relative appeal and shifting capital from metal ETFs into sovereign debt.

  • Long-Term Support Mechanisms: Gold and silver still show strong trailing one-year returns, supported by central bank buying — particularly emerging-market reserve diversification — and robust industrial silver demand from solar and electronics manufacturing.

Digital Assets & Cryptocurrency

Digital assets faced heavy pressure from fading institutional inflows, thinning liquidity, and futures-market liquidations. Spot Bitcoin ETF demand, which had driven gains through late 2025 and early 2026, softened by mid-2026 as tighter monetary conditions prompted outflows, exposing a highly leveraged market structure. Three overlapping pressures accelerated the drawdown:

  • ETF Outflow Pressures: Sustained redemptions forced institutional desks to liquidate spot holdings, removing a key source of buying support.

  • Basis Trade Compression: As futures premiums narrowed, arbitrage desks unwound multi-billion-dollar basis trades, adding automated spot-selling pressure.

  • Derivatives Liquidation Cascades: High leverage magnified declines; breaches of key technical levels triggered margin calls and forced hundreds of millions of dollars in position closures.

Private Assets — Equity / Credit / Real Estate

Private markets held up despite higher borrowing costs, tighter debt availability, and slower exits. Fundraising stayed concentrated among top-tier managers.

Venture capital diverged sharply between general technology and AI infrastructure. North American startup funding set records in the first half of 2026 on AI-related investment, with late-stage deals carrying high valuations and concentration risk. Anthropic approached a $1 trillion valuation, surpassing OpenAI, after a large corporate funding round. Exit activity improved: SpaceX’s IPO closed up 19%, one of the largest venture-backed debuts on record, and Eli Lilly’s acquisition of Kelonia marked one of the largest funded venture exits in years.

Private credit allocators stayed active but raised due-diligence standards. Direct lending faced scrutiny over interest coverage amid higher-for-longer rates, though the broader private credit market remained structurally sound, with capital shifting toward asset-backed and infrastructure financing that offers contractual income and downside protection.

Commercial real estate saw slower new development as construction costs and bank credit tightened, which supported existing assets as landlords passed costs through via rent increases. Capital-constrained sponsors increasingly used structured secondaries, creating entry points for well-capitalized investors. Rental housing, student housing, and medical facilities continue to offer stable, low-beta income.

Infrastructure Scarcity and the Limits of AI Optimism

The macro backdrop is pulling in two directions. Equity markets are priced for fast, broad AI-driven productivity gains, while the physical economy shows constrained power grids, bottlenecked chip supply chains, rising geopolitical friction, and expanding sovereign debt. This section attempts to separate the capital narrative from capital reality across five themes:

  1. Compute is a physical, scarce resource — bottlenecked by GPUs, fabrication capacity, memory, and power — not an infinite digital one.

  2. AI-driven margin expansion is real but currently confined to technology; the other roughly 493 S&P 500 companies face a multi-year lag before efficiency gains reach earnings.

  3. Markets expect AI to push the neutral rate higher; the evidence points toward a lower near-term neutral rate.

  4. A structural U.S. primary deficit near 3% of GDP, combined with rising real rates, is building toward a debt spiral that AI growth alone cannot resolve.

  5. Japan illustrates where an unaddressed debt trap leads, and the yen has further room to fall.

Compute Scarcity: The Physical Limits of AI

Markets have largely treated AI as a software story that scales in the cloud at near-zero marginal cost. That misreads the current cycle: computational power is now the defining physical input of this economy, and the key question is whether the world can extract enough raw materials, build enough hardware, and generate enough power to meet demand. The shift isn’t only about more users — AI workloads themselves have changed. Early large language models answered a single prompt in a single pass. Today’s agentic and deep-reasoning systems break problems into steps, run multiple searches, iterate on code, and check their own work — consuming 100 to 1,000 times more “compute” than a standard chatbot query. Hardware supply is expanding at an historic pace, but demand is growing faster still.

Four constraints are converging:

  1. GPU scarcity — frontier-chip capacity is effectively sold out across major cloud providers, and older hardware commands lease rates not seen since the 2024 buildout.

  2. Fabrication limits — leading foundries such as TSMC are booked for multiple quarters, leaving no room for short-term supply response.

  3. Component shortages — high-bandwidth memory and advanced networking switches remain scarce.

  4. Power constraints — data centers need continuous power at a scale legacy grids weren’t built for; power, not chips, is increasingly the binding constraint.

This scarcity is reshaping competitive behavior. Hyperscalers such as Google are locking in multi-year capacity commitments years ahead of delivery, and software rivals are forming infrastructure partnerships out of necessity — both Google and Anthropic have signed agreements to lease compute from xAI’s clusters. For investors, the more reliable risk-adjusted returns this cycle likely come from owning the physical bottlenecks — semiconductors, copper, turbine and generator suppliers — rather than betting on which software layer wins.

The ROI Mismatch: Valuations Ahead of Cash Flow

Equity valuations already price in broad AI-driven earnings upside that the underlying data doesn’t yet support. Outside a narrow group of technology and software names, there’s little evidence of margin expansion across the rest of the S&P 500. Current valuations assume the other roughly 493 companies will catch up; a slower catch-up than the market expects could trigger a repricing.

Source: Apollo Global Management

The constraint is the length of the ROI runway. Software companies can integrate AI into cloud-native systems almost immediately — that’s the exception. Across more than 80% of global GDP — healthcare, banking, insurance, manufacturing, logistics, education, defense, real estate, legal services, and government — realizing productivity gains requires rebuilding data infrastructure and reworking processes, which takes years.

 

Source: SemiAnalysis

One indicator worth watching is the price of compute tokens. A rapid decline toward zero — driven by more efficient inference and open-source competition — would be deflationary for software and could mean cloud providers’ capital spending outpaces what the market can monetize, even as total compute demand keeps rising. The current focus on token efficiency and local computing itself suggests AI adoption may be running slower than consensus assumes. This sits in some tension with the compute-scarcity case made above: if inference is getting cheaper quickly, that eases the physical bottleneck even as it pressures software-layer economics. We think both can be true at once — training and frontier-model compute stay scarce and capital-intensive, even as inference on already-trained models gets cheaper — but the two dynamics cut in different directions for different parts of the value chain, which is a qualifier to the bottleneck thesis worth keeping in view, not a contradiction of it.

Governance Risk: AI Dependence Inside Portfolio Companies

A related risk is emerging at the level of individual firms: how executives use, and potentially over-rely on, AI in decision making. Behavioral economics traditionally describes judgment through two systems: fast, intuitive “System 1” thinking and slower, deliberate “System 2” analysis. Recent Wharton School research (Shaw & Nave, 2026) proposes a third — “System 3,” AI functioning as an on-demand cognitive partner outside the human brain. Used well, this extends what a decision-maker can do. The risk the authors identify is “cognitive surrender”: adopting AI output with minimal scrutiny, overriding one’s own intuition and deliberation. Because AI systems produce fluent, confident answers, they carry an air of authority that suppresses the skepticism that would normally trigger reviewi.

Two caveats limit how directly this maps onto executive decision-making, and the study’s own authors flag the first: the experiments used an abstract reasoning task (a modified Cognitive Reflection Test) completed by university lab and online panel participants, not executives making pricing, forecasting, or risk calls inside real companies — so the effect’s existence is well established, but its magnitude in a corporate setting is unproven. Second, because incentives and fast feedback measurably reduced (rather than eliminated) the effect, the practical implication isn’t that AI reliance is unmanageable — it’s that accountability structures matter and should be built deliberately into how a management team uses AI, rather than assumed away.

With that qualification, the underlying risk is real: a management team that treats AI output as a substitute for scrutiny, rather than an input to it, can look articulate and confident right up until something breaks. We factor this into how we assess management quality and look for evidence that leadership retains and exercises independent judgment rather than defaulting to AI.

Monetary Policy: Questioning the “Higher r*” Narrative

Some policymakers and strategists argue that AI-driven productivity growth should permanently raise the long-run real neutral rate (r*): faster growth means less household saving and more business borrowing, both of which push rates up. The usual comparison is the late-1990s productivity boom, which coincided with a rise in neutral rates. r* is the theoretical policy rate consistent with full employment and stable inflation — the benchmark for judging whether policy is tight or loose2. The data doesn’t clearly support a higher r*: long-dated Treasury yields have compressed, not risen, around major AI model announcements, and market-implied neutral-rate estimates have moved lower over the same period. That evidence is suggestive rather than conclusive — it’s a correlation over a narrow set of event windows that overlapped with a Fed leadership transition and shifting geopolitical risk premia, both of which independently move yields, so isolating an AI-specific effect is harder than the headline pattern implies. Two mechanisms could still explain a genuine AI-linked effect:

  1. A permanent productivity gain would strengthen a sovereign’s future tax base, giving bondholders an option-like claim on that growth. That would compress the term premium and lower yields — Fed modeling attributes roughly half of the recent long-term yield decline to this effect, though this is a model-based estimate rather than a directly observed one. Note this channel is in some tension with our own view elsewhere in this letter that measurable productivity gains remain confined to a narrow slice of the economy; to the extent that’s true, the tax-base channel is weaker than the modeling implies.

  2. Near-term AI-driven disruption — labor market adjustment, corporate restructuring — raises uncertainty, which increases precautionary saving and pushes capital toward safe assets, also compressing yields.

Net effect: AI may lift growth over decades, but its near-term effect is more likely to raise demand for safe assets and lower the real neutral rate than to push it higher — a view we hold with real uncertainty given the identification challenge above.

U.S. Sovereign Debt: A Fiscal Trap Without an Easy Exit

U.S. public debt has grown faster than nominal GDP for most of this century. Debt-to-GDP ratios used to stabilize during expansions even as they rose in downturns; that pattern has broken down. The current structural primary deficit — the deficit excluding interest — is roughly 3% of GDP, a level with no precedent outside wartime or deep recession, occurring during low unemployment and steady growth3.

For over a decade after 2008, g comfortably exceeded r, making persistent deficits tolerable. That gap has narrowed sharply as real rates rebounded, and current projections predict borrowing costs overtaking GDP growth within roughly three years — though the exact timing is sensitive to the growth and rate assumptions fed into the model and shouldn’t be read as a precise date.

Debt levels matter because a larger debt stock amplifies any r-g gap. Two feedback loops compound the problem: rising issuance crowds out private investment, slowing growth, and each additional point of debt-to-GDP is estimated to raise long-term real rates by roughly 2 basis points, further raising the interest burden. Closing the gap requires a roughly 2.6-percentage point-of-GDP reduction in the primary deficit — about $840 billion a year, or $10.5 trillion over a decade. An immediate fix would require either a 15% across-the-board tax increase or a 13% cut to all federal spending; protecting Social Security and Medicare would require cutting non-entitlement programs — defense, research, infrastructure — by roughly 57%. Given Congress’s limited recent record on fiscal consolidation, an orderly resolution looks unlikely.

 

Source: Center for American Progress

A common counterargument is that AI-driven growth will simply outgrow the debt. We think this overstates what technology can do. Major technological breakthroughs tend to raise the level of productivity during adoption, not permanently accelerate its growth rate — the 1990s internet boom is the template: productivity growth accelerated for roughly a decade, peaked around 2000, then reverted to trend. Even under optimistic adoption scenarios, faster automation would require substantial new social-safety-net spending to manage labor displacement, offsetting much of the fiscal benefit.

We treat this fiscal instability as a structural baseline, not a tail risk, and continue positioning client portfolios in high-quality credit, short-duration liquid instruments, and real assets with independent pricing power.

Japan: A Live Template for a Sovereign Debt Crisis

Japan shows where an unaddressed debt trap leads. Consensus has treated the yen’s depreciation as orderly and policy-managed; we think that misreads a slow-moving crisis as stability. Japan’s general government gross debt exceeds 240% of GDP, the highest of any major economy. To keep the interest burden manageable, the Bank of Japan has capped long-term bond yields — which prevents Japanese yields from reflecting the risk international investors assign to Japanese debt. With little incentive to hold yield-capped yen assets, capital has flowed out, forcing repeated large-scale FX interventions by the Ministry of Finance and BoJ to support the yen.

 

Source: Haver Analytics

These interventions treat the symptoms, not the underlying debt problem, and are losing effectiveness — the intervention in late April 2026 was notably weaker than a comparable move in January. Adjusted for real rate differentials against G10 peers, the mismatch is stark: Japan carries the highest debt load of any major economy, yet its 30-year yield trades close to Germany’s, a country with a far stronger fiscal position. That’s unsustainable and points to continued yen depreciation, with USD/JPY positioned to test and potentially break through 170. We hold that as a directional view rather than a precise target — FX forecasts carry wide uncertainty bands at any horizon — but a move through that level would risk reverberations across global financial markets.

Portfolio Construction

A traditional portfolio built on passive equity exposure and long-dated nominal bonds assumes steadily expanding margins and a stable sovereign balance sheet. We think that assumption no longer holds and structure client portfolios accordingly: keeping public fixed income short in duration to limit exposure to sovereign balance sheet risk, while diversifying into real assets with independent pricing power — core logistics infrastructure and precious metals. We hold the metals allocation for its structural, multi-year role as a hedge against fiscal and currency debasement risk, not as a near-term tactical call: the hawkish rate backdrop described earlier in this report is a genuine headwind to bullion in the coming quarters, and we’d expect this position to lag if that backdrop persists. These are disruptive transitions that will keep breaking the historical correlations passive portfolios rely on. For investors willing to set aside the speculative narrative and anchor positioning in the physical realities of resource scarcity, fiscal arithmetic, and behavioral risk, we believe this environment offers a genuine opportunity to build durable, non-correlated real return portfolios. We remain grateful for your trust in navigating it on your behalf.

Conclusion & Outlook: Navigating the Second Half of 2026

Heading into the second half of 2026, investors face steady economic growth, shifting central bank policy, and major technology infrastructure trends. Markets will watch Chair Warsh’s Fed for signs on the timing and likelihood of a rate hike later this year and will need to adapt to a new monetary regime as central banks navigate persistent inflation and diverging growth. AI and hardware companies have driven much of the market’s return, but investors are growing more selective — future leadership will depend on companies showing clear revenue growth, sustainable pricing power, and real returns on AI infrastructure spending. Tight investment-grade and high-yield spreads suggest credit markets are pricing in a constructive economic environment. We will continue to closely monitor any shifts in this narrative as the year progresses.

We at Twelve Points Wealth hope you and your families are well and have an enjoyable Summer. Please call or email if you have any questions.

Steve

Steve Bruno, CFA

Chief Investment Officer

July 10, 2026

iIn a preregistered study of 1,372 participants across three experiments (9,593 trials), participants solved reasoning problems with optional access to an AI assistant whose accuracy was randomized and hidden. The pattern showed up consistently: accuracy rose sharply when the AI was correct (roughly 70–75% versus a no-AI baseline near 46%); accuracy fell to roughly 14 to 15 percentage points below the no-AI baseline when the AI was confidently wrong; and participants grew roughly 12 percentage points more confident in their own answers after consulting the AI, regardless of whether it was right — so confidence rose fastest exactly when accuracy was falling.

The pattern held under time pressure, which suppressed independent reasoning and pushed participants toward whatever the AI said. Financial incentives paired with real-time accuracy feedback meaningfully reduced it — override rates on incorrect AI answers roughly doubled, from about 20% to 42%, but did not eliminate it: participants who relied heavily on the AI still did markedly worse when it erred than when it was accurate, even when paid for correct answers. Higher self-reported trust in AI predicted greater susceptibility; stronger critical-thinking disposition and higher fluid intelligence predicted more resistance. A related pattern has been documented in medicine, where repeated deference to AI-assisted diagnosis has been linked to erosion of unaided diagnostic skill among endoscopists.

 

Notes

  1. Data Sources: All quantitative financial metrics, index performance figures, and sector attribution are derived from FactSet Research Systems Inc., MSCI Inc., and Bloomberg Index Services Limited as of June 30, 2026.

  2. Since it can’t be observed directly, we proxy it with the 5-year, 5-year-forward rate:

    5y5y forward rate = E[future short-term rate] + term premium + inflation premium

  3. Debt sustainability depends on three variables: the primary deficit, the existing debt stock, and the gap between the interest rate on debt (r) and nominal growth (g):

    ΔDₜ = [(r − g) / (1 + g)] × Dₜ₋₁ + PBₜ

    where D is debt-to-GDP and PB is the primary balance.

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