Generated April 2026 from current fund data.
Overview
AGG, BND, LQD, and VCIT are all investment-grade bond ETFs, but they carve out different slices of the fixed-income market. AGG and BND track the broad U.S. aggregate bond index (treasuries, corporates, and mortgage-backs combined), while LQD focuses exclusively on liquid investment-grade corporate bonds and VCIT narrows further to intermediate-term corporates. The key distinction: broad diversification versus targeted corporate exposure, and the yield-versus-volatility tradeoff that comes with it.
How they differ
AGG and BND are near-twins—both track aggregate bond indices with identical 0.03% fees and 3.97–4.00% yields—but BND is substantially larger ($387 billion AUM vs. $139 billion) and uses a float-adjusted index, which slightly tilts holdings toward larger issuers. LQD isolates corporate bonds only, lifting its yield to 4.61% and its beta to 1.34 (more interest-rate sensitive than the broad indices' ~0.99 beta), but charging 0.14% in fees. VCIT splits the difference: it's corporate-focused like LQD, but intermediate-term only, delivering a 4.79% yield and a lower 1.07 beta, with AGG/BND's rock-bottom 0.03% fee structure.
The yield progression—from AGG/BND at ~4% to LQD at 4.61% to VCIT at 4.79%—reflects credit and duration risk: more corporates and shorter maturities generally compress duration and boost yield, but at the cost of higher price sensitivity to credit spreads. VCIT's beta of 1.07 versus LQD's 1.34 suggests intermediate corporates are less volatile than the full investment-grade corporate spectrum.
Who each is best for
* AGG: Investors seeking the simplest, lowest-cost exposure to the entire U.S. bond market (treasuries, corporates, mortgages, agencies). Best in taxable accounts where the monthly distributions can be easily reinvested.
* BND: Essentially identical to AGG, but with substantially more AUM and a float-adjusted methodology; ideal if you prefer Vanguard's ecosystem or want the slight issuer-size weighting.
* LQD: Investors comfortable with corporate-credit risk who want to capture the full yield spectrum of investment-grade corporates and don't mind the higher fee (0.14% vs. 0.03%) or volatility. Suitable for those with a 5+ year horizon.
* VCIT: Investors seeking higher yield than the broad indices without the duration or credit concentration of longer-dated corporates; works well in balanced portfolios where intermediate-term bonds fit a specific maturity ladder or liability.
Key risks to know
* Interest-rate sensitivity and NAV volatility. LQD's 1.34 beta means a 1% rise in rates will roughly halve the price drop of AGG/BND; VCIT at 1.07 is more moderate but still materially more volatile than the aggregate indices.
* Credit and spread risk. All corporate-heavy funds (LQD, VCIT) rely on issuer creditworthiness and are sensitive to widening credit spreads during economic stress. A recession could drive both NAV decline and distribution cuts as default risk rises.
* Yield-to-NAV math. VCIT's 4.79% yield is attractive, but check whether it's being sustained by capital appreciation or capital return; at these interest-rate levels, a sustained 4.79% distribution may compress NAV over time if underlying bond coupons don't rise.
* Fee impact at scale. LQD's 0.14% fee (vs. 0.03% for the others) costs an extra $140 per $100k AUM annually; over decades, that compounds.
Bottom line
If you want maximum simplicity and diversification with minimal fees, AGG and BND are functionally identical and appropriate for most bond-only allocations. If you're pursuing higher income and comfortable with corporate credit and spread risk, VCIT offers a compelling middle ground—higher yield than the aggregate indices without LQD's full-spectrum corporate volatility or elevated fees. LQD is the choice for investors deliberately overweighting corporate exposure and who believe the 4.61% yield justifies the fee and beta tradeoff. None of these funds are income-focused vehicles in the mold of closed-end funds or preferred-stock ETFs; they're all anchored to underlying index returns, and past performance doesn't predict future results.
AI-generated analysis for educational purposes only. Verify important details independently; past performance does not guarantee future results.
