Private equity secondaries trade at discounts to reported Net Asset Value for reasons that combine liquidity, information asymmetry, and legal or contractual frictions. Models that best capture those discounts combine balance-sheet signals with market-implied adjustments rather than relying on a single heuristic.
Valuation approaches that explain discounts
The simplest starting point is NAV-based pricing adjusted for an explicit liquidity haircut and time-to-distribution. Academic practitioners such as Tim Jenkinson University of Oxford emphasize that NAV remains the primary anchor for many market participants because it aggregates GP valuations and asset-level information. To capture uncertainty beyond published NAVs, discounted cash flow frameworks that project expected future distributions and discount them at a risk-adjusted rate help translate illiquidity and extension risk into price effects. These projections require careful judgement on remaining fund life and portfolio realizations, which are often opaque.Option-theoretic and stochastic models add value when portfolios contain highly skewed outcomes. Steven N. Kaplan University of Chicago Booth and other researchers highlight that private equity returns are fat-tailed and that buyers effectively purchase a bundle of real options on exits. Modeling the convexity of eventual payoffs through option pricing or scenario-based Monte Carlo methods can better reproduce observed discounts in vintages with concentrated tail risk.
Drivers and consequences for model choice
Empirical caution comes from Ludovic Phalippou University of Oxford who has documented how reported NAVs can mislead if treated as full-market marks rather than managerial estimates. Therefore a best-practice model layers three elements: an NAV anchor, an explicit liquidity and information premium, and a structural capture of tail risk. Regional and cultural differences matter because secondary market depth varies across territories; European transactions often reflect different regulatory disclosure and tax regimes than US sales, which changes the size of the liquidity haircut. Consequently a one-size-fits-all spread to NAV is rarely sufficient.Choosing a model affects behavior: undervaluing liquidity costs can produce losses for buyers who inherit valuation risk, while overestimating tail risk can freeze deal flow and amplify market segmentation. For portfolio managers and institutional buyers, combining NAV-adjusted DCF with scenario-based option metrics and transparent assumptions yields the most defensible prices in secondary trades because it aligns observable information with structural uncertainties and documented academic findings.