14 Session 5: Term Structure of Private Capital Returns
| Unit | 1 — Why DCF Fails |
| Book Chapter | 3 (all sections) |
| Track | Common core (both tracks) |
| Assessment milestone | Track declaration deadline (end of session) |
14.1 Learning Objectives
By the end of this session, students will be able to:
- Describe the empirical term structure of private capital returns by asset class (PE buyout, growth equity, infrastructure, real estate, private credit).
- Explain why long-horizon assets are disproportionately exposed to the liquidity illusion.
- Compute the Jensen bias \(B(T)\) for a given asset class and horizon using the affine formula.
- Compare the GE-LAV liquidity-adjusted term structure to the standard DLOM (Discount for Lack of Marketability) methodology.
- State and justify their Track selection by the end of class.
14.2 Pre-Class Assignment
- Read: Book Chapter 3, all sections (~25 pages)
- Optional: Section 1 of Phalippou & Gottschalg (2009), “The Performance of Private Equity Funds,” Review of Financial Studies
14.3 In-Class Outline (75 minutes)
| Time | Segment | Format |
|---|---|---|
| 0:00–0:05 | Recap: OU dynamics → today: how dynamics interact with horizon | Lecture |
| 0:05–0:20 | Empirical term structure of private capital returns | Lecture + chart deep-dive |
| 0:20–0:40 | The liquidity risk term structure | Lecture + worked formula |
| 0:40–0:55 | Asset class implications: which assets are most exposed | Lecture |
| 0:55–1:05 | DLOM vs. GE-LAV — comparative framework | Lecture |
| 1:05–1:15 | Track declaration · Q&A · final track guidance | Activity |
14.4 Discussion Questions
- The Jensen bias for a 30-year PPP asset is ~5.4%. The DLOM convention for infrastructure is 10–20%. Are these incompatible? How should they be reconciled?
- Pension funds and insurers hold most of the long-duration private assets where Jensen bias is largest. If GE-LAV becomes adopted methodology, what happens to reported PE returns at large pension funds?
- Some private credit assets have short effective duration (1–3 years for direct lending). Jensen bias is small there. Does that mean private credit is “GE-LAV-immune”? What other GE-LAV channels might still matter?
14.5 Worked Numerical Example: Choosing Between DLOM and GE-LAV
Setup: Insurance company holds a 15-year core infrastructure asset (regulated water utility). DCF mark = $500M.
Method 1: DLOM at 15% - Adjusted value: $500M × 0.85 = $425M - Applied uniformly across all market conditions - “Always 15% off DCF”
Method 2: GE-LAV (current state, normalization regime) - Jensen bias for T = 15: \(A \cdot 15 + C = 0.18\% \cdot 15 = 2.7\%\) - LAV value (just Jensen): $500M × (1 + 0.027) = \(513.5M\) - GE-LAV (normal regime, small equilibrium correction): ~$495M - “About flat with DCF in normal markets”
Method 3: GE-LAV (current state, stressed regime, L = −0.8) - Equilibrium rate: \(r_\text{GE-LAV}(L = -0.8) \approx 12\%\) (vs. DCF 7.5%) - GE-LAV value: $500M × e^{-(12% - 7.5%) } $250M - “50% off DCF in stress”
Method 4: GE-LAV (GFC depth, L = −1.5) - Equilibrium rate: ~32% - GE-LAV value: ~$150M - “70% off DCF at GFC depth”
Comparison: DLOM gives one answer ($425M). GE-LAV gives a menu of answers conditional on regime. The insurance regulator should care about the GFC-depth number ($150M) for solvency purposes.
14.6 What to Expect Next Session
Session 6 begins Unit 2 (Measurement and Theory) with a deep look at IRR. Why IRR is the dominant performance metric in private markets, what its two structural biases are, and why a constant-rate metric cannot correctly measure performance when rates are stochastic.
Reading: Book Chapter 4, sections 4.2–4.4 (~10 pages).
For Track 2 students who declared today: Begin reading Karatzas-Shreve Ch. 5 (Brownian motion and stochastic calculus). You’ll need this background by Session 25.