Assume you have 30 years of quarterly PE cash flow data across various economic regimes, so that estimation is statistically precise. Vanilla PME still systematically mismeasures risk-adjusted performance. What does it get wrong?

Question

BlackTom AI · Accepted Answer

Vanilla PME uses the log-utility stochastic discount factor (SDF) — effectively treating public-market returns as if they price private-equity cash flows with the same growth-optimal (log-utility) kernel. That assumption is the root cause of its systematic mismeasurement: it gets both the quantity and the price of risk wrong.

1) Relevant concepts
- Stochastic discount factor (SDF): prices risky cash flows; m_t relates expected discounted payoffs to current prices.
- Log utility SDF: for u(C)=ln C, marginal utility ∝ 1/C so the SDF weights outcomes by inverse cumulative wealth (growth-optimal weighting).
- Quantity of risk: exposure of returns/cash flows to systematic risk factors (volatility, betas).
- Price of risk: the market compensation per unit of exposure (risk premia or lambda for each factor).

2) Step-by-step reasoning
- PME replicates private-equity cash flows by trading the public index; this implicitly assumes the SDF that prices those trades is the one implied by geometric/compound index returns (the log-utility kernel).
- For log utility, m_t ∝ 1/W_t (wealth), so private cash flows are effectively discounted with the inverse of compounded public returns. That equates to fixing a particular relationship between mean returns and higher moments (growth-optimal portfolio).
- Real investors and markets price risk via mean-variance tradeoffs and factor risk premia; PE exposures (illiquidity, leverage, exposure to private value creation) have different betas and are priced differently.

3) Why this is wrong (intermediate logic)
- Quantity error: vanilla PME misstates the true exposure (beta) of PE cash flows to systematic factors because it forces exposures into the log-utility weighting rather than measuring actual covariances.
- Price error: it also imposes a single, incorrect mapping from exposure to expected return (the log-utility price of risk), so the compensation per unit of risk is mis-specified.

4) Why the correct answer is correct
- Saying it gets “Both the quantity and the price of risk — the log-utility SDF misspecifies the entire risk-return relationship” captures both mis-measured betas and mis-priced premia; that is precisely the structural error of vanilla PME.

5) Why alternatives (practical confusions) are not the main point
- Common critiques (timing, liquidity, sample size) matter, but vanilla PME’s structural bias is deeper: it imposes the wrong SDF. That is not remedied by more data or timing adjustments.

6) Conclusion
- Therefore vanilla PME systematically mismeasures performance because its log-utility SDF misspecifies both quantity and price of risk — matching the provided answer.

Assume you have 30 years of quarterly PE cash flow data across various economic regimes, so that estimation is statistically precise. Vanilla PME still systematically mismeasures risk-adjusted performance. What does it get wrong?Single choice

Similar Questions

Asset pricing models...[Fill in the blank]

In an asset pricing model, betas (or factor loadings) reflect...[Fill in the blank]

Which statement best describes our current understanding of how to predict future stock returns?[Fill in the blank]

A long-short strategy consistently delivering significant alpha suggests that...[Fill in the blank]

The index model for stock B has been estimated with the following result: RB,t = 0.01 + 1.1RM,t + eB,t. If σM = 0.20 and R2B= 0.50, the standard deviation of the return on stock B is

Question1.13 According to the Capital Asset Pricing Model (CAPM), in a disequilibrium, overpriced securities have ______ None of the options is correct. positive betas. zero alphas. negative betas. positive alphas. ResetMaximum marks: 2.5 Flag question undefined

A key limitation of using the log-utility CAPM as the basis for PME's risk adjustment is that:

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler