TOPIC 9 : Capital Market Assumptions (CMA)

Question 1

Why do we need capital market assumptions (CMA)?

Answer 1

To:
✅ Develop forward-looking estimates of return, risk, and correlations
✅ Combine them in portfolio optimisation
✅ Support asset allocation decisions

Question 2

Why are historical estimates unreliable?

Answer 2

➥ Mean and variance are not stationary over time
➥ Historical average may be upward or downward biased
➥ May ignore current conditions (inflation, policy, earnings)

Question 3

Which components are reasonably reliable from historical data?

Answer 3

➥ Volatility (since it’s more persistent than return)
➥ Correlations — but they fluctuate over time

Question 4

What’s the Risk Premium Approach?

Answer 4

➥ Suggests future return = risk-free rate + risk premium
➥ Based on historical equity risk premium (6% over bonds, 8% over cash)

Question 5

Why might the risk premium diminish?

Answer 5

➥ Rising prices due to demand for equities
➥ Lower future risk and lower required return

Question 6

What’s the Building Block Approach?

Answer 6

➥ Start from 10-year bond yield
➥ Deduct 0.8–1% for cash vs bond
➥ Add 4% equity premium over bond
➥ Allows forward view while ignoring temporary fluctuations

Question 7

What’s Black-Litterman Model?

Answer 7

➥ Combines market’s view (implied from market weights) with
➥ Investor’s subjective view
➥ To produce blended expected returns
➥ Addresses issues with pure Mean-Variances (too sensitive to small input)

Question 8

Black-Litterman – key components?

Answer 8

➥ Market portfolio weights
➥ Investor’s view vector (Q)
➥ Uncertainty about view (Ω)
➥ Prior (equilibrium) + view (Bayesian blend)

Question 9

What’s James Stein Estimator (JS)?

Answer 9

➥ Combines sample mean with grand average
➥ “Shrinks” the sample mean toward the grand mean
➥ To account for sampling error and reduce variance

Question 10

What controls shrinkage?

Answer 10

➥ If variance of sample > variance of grand average → more shrinkage
➥ If sample is close to grand average → less shrinkage

Question 11

Why is James Stein a Bayesian Estimator?

Answer 11

➥ It adjusts each observation toward a “prior”—the grand mean
➥ Based on variance of estimates and their dispersion

Question 12

Summary — Black-Litterman vs James Stein:

Answer 12

➥ Black-Litterman adjusts for subjective view + market view
➥ James Stein adjusts pure historical means toward a grand average
➥ Both aim to produce more robust forward estimates