A challenge to ESG integration at the asset allocation level when using mean-variance optimization is that it:

A challenge to ESG integration at the asset allocation level when using mean-variance optimization is that it:
A . is highly sensitive to baseline assumptions
B . requires specialist knowledge to make informed judgments about future risk.
C . could introduce an additional source of estimation errors due to the need for dynamic rebalancing

Answer: A

Explanation:

A challenge to ESG integration at the asset allocation level when using mean-variance optimization is that it is highly sensitive to baseline assumptions.

Here’s why:

Baseline Assumptions:

Mean-variance optimization relies on assumptions about expected returns, risks, and correlations among different asset classes. These assumptions are often based on historical data, which may not accurately predict future performance, especially when integrating ESG factors.

Sensitivity:

Small changes in the baseline assumptions can lead to significantly different portfolio allocations. This sensitivity can be problematic when integrating ESG factors, as the data and methodologies for assessing ESG risks and opportunities are still evolving and can introduce additional variability.

Dynamic Rebalancing:

While dynamic rebalancing can introduce estimation errors, the primary challenge remains the sensitivity to initial assumptions. Specialist knowledge is essential for making informed judgments about future risks, but this is secondary to the issue of assumption sensitivity.

CFA ESG Investing

Reference: The CFA ESG Investing curriculum covers the complexities of integrating ESG factors into asset allocation models, particularly the challenges posed by the sensitivity of mean-variance optimization to baseline assumptions.