Questions
25847 Sustainable Finance - Spring 2025 Quiz 7: Topic 7: ESG Integration into Equities
Single choice
In the ESG-tilted quadratic optimisation model, the gamma (γ) term represents:
Options
A.The target ESG score that must be met at all costs
B.The investor's preference strength for ESG relative to tracking error
C.The penalty for diverging from ESG benchmarks
D.The constraint on sector weights to ensure ESG compliance
View Explanation
Verified Answer
Please login to view
Step-by-Step Analysis
In tackling the question about the ESG-tilted quadratic optimisation model, I will evaluate each option on its own merits and potential misconceptions.
Option 1: "The target ESG score that must be met at all costs". This is incorrect because gamma in such models is not typically a hard target threshold for ESG scores; rather, it inf......Login to view full explanationLog in for full answers
We've collected over 50,000 authentic exam questions and detailed explanations from around the globe. Log in now and get instant access to the answers!
Similar Questions
Which statement is (are) false about resampled efficient frontier?
The optimal portfolio on the efficient frontier for a given investor does not depend on_______
Question at position 15 Without imposing no-shorting constraints, you found the tangency portfolio of 3 assets A, B, and C to have weights [0.6, 0.6, -0.2], respectively. What is the most precise thing you can say about the weights of the tangency portfolio once you impose no-shorting constraints?The weights will be [0.5, 0.5, 0.0].The weight on asset C will increase.The weights will remain the same: [0.5, 0.7, -0.2].None of the statements above are necessarily true.The weights on both assets A and B will decrease.
Question at position 15 Rather than tracing out the efficient frontier, you decide to calculate the Sharpe ratio-maximizing portfolio directly. You wrote the function to calculate the sharpe ratio of a portfolio given its weights as follows: def sharpe_ratio(w, μ, Σ, rf): r = w.transpose @ μ σ = np.sqrt(w.transpose @ Σ @ w) return (r - rf) / σ You then define an objective function objective(w): def objective(w): return a * sharpe_ratio(w, μ, Σ, rf) and minimize it subject to constraints using scipy.optimize.minimize. Which value of a will allow you to find the Sharpe ratio-maximizing portfolio? rf10You cannot find the maximum Sharpe ratio using the minimize function.-1
More Practical Tools for Students Powered by AI Study Helper
Making Your Study Simpler
Join us and instantly unlock extensive past papers & exclusive solutions to get a head start on your studies!