Chapter 3 · Section 5 practice
Portfolio Losses and Tail Risk
These questions review the examples in the companion slides. Choose an answer, then check the explanation.
Question 1
The historical monthly ES uses two tail months. What is the appropriate interpretation?
- A precise future loss limit
- A sparse-sample tail summary requiring uncertainty and more evidence
- Proof that worse losses cannot happen
- A daily risk measure
Question 2
Which risk statistic changes when return order changes but the set of returns stays fixed?
- Maximum drawdown
- The empirical loss quantile
- The mean loss
- The unordered tail mean
Question 3
A GEV fitted to 20-day block minima describes what outcome?
- The compounded 20-day portfolio return
- The average daily return in each block
- The worst single daily return in each block
- The maximum portfolio weight
Question 4
Which denominator belongs with tail co-exceedance?
- All non-missing feature cells
- The number of fitted parameters
- The number of portfolio holdings
- The number of market-tail dates
Question 5
For continuous losses, which description distinguishes VaR95 from ES95?
- VaR is the maximum possible loss; ES is the mean return
- Both are confidence limits for mean return
- VaR is the 95th loss percentile; ES is mean loss beyond that percentile
- ES counts how many observations are in the sample
Question 6
genextreme.fit returns c = -0.35. Which interpretation follows from this fitted family?
- A bounded upper loss tail because c is negative
- A heavy unbounded upper loss tail because ξ = 0.35
- A Gaussian tail with standard deviation 0.35
- A 35% probability of losing money
Question 7
Why should we compare several plausible POT thresholds?
- The highest threshold always gives the most precise estimate
- More excesses always mean a better tail approximation
- Threshold choice changes approximation bias, sample size and uncertainty
- We should select whichever threshold gives the smallest reported VaR
Question 8
Which summary correctly reports the POT result?
- The strategy cannot lose more than 3.27% in a month
- A 1985–1990 Dow fit above 1.5% daily log loss uses 91 excesses and estimates daily log-loss VaR99 near 3.27%
- Ninety-one exceedances guarantee a precise future tail probability
- ES99 is a 99% confidence interval for mean return