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Case Study Two –
“Rate Expectations”
Another Holborn client wanted to know how different exposures
contribute to its overall volatility of surplus:
 General inflation levels
Large Property policy exposures
Large Casualty policy exposures
Property catastrophes
Equity market exposure
Which exposures cause more volatility than they are worth?
Which exposures merit the most attention over the long term?
To measure each exposure’s contribution to the overall risk, we showed the
expected annual hit to surplus from each cause, and the correlation factor with
overall surplus levels:
 |
| |
Inflation Spikes |
Large Property Policies |
Large Casuality Policies |
Property Catastrophe |
Stock Market Declines |
|
| Expected Losses (%PHS) |
0.9% |
0.1% |
6.5% |
0.2% |
2.0% |
| Standard Deviation |
1.3% |
0.3% |
2.0% |
1.5% |
5.4% |
| Correlation w/ Surplus |
14.2% |
2.9% |
22.2% |
7.4% |
31.4% |
|
For each type of exposure, we also show the ratio of the expected losses to the
covariance with surplus to show which factors, are in proportion to their size, the
biggest drivers of the company’s overall volatility. This is analogous to the return
on risk adjusted capital.
|
| |
Stock Market Declines |
Property Catastrophe |
Inflation Spikes |
Large Property Losses |
Large Casuality Losses |
|
| Reward-to-Risk Ratio |
1.99% |
2.77% |
8.42% |
18.33% |
24.93% |
| Importance to Address |
#1 |
#2 |
#3 |
#4 |
#5 |
|
This comparison shows where this company can best invest in improving its
volatility and performance. Exposures with low reward to risk ratios should be
targeted for correction first.
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