PML Aggregation
Aggregate modeled occurrence losses into a caller-parameterized PML.
Description
A parameterized PML aggregate scales the sum of supplied modeled occurrence losses.
A parameterized PML aggregate scales the sum of supplied modeled occurrence losses. The factor is a caller-defined aggregation assumption.1
Aggregate modeled occurrence losses into a caller-parameterized PML.
When to use PML Aggregation
Use this tool to aggregate modeled occurrence losses into a caller-parameterized PML.
Prepare the supplied policy terms, dates, assumptions or recorded indicators requested. Keep monetary amounts on one currency basis and select the applicable thresholds separately.
How to use PML Aggregation and understand its results
- Prepare the supplied policy terms, dates, assumptions or recorded indicators requested. Keep monetary amounts on one currency basis and select the applicable thresholds separately.
- Enter modeledOccurrenceLosses, aggregateCorrelationFactor, portfolioLimit using the field table and calculation rules, then run the tool.
- Read largestOccurrenceLoss, modeledAggregatePml, limitedAggregatePml, limitHeadroom. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| modeledOccurrenceLosses | Modeled occurrence losses | list of number | Required |
| aggregateCorrelationFactor | Aggregate correlation factor | number; minimum: 0 | Required |
| portfolioLimit | Portfolio limit | number; minimum: 0 | Required |
Enter nonempty nonnegative losses, nonnegative factor and portfolio limit. Return largest occurrence, scaled aggregate, capped aggregate and remaining limit. Factor can exceed one.
modeledPML=Σlosses×factor; limited=min(limit,modeledPML) Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| largestOccurrenceLoss | Largest occurrence loss | number; minimum: 0 |
| modeledAggregatePml | Modeled aggregate PML | number; minimum: 0 |
| limitedAggregatePml | Aggregate PML after limit | number; minimum: 0 |
| limitHeadroom | Portfolio-limit headroom (currency) | number |
{
"input": {
"modeledOccurrenceLosses": [
100,
200,
300
],
"aggregateCorrelationFactor": 0.5,
"portfolioLimit": 400
},
"output": {
"largestOccurrenceLoss": 300,
"modeledAggregatePml": 300,
"limitedAggregatePml": 300,
"limitHeadroom": 100
}
}Limitations and assumptions
No probability distribution, return period, correlation matrix or catastrophe simulation is fitted; the factor is not a statistical correlation coefficient.
Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Trade-Credit Exposure: Estimate trade-credit portfolio exposure and covered expected loss. For a complementary task, use Retrocession Waterfall: Apply retrocession attachments and limits to a gross ceded loss.
References
-
PML Aggregation — tool.io
Similar or alternative tools
- Trade-Credit Exposure
Estimate trade-credit portfolio exposure and covered expected loss.
- Retrocession Waterfall
Apply retrocession attachments and limits to a gross ceded loss.