risksim

Portfolio Monte Carlo: aggregate simulation, dependence, reinsurance contracts, and risk measures.

Overview

risksim simulates the aggregate loss of a portfolio whose components can be anything exposing a .sample(n, rng=...) method — lossmodels aggregate models, extremeloss tails, or your own objects. Components are independent by default; rank correlation is imposed with the Iman–Conover construction when you specify a dependence structure.

Aggregate reinsurance is applied as layers or stacked programs, and results come back with the standard risk measures (VaR, TVaR, exceedance probabilities) plus bootstrap confidence intervals, so a simulation answer always carries its simulation error.

Installation

pip install risksim

Requires Python 3.10 or newer.

Quick start

The example pairs risksim with lossmodels (pip install lossmodels), the intended combination for frequency–severity components.

from lossmodels import Poisson, Lognormal, CollectiveRiskModel
from risksim import Portfolio, PortfolioItem, AggregateLayer

# two lines of business, each a collective-risk model (any .sample object works)
line_a = CollectiveRiskModel(Poisson(3.0), Lognormal(8.0, 0.9))
line_b = CollectiveRiskModel(Poisson(1.5), Lognormal(9.0, 0.7))

portfolio = Portfolio([
    PortfolioItem("line_a", line_a),
    PortfolioItem("line_b", line_b, weight=1.25),   # optional exposure weight
])

# aggregate annual loss, net of an aggregate layer
layer = AggregateLayer(attachment=250_000, limit=500_000, share=1.0)
result = portfolio.simulate(100_000, contract=layer, rng=42)

print("gross mean    :", result.gross_mean)
print("ceded mean    :", result.ceded_mean)
print("retained mean :", result.retained_mean)
print("net (retained) 99% TVaR:", result.tvar(0.99))

What's inside

• Portfolios — Portfolio / PortfolioItem over any objects exposing .sample, with optional exposure weights.
• Dependence — Iman–Conover rank correlation via risksim.dependence.impose_rank_correlation.
• Contracts — AggregateLayer, stacked ContractPrograms, and apply_contract on raw loss vectors.
• Risk measures — empirical VaR, TVaR, exceedance probabilities, and one-call summaries in risksim.metrics.
• Uncertainty — bootstrap confidence intervals for any statistic in risksim.uncertainty.

The full API reference and end-to-end worked examples live at openactuarial.org/risksim.html.

The OpenActuarial ecosystem

risksim is one of seven packages that share conventions — tidy tables, explicit distribution parameterizations, reproducible random-number handling — and compose across package seams:

Package	Role
actuarialpy	Calculation primitives the workflow packages build on
experiencestudies	Experience reporting, actual-vs-expected, claimant and concentration analysis
projectionmodels	Claim, premium, and expense projection over a renewal horizon
ratingmodels	Manual and experience rating, credibility, indication, GLM relativities
lossmodels	Severity and frequency fitting, aggregate loss distributions
extremeloss	Extreme-value tails: POT/GPD, GEV, return levels, splicing
risksim	Portfolio Monte Carlo, dependence, reinsurance contracts, risk measures

Install everything at once with pip install openactuarial.

Development

git clone https://github.com/OpenActuarial/risksim
cd risksim
python -m pip install -e ".[dev]"
pytest
ruff check src tests

CI runs the same gate on Python 3.10–3.14 across Linux and Windows.

Versioning and stability

All ecosystem packages are pre-1.0: minor releases may change APIs, and every release is documented in CHANGELOG.md. Current per-package API stability is tracked at openactuarial.org/stability.html.

License

MIT — see LICENSE.