Portfolio Optimizer Methodology
Per-insured Euler/Shapley TVaR decomposition, RAROC computation, and the renewal walk-away engine that drives bind / re-rate / non-renew decisions from capital efficiency.
1. Executive Summary
The Portfolio Optimizer answers three of the hardest questions in cyber underwriting: (1) which insureds drive my tail capital, (2) what is my return on capital per insured, and (3) which insureds should I renew, re-rate, or walk away from.
It runs a Monte Carlo simulation of the portfolio (severity-aware, calibrated on real incident history), decomposes the resulting TVaR contribution per insured using Euler allocation (the standard Solvency II additive method), and applies user-defined RAROC assumptions to produce binding / renewal recommendations.
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2. Shapley/Euler Decomposition
Each insured contributes to portfolio AAL and TVaR through correlation with the rest of the book. Two insureds can have identical standalone AAL but very different marginal contributions to the tail.
The engine simulates num_years of joint losses, identifies the tail set (years above VaRα), and computes for each insured i:
This is the Euler allocation (sum of TVaR_i = portfolio_TVaR exactly). For non-additive cases the engine falls back to a Monte Carlo Shapley approximation.
3. Concentration Index
The concentration index is the ratio of an insured's TVaR share to its "fair share" (1/N):
Concentrators are the binding decisions to question; diversifiers are the policies you want more of.
4. RAROC Formula
Risk-Adjusted Return on Capital divides expected profit by required regulatory/economic capital. Required capital is taken as a fraction of the insured's TVaR contribution.
All four assumptions (expense ratio, capital charge, target RAROC, target LR) are user-tunable on the page and persisted in localStorage.
5. Renewal Walk-Away Engine
For each insured, the engine determines a renewal action and (if needed) the rate change required to clear the RAROC hurdle:
6. Recent-Incident Override
Pure mathematical concentration_index can mislead: an insured with two recent catastrophic ransomware events but a small standalone TVaR could still mathematically be flagged a "diversifier". The engine therefore applies a hard override:
The simulation engine also amplifies the lognormal severity parameters of insureds with high historical max severity (×2.5 if max_severity ≥ 80, ×1.7 if ≥ 65), so their TVaR contribution rises naturally — but the override is the underwriting safety net.
7. Data Sources
| Source | Used For |
|---|---|
| Portfolio collection | Insured list |
| Company score collection | Per-insured cyber score → frequency baseline |
| cyber_portfolio.blog_data | Real incident history → severity-aware frequency boost |
| Company profile collection | Industry, employees, revenue → severity scaling |
8. Glossary
| Term | Definition |
|---|---|
| AAL | Annual Average Loss |
| TVaR | Tail Value at Risk — average loss in the worst α% of years |
| RAROC | Risk-Adjusted Return on Capital |
| Euler allocation | Additive capital allocation method that sums exactly to portfolio capital |
| Concentrator | Insured whose TVaR share exceeds its fair (1/N) share |
| Diversifier | Insured whose TVaR share is below its fair share — pays for itself via correlation offset |