Where the capital markets meet catastrophe risk
Insurance linked securities education

Insurance linked securities education

Learn catastrophe bonds, ILS pricing, triggers, reinsurance risk transfer, parametric insurance, and sovereign disaster risk finance through a structured online course.

CPD accredited 50 hours Cat bonds, pricing, triggers, parametric insurance
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Educational content only. No investment advice.
Course credentials
50
hours
CPD
accredited
CPD Certified CPD UK Provider #776667
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Start with the ILS101 course
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Start the ILS101 course

Begin with the core ILS series, then move into cat bond pricing, triggers, reinsurance applications, sovereign risk, climate, cyber, and parametric insurance.

1
Foundations of ILS and reinsurance risk transfer
How insurance risk is transformed into capital markets instruments.
2
Cat bond pricing, risk metrics, and trigger design
Expected loss, attachment, exhaustion, collateral, and payout mechanics.
3
Applications across climate, cyber, and public risk
Where ILS, parametric insurance, and disaster risk finance are used.
CPD Certified CPD-accredited course
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CPD Certified CPD-accredited course

What you get

A structured way to learn the language, mechanics, and applied use cases of insurance linked securities.

Structured video lessons
A guided path through ILS fundamentals, catastrophe bonds, triggers, pricing, and applications.
Quizzes and knowledge checks
Test understanding as you move from definitions to applied market concepts.
Terminology and reference material
Build fluency with cat bond, ILS, reinsurance, parametric, and disaster risk finance terms.
Pricing and risk-transfer examples
Learn how expected loss, attachment, exhaustion, triggers, and return metrics fit together.
Certificate and CPD where applicable
Use the course as a documented learning pathway for professional development.

Course Curriculum

A structured learning journey through insurance-linked securities, climate risk, cyber exposure, and parametric insurance.

Core Programme

The ILS Series

9 lectures covering the full spectrum of insurance-linked securities — from fundamentals to sovereign risk management.

01 Why Risk Transfer is so Important
The economic and social value of catastrophe risk transfer and how it underpins the ILS market.
FoundationsRisk Transfer
Go to lecture
02 What are ILS Part 1 — Catalyst, Market Data, Cat Bond Structure & Term Sheet
The catalyst behind the ILS market, key market data, and a deep dive into cat bond structures and term sheets.
Cat BondsStructures
Go to lecture
03 What are ILS Part 2 — Sidecars, CRe, ILWs & Weather Derivatives
Beyond cat bonds: sidecars, collateralised reinsurance, industry loss warranties, and weather derivatives.
SidecarsILWs
Go to lecture
04 What are ILS Part 3 — Advantages of ILS, Uncorrelated, Losses & Spreads
Why ILS is attractive to investors: low correlation, historical losses, and spread analysis.
InvestorsReturns
Go to lecture
05 What are ILS Part 4 — Triggers and How They Work
Indemnity, parametric, modelled loss, and industry loss triggers — how each works and when they pay out.
TriggersPayouts
Go to lecture
06 ILS Pricing Part 1
Key variables influencing cat bond pricing models, expected loss, and risk premiums.
PricingExpected Loss
Go to lecture
07 ILS Pricing Part 2
Advanced pricing, secondary market valuation, and spread dynamics.
Secondary MarketValuation
Go to lecture
08 Why (Re)insurers Use ILS
How insurers and reinsurers use ILS for capital relief, diversification, and risk management.
CedantsCapital Relief
Go to lecture
09 Sovereign Disaster Risk Management
How governments and NGOs use cat bonds and risk pools for resilience funding.
Sovereign RiskDevelopment
Go to lecture
Specialist

Specialist Series

Deep dives into climate science, cyber risk, and parametric insurance.

10

The Climate Series

How our evolving climate shapes catastrophe risk, from SSTs and ENSO to CMIP6 projections.

Climate Fundamentals — Full Episode
Audio episode available
1 Episode Live Go to lecture
11

The Cyber Series

Explore the emerging asset class — from threat landscapes and underwriting to cyber cat bonds and systemic risk.

Cyber ILS — Full Episode
Audio episode available
1 Episode Live Go to lecture
12

The Parametric Series

Triggers, payout structures, sovereign pools, basis risk, and the future of parametric insurance.

Parametric Insurance — Full Episode
Audio episode available
1 Episode Live Go to lecture
Supporting

Supporting Series

Additional modules to complement your ILS education.

The Legal Series COMING SOON

Legal frameworks, ISDA documentation, regulatory considerations, and contractual structures underpinning ILS transactions.

The Regulator Series COMING SOON

Rating methodologies from S&P, Moody's and AM Best. Capital adequacy, risk-based models, and governance.


Lecture Library

Select a lecture to begin learning


Shorts

Supplementary audio content supporting the Specialist Series (Climate, Cyber, and Parametric modules). These episodes complement the core ILS curriculum with deeper exploration of specialist topics.



Industry Reports

Research and market analysis from leading insurance and reinsurance institutions.


Did You Know?

Bite-sized ILS facts in under 60 seconds. Perfect for a quick knowledge boost between lectures.


Factsheet Library

Download our educational resources on Insurance-Linked Securities

Climate Data Centre

Sea surface temperatures are a key driver of Atlantic hurricane activity and catastrophe bond pricing. Use these tools to explore live climate data.

Why SSTs Matter

Warmer ocean surfaces provide the energy that fuels tropical cyclones. SST anomalies in the Atlantic Main Development Region (MDR) are one of the strongest predictors of hurricane season severity.

ENSO & Hurricane Activity

El Niño events increase vertical wind shear over the Atlantic, suppressing hurricane formation. La Niña does the opposite — reduced shear allows more storms to develop and intensify.

ILS Market Relevance

Cat bond spreads widen ahead of hurricane season and tighten afterwards. Monitoring SSTs and ENSO phases helps ILS investors assess whether seasonal hurricane risk is above or below average.

ENSO Phase Timeline

El Niño years (red) tend to suppress Atlantic hurricanes via increased wind shear, while La Niña years (blue) remove that brake — leading to more and stronger storms. Notice how the most active Atlantic hurricane clusters (late 1990s–2000s, 2020–2022) coincide with La Niña phases. ILS investors track ENSO forecasts closely: a La Niña outlook can widen cat bond spreads months before hurricane season begins.

Source: NOAA Climate Prediction Center ONI (Oceanic Niño Index) classification.

ENSO Phase vs Atlantic Storm Activity

La Niña years average significantly more Atlantic named storms than El Niño years, consistent with reduced wind shear favouring cyclogenesis. ENSO phase classified by NOAA ONI during peak hurricane season (JAS/ASO).

Sea Surface Temperature Analysis

Source: NOAA OISST V2.1 via Climate Reanalyzer, University of Maine

Sea Surface Temperature Analysis

Source: NOAA OISST V2.1 via Climate Reanalyzer, University of Maine

Current Year SST Anomaly

This chart shows how much warmer or cooler the ocean surface is compared to the long-term average (1982–2011). The highlighted line represents the selected year's daily anomaly: values above zero (shaded red) mean the ocean is warmer than normal, values below zero (shaded blue) mean cooler than normal. Background lines show the previous decade for context.

Why it matters: Tropical cyclones draw energy from warm ocean water. When SSTs run persistently above the climatological mean — especially in the Atlantic Main Development Region during hurricane season (Jun–Nov) — storms can form more easily and intensify more rapidly. Anomalies of +0.5°C or more are considered significant and often feature in seasonal hurricane outlooks from CSU and NOAA.

For ILS investors: Sustained positive anomalies heading into hurricane season signal elevated expected loss, which can pressure cat bond spreads wider and increase reinsurance pricing at mid-year renewals.

Source: NOAA Optimum Interpolation SST V2.1 (OISST), via Climate Reanalyzer, University of Maine. Anomaly = selected year daily SST minus 1982–2011 climatological mean.

Efficient Frontier Model

Build your own multi-asset portfolio and visualise where it sits on the efficient frontier. This tool uses simulated daily data for six illustrative asset classes spanning January 2002 to March 2026 to compute risk, return, and optimal allocation statistics. Adjust the sliders below to set your desired portfolio weights and observe how adding catastrophe bonds affects the risk-return profile.

Simulated data — educational illustration only. All series shown are computer-generated, calibrated to broadly resemble the published long-run characteristics of each asset class. They are not actual market data, and this tool is not investment advice, a recommendation to allocate to any asset class, or a projection of future performance. Consult a qualified, regulated financial adviser before making investment decisions.

What is the Efficient Frontier?

Build Your Portfolio

Set your allocation across six asset classes. Weights must sum to 100%. Use the preset buttons above for common allocations, or drag the sliders to build a custom portfolio. The "Optimal" preset loads the maximum Sharpe ratio allocation from the simulation.

Cat Bonds
Global Equities
Global Bonds
Long Treasuries
Commodities
Hedge Funds

HFRI data is reported monthly and forward-filled to daily frequency. This understates hedge fund volatility and may overstate its diversification benefit relative to daily-priced indices.

Total:
Rf:
%

Cumulative Returns

All six simulated asset classes rebased to 100 at the start of the selected period. Your custom portfolio is shown as the thick white line. This chart assumes daily rebalancing to the target weights. The simulated cat bond series (teal) is driven by event risk rather than economic cycles: it embeds illustrative drawdowns at the approximate dates of real catastrophes (2005, 2011, 2017, 2022) alongside benign years.

Data: simulated series generated for ILS101 (see Limitations & Assumptions below). Calibrated to broadly reflect publicly documented long-run risk and return characteristics of the six asset classes shown. Not actual index or market data.

Drawdown Analysis

Peak-to-trough decline for each asset and your portfolio over the selected period. The drawdown measures how far an investment has fallen from its highest point. Cat bonds (teal) typically show shallow, short-lived drawdowns compared to equities and commodities, reflecting their low sensitivity to financial market sell-offs.

Rolling Window Analysis

Rolling annualised metrics for each asset and your portfolio. Toggle between return, volatility, and Sharpe ratio, and choose a 3-year or 5-year window. This shows how performance characteristics change through different market regimes.

Efficient Frontier

Each dot represents a randomly simulated long-only portfolio. The colour scale shows the Sharpe ratio of each allocation — red (poor) through teal/lime (excellent). Two frontiers are plotted: the dashed grey line shows the best achievable risk-return combinations without cat bonds (5 assets), while the solid teal line shows the frontier with cat bonds (all 6 assets). Your custom portfolio appears as a white diamond, and the optimal (maximum Sharpe) portfolio is marked with a gold star. The leftward shift of the teal frontier demonstrates the diversification benefit of including catastrophe bonds.

10,000 random portfolios simulated per frontier (long-only, fully invested). Returns annualised from monthly log returns. Risk-free rate is configurable via the Rf input above.

Correlation Matrix

Pairwise correlations of monthly log returns for the selected period. Values near zero indicate that two assets move independently. The simulated cat bond series is constructed with low correlation to the other asset classes — mirroring the published behaviour of real cat bond indices, and the statistical driver of the frontier shift illustrated above. In reality, correlations are period-dependent and can increase when catastrophe events and market stress coincide.

Performance Statistics

Annualised risk and return metrics for each asset class and your custom portfolio over the selected period. Your portfolio is highlighted at the bottom.

Metric Definitions

  • Annualised Return: The geometric mean return, annualised from monthly log returns. Represents the average compounded return per year over the selected period.
  • Annualised Volatility: The standard deviation of monthly log returns, scaled to annual frequency (×√12). Measures the dispersion of returns — higher values indicate greater uncertainty.
  • Sharpe Ratio: Annualised return divided by annualised volatility. Measures risk-adjusted performance — how much return per unit of risk. Higher is better. Values above 0.5 are generally considered attractive; above 1.0 is excellent.
  • Maximum Drawdown: The largest peak-to-trough decline in cumulative portfolio value over the selected period. Represents the worst-case loss an investor would have experienced. Computed from daily prices.
  • Correlation: Pearson correlation coefficient of monthly log returns between two assets. Ranges from −1 (perfectly inverse) to +1 (perfectly aligned). Near-zero values indicate the assets move independently.

Limitations & Assumptions

  • All data is simulated. The six series are computer-generated correlated random walks, calibrated to broadly resemble publicly documented long-run return, volatility, and correlation characteristics of each asset class. No actual index or market data is used or displayed.
  • Event-style cat bond drawdowns are placed at the approximate dates of real catastrophes (Katrina 2005, Tohoku 2011, Harvey/Irma/Maria 2017, Ian 2022) so chart shapes are recognisable, but all magnitudes are illustrative.
  • The hedge fund series is generated with dampened volatility to mimic the return-smoothing seen in monthly-reported hedge fund indices.
  • The model assumes daily rebalancing to target weights with no transaction costs, taxes, or liquidity constraints. Real-world implementation costs would reduce returns.
  • The optimal allocation is derived from the simulated history and illustrates the mathematics of diversification — it says nothing about future performance of any real asset.
  • The efficient frontier is computed analytically via quadratic programming (exact solution), but the random portfolio cloud shown on the scatter chart uses 10,000 Monte Carlo samples for visual illustration.

Asset Class Descriptions

Each simulated series is modelled on the typical profile of its real-world asset class:

Cat Bonds: Modelled on broad catastrophe bond market indices. Real cat bond returns comprise floating-rate coupons (risk spread + money market) minus losses from triggered events — steady accrual punctuated by event-driven drawdowns.

Global Equities: Modelled on developed-market large/mid-cap equity indices — higher long-run returns with substantial volatility and deep drawdowns in crises (2008, 2020, 2022).

Global Bonds: Modelled on broad investment-grade global aggregate bond benchmarks (currency-hedged) — low volatility, modest returns, vulnerable to sharp rate rises as in 2022.

Long Treasuries: Modelled on long-maturity (10yr+) US Treasury indices. Highly sensitive to interest rate movements — strong returns when rates fall, significant losses when rates rise.

Commodities: Modelled on diversified commodity futures indices spanning energy, agriculture, and metals — volatile, cyclical, with returns from spot moves, roll yield, and collateral.

Hedge Funds: Modelled on broad multi-strategy hedge fund composites — equity-correlated returns with reported volatility dampened by monthly smoothing.

Data: simulated for educational illustration — not actual market data. Author: T. Pughe

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Module Assessments

Choose Quick Quiz (10 random questions) to practice, or Full Assessment to earn your certificate. Score 70%+ to download your certificate.

Leaderboard

Top 20 scores per module from all users.

Glossary

Search ILS, credit, and cyber terminology.

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Natural Catastrophe Explorer

Explore historical hurricane tracks and global earthquake activity.

Why Track Hurricanes?

Atlantic hurricanes are the single largest driver of cat bond losses. Tracking historical tracks, intensities, and landfalls helps ILS investors calibrate their risk models.

Earthquake Risk & ILS

Japan, California, and New Zealand earthquakes have triggered cat bonds. Seismic risk is modelled using fault databases, historical catalogues, and ground motion models.

From Data to Pricing

Cat modellers use event catalogues like these to build occurrence exceedance probability curves, which feed directly into cat bond attachment and exhaustion point calibration.

Data source: NOAA National Hurricane Center, HURDAT2 (Atlantic hurricane database). This data is provided by NOAA and is in the public domain. Use of this data does not imply endorsement by NOAA. For official forecasts and warnings, visit nhc.noaa.gov.

Recent Significant Earthquakes

Magnitude Distribution

Data source: USGS Earthquake Hazards Program FDSN Event Web Service. Data is provided by the U.S. Geological Survey and is in the public domain. Use of this data does not imply endorsement by USGS. For official seismic hazard information, visit earthquake.usgs.gov.

Hurricane Season Comparison

Compare two Atlantic hurricane seasons side by side. Select years and click Compare to fetch HURDAT2 data for both seasons.

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