CourseInsurance, Risk & Actuarial Data Applications open

Course in Extreme Risk Models in Reinsurance

Online 300-hour course in extreme risk models in reinsurance. 6 modules covering Directive 2009/138/EC (Solvency II, VaR at 99.5%), Delegated Regulation (EU) 2015/35 (catastrophe risk) and R (evd, extRemes). Assigned tutor and open enrolment.

Duration300 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Explain the statistical problem of extreme events.

02

Apply extreme value theory with GEV and GPD distributions.

03

Interpret the results of commercial catastrophe models.

04

Model dependence and aggregation with copulas.

05

Calculate risk measures such as VaR and TVaR in the tail.

06

Validate and govern extreme risk models.

07

Develop an extreme risk modelling project for a reinsurance programme.

Profile

Who it's for

  • Reinsurance actuary wishing to specialise in Extreme Risk Models in Reinsurance.
  • Catastrophe modeller wishing to specialise in Extreme Risk Models in Reinsurance.
  • Quantitative risk analyst wishing to specialise in Extreme Risk Models in Reinsurance.
  • Internal model validator wishing to specialise in Extreme Risk Models in Reinsurance.
  • Economic capital analyst wishing to specialise in Extreme Risk Models in Reinsurance.
  • Actuarial consultant wishing to specialise in Extreme Risk Models in Reinsurance.
  • Graduates in engineering or sciences seeking to enter reinsurance.
Standards & tools

What you'll work with

  • Directive 2009/138/EC (Solvency II, VaR at 99.5%)
  • Delegated Regulation (EU) 2015/35 (catastrophe risk)
  • EIOPA guidelines on internal models
  • ASOP No. 39 (catastrophe losses in pricing)
  • ASOP No. 38 (use of external models)
  • ASOP No. 56 (modelling)
Syllabus

6 modules · 300 h

  1. 1.1Why the tail matters in reinsurance
  2. 1.2Scarcity of data in the extreme zone
  3. 1.3Heavy-tailed distributions
  4. 1.4Biases of classical methods
  5. 1.5Probable maximum loss and its interpretation
  6. 1.6Return period and its correct use
  7. 1.7Industry vocabulary and acronyms
  8. 1.8Parameter and model uncertainty
  9. 1.9Communicating extreme risk
  10. 1.10Exercise: critiquing a tail estimate
  1. 2.1Block maxima and their distribution
  2. 2.2Peaks over threshold
  3. 2.3Choice of threshold and its effect
  4. 2.4Parameter estimation
  5. 2.5Graphical diagnostics of the fit
  6. 2.6Confidence intervals
  7. 2.7Extrapolation beyond the data
  8. 2.8Limitations of the approach
  9. 2.9Application to reinsurance claims
  10. 2.10Practice: fitting a tail to real data
  1. 3.1Model structure: hazard, exposure and vulnerability
  2. 3.2Loss exceedance curves
  3. 3.3Primary and secondary uncertainty
  4. 3.4Differences between model vendors
  5. 3.5User adjustments and their justification
  6. 3.6Quality of exposure data
  7. 3.7Unmodelled risks
  8. 3.8Climate change in models
  9. 3.9Validation against real events
  10. 3.10Practice: critical reading of a model output
  1. 4.1Tail correlation
  2. 4.2Copulas and their choice
  3. 4.3Dependence between lines and regions
  4. 4.4Aggregation of losses from several sources
  5. 4.5Simulation of the aggregate distribution
  6. 4.6Effect of dependence on capital
  7. 4.7Joint scenarios
  8. 4.8Hidden accumulation risk
  9. 4.9Sensitivity tests
  10. 4.10Practice: aggregating two portfolios
  1. 5.1Setting priorities and limits
  2. 5.2Pricing high layers
  3. 5.3Retrocession decisions
  4. 5.4Calculating capital for extreme risk
  5. 5.5Reverse stress scenarios and their use
  6. 5.6Independent model validation
  7. 5.7Documentation and declared assumptions
  8. 5.8Communication to management
  9. 5.9Periodic review
  10. 5.10Practice: extreme risk report
  1. 6.1Choice of portfolio and peril
  2. 6.2Exploratory data analysis
  3. 6.3Fitting a tail model
  4. 6.4Using a catastrophe model
  5. 6.5Aggregation and dependence
  6. 6.6Application to a reinsurance decision
  7. 6.7Validation and assumptions
  8. 6.8Communicating uncertainty
  9. 6.9Methodology report
  10. 6.10Project defence
Methodology, software & labs

How you'll learn

Fully 100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, making it possible to combine with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is R (evd, extRemes), Python (SciPy), Moody’s RMS, Verisk Touchstone, Oasis LMF, in free, academic or evaluation versions. Practical work is based on real procedures for fitting heavy-tailed distributions, copula modelling and validation of catastrophe models. Assessment is continuous by module plus the final integrative project.

Accredited laboratories(EN ISO/IEC 17025) with rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented final project:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Choice of portfolio and peril

Explain the statistical problem of extreme events.

Exploratory data analysis

Apply extreme value theory with GEV and GPD distributions.

Fitting a tail model

Interpret the results of commercial catastrophe models.

Using a catastrophe model

Model dependence and aggregation with copulas.

Aggregation and dependence

Calculate risk measures such as VaR and TVaR in the tail.

Career opportunities

Where this takes you

Reinsurance actuaryCatastrophe modellerQuantitative risk analystInternal model validatorEconomic capital analystActuarial consultant
Admissions & scholarships

Requirements

  • No prior degree is required.
  • Internet connection and a computer capable of running the programme’s software.
  • Sufficient Spanish to follow technical regulatory reading.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

FAQ

Frequently asked questions

The programme comprises 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

The work is carried out with R (evd, extRemes), Python (SciPy), Moody’s RMS, Verisk Touchstone, Oasis LMF. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.

A SEIUM Institute own diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the advanced programme. Check the credit transfer policy when you enrol.

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