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Stochastic Loss Reserving Methods
Last Updated: 2026-02-05 16:37:25
Abstract
Loss Reserving is one of the central topics in non-life insurance. Mathematicians and actuaries need to estimate adequate reserves for liabilities caused by claims. These claims reserves have influence all financial statements, future premiums and solvency margins. We present the stochastics behind various methods that are used in practice to estimate those loss reserves.
Objective
Our goal is to present the stochastics behind various methods that are used in prctice to estimate claim reserves. These methods enable us to set adequate reserves for liabilities caused by claims and to determine prediction errors of these predictions.
Content
We will present the following stochastic claims reserving methods/models: - Stochastic Chain-Ladder Method - Bayesian Methods, Bornhuetter-Ferguson Method, Credibility Methods - Distributional Models - Linear Stochastic Reserving Models, inlcusive one practice lesson - Bootstrap Methods - inflation, large claims, mid year reserving - Claims Development Result (solvency view) - Coupling of portfolios
General Information
- Language
- English
- Levels
- BSC , DR , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 30 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Stochastic Loss Reserving Methods |
|
2 h weekly |
Offered In
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Actuary SAA Education at ETH Zurich (Further pieces of information are available at Prof. M. Wüthrich's secretariat, HG F 42.)
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Electives (For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 14 of the required 26 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields.)
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Quantitative Finance Master (see Students in the Joint Degree Master's Programme "Quantitative Finance" must book UZH modules directly at the UZH. Those modules are not listed here.)
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MF (Mathematical Methods in Finance) (For possible additional course offerings see )
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