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CS2: Risk Modeling and Survival Analysis
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Module 9
Modeling Insurance Losses
1
Maximum Likelihood Estimation for Loss Distributions
Fit standard loss distributions (e.g., Exponential, Pareto, Lognormal) to claims data using maximum likelihood.
2
Modeling Truncated and Censored Loss Data
Incorporate data features like truncation and censoring when fitting loss distributions.
Comparing Loss Models with AIC, BIC, and LRTs
Apply model selection criteria (AIC, BIC, likelihood ratio tests) to compare fitted loss models.
4
Fitting GEV to Block Maxima Data
Apply the block maxima method to fit the Generalized Extreme Value (GEV) distribution to data.
5
Peaks Over Threshold: Fitting the GPD
Apply the peaks-over-threshold method to fit a Generalized Pareto Distribution (GPD) to excess losses.
6
Extreme Value Theory: Estimating Quantiles and Tail Probabilities
Estimate high quantiles (Value-at-Risk) and tail probabilities using extreme value models.
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Mortality Projection Models
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Copulas and Dependence Modeling