Abstract
A Bayesian MCMC stochastic loss reserve model provides an arbitrarily large number of equally likely parameter sets that enable one to simulate future cash flows of the liability. Using these parameter sets to represent all future outcomes, it is possible to describe any future state in the model’s time horizon including those states necessary to calculate a cost of capital risk margin. This paper shows how to use the MCMC output to: (1) Calculate the risk margin for an “ultimate” time horizon; (2) Calculate the risk margin for a one-year time horizon; and (3) Analyze the effect of diversification in a risk margin calculation for multiple lines of insurance.
Volume
Winter
Page
1-27
Year
2017
Keywords
Stochastic Loss Reserving, Bayesian MCMC, Capital Requirements, Risk Margins
Categories
Financial and Statistical Methods
Statistical Models and Methods
Bayesian Methods
Actuarial Applications and Methodologies
Capital Management
Capital Requirements
Actuarial Applications and Methodologies
Reserving
Publications
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