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Stochastic Modeling - Theory and Reality from an Actuarial Perspective
A guide for practitioners interested in understanding this important emerging field, Stochastic Modeling — Theory and Reality from an Actuarial Perspective presents the mathematical and statistical framework necessary to develop stochastic models in any setting (insurance or otherwise).
Sufficient mathematical detail is presented but no advanced background in mathematics or statistics is required.
You will find:
Techniques – such as Monte Carlo simulation and lattice models – commonly used in various applications of stochastic modeling.
Risk metrics that have applications in stochastic modeling, such as Value at Risk (VaR) and Conditional Tail Expectation (CTE).
Stochastic scenario generation for key risk factors affecting life insurance products, including interest rates, credit defaults, exchange rates, mortality and lapses.
Practical considerations of stochastic modeling, including model calibration and validation.
Model review and communication of results, of interest to senior practitioners already familiar with the fundamentals of stochastic modeling.
Case studies of life and non-life insurance companies, covering a range of topics relevant to capital and surplus modeling of life and non-life insurance companies, including Economic Capital calculations, stochastic reserve and capital calculations, embedded value analyses, and stochastic product pricing and risk management. Taken together, these case studies cover most of the widely-used insurance applications of stochastic modeling to date, and provide an illustrative framework from which future applications can be developed.