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In the late 1980s and early 1990s, there was a distinct effort for actuaries to combine financial theory and stochastic methods into their established models. [12] Ideas from financial economics became increasingly influential in actuarial thinking, and actuarial science has started to embrace more sophisticated mathematical modelling of ...
This page is concerned with the stochastic modelling as applied to the insurance industry. For other stochastic modelling applications, please see Monte Carlo method and Stochastic asset models. For mathematical definition, please see Stochastic process. "Stochastic" means being or having a random variable.
In credibility theory, a branch of study in actuarial science, the Bühlmann model is a random effects model (or "variance components model" or hierarchical linear model) used to determine the appropriate premium for a group of insurance contracts. The model is named after Hans Bühlmann who first published a description in 1967.
Actuarial Models: 2005: Course 3 2006: Exams MFE and MLC 1: Mathematical Foundations of Actuarial Science: 2000: Education system redesign 2005: Exam P and VEE 2: Interest Theory, Economics and Finance: 2000: Education system redesign 2005: Exam FM and VEE 3: Actuarial Models: 2000: Education system redesign 2005: Exam M 4: Actuarial Modeling ...
The output of a cat model is an estimate of the losses that the model predicts would be associated with a particular event or set of events. When running a probabilistic model , the output is either a probabilistic loss distribution or a set of events that could be used to create a loss distribution; probable maximum losses ("PMLs") and average ...
Predictive modelling uses statistics to predict outcomes. [1] Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. [2]
Actuarial credibility describes an approach used by actuaries to improve statistical estimates. Although the approach can be formulated in either a frequentist or Bayesian statistical setting, the latter is often preferred because of the ease of recognizing more than one source of randomness through both "sampling" and "prior" information.
In 2007, the Fundamentals of Actuarial Practice (FAP) were introduced to cover real-world topics such as insurance and professionalism with readings, case studies, and projects. [14] The FAP modules superseded the former Course 5 (Application of Basic Actuarial Principles) and Course 7 (Applied Modeling).