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International Series on Actuarial Science
Computation and Modelling in Insurance and Finance
Focusing on what actuaries need in practice, this introductory account provides readers with essential tools for handling complex problems and explains how simulation models can be created, used and re-used (with modifications) in related situations. The book begins by outlining the basic tools of modelling and simulation, including a discussion of the Monte Carlo method and its use. Part II deals with general insurance and Part III with life insurance and financial risk. Algorithms that can be implemented on any programming platform are spread throughout and a program library written in R is included. Numerous figures and experiments with R-code illustrate the text. The author's non-technical approach is ideal for graduate students, the only prerequisites being introductory courses in calculus and linear algebra, probability and statistics. The book will also be of value to actuaries and other analysts in the industry looking to update their skills.
1025 pages, Kindle Edition
First published March 1, 2014
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February 24, 2023The reader must be familiar with elementary calculus, probability and matrix algebra… should preferably have some programming experience.
the exercises make use of the open-source R software which permits Monte Carlo simulation
Typical examples are compensations for claims in general insurance, pension schemes interrupted upon death in life insurance and future values of shares and bonds in finance.
risks X andY seen as random variables. We shall know their values eventually (after the event), but for planning and control and to price risk-taking activities we need them in advance and must fall back on their probabilities.
Monte Carlo method or stochastic simulation. It belongs to the realm of numerical integration
Excel and Visual Basic are standard in the industry and may be used even for simulation. Much higher speed is obtained with C, Pascal or Fortran
Regulators demand sufficient funds to cover X with high probability. The amount qe is known as the solvency capital or reserve.
For ideas on the practical side of the actuarial profession, try Szabo (2004). A general introduction to assets and liabilities is Booth et al. (1999), with management issues covered in Williams et al. (1998).
the exercises make use of the open-source R software which permits Monte Carlo simulation
Typical examples are compensations for claims in general insurance, pension schemes interrupted upon death in life insurance and future values of shares and bonds in finance.
risks X andY seen as random variables. We shall know their values eventually (after the event), but for planning and control and to price risk-taking activities we need them in advance and must fall back on their probabilities.
Monte Carlo method or stochastic simulation. It belongs to the realm of numerical integration
Excel and Visual Basic are standard in the industry and may be used even for simulation. Much higher speed is obtained with C, Pascal or Fortran
Regulators demand sufficient funds to cover X with high probability. The amount qe is known as the solvency capital or reserve.
For ideas on the practical side of the actuarial profession, try Szabo (2004). A general introduction to assets and liabilities is Booth et al. (1999), with management issues covered in Williams et al. (1998).
September 13, 2018
Read it for work. Quite friendly and thoughtful, though not enough of those (nor broad enough) to be a good introduction to the modern way of science (which I am still looking for). He is extremely direct about the costs and benefits of numerical work, and his maths is all well-motivated.
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