Reference Guide to Monte Carlo Simulation in Insurance
Summary
The discussion surveys starting points for learning Monte Carlo methods in insurance, especially dynamic financial analysis (DFA). It contrasts general financial simulation texts, which may emphasize derivative valuation, with insurance needs such as risk management, economic scenario generation, and representing a life or non-life insurer’s business. The question highlights the challenge of finding a single, current reference that covers these areas comprehensively.
The response points to an actuarial association overview, simulation material in a non-life loss-modeling textbook, and simulation coverage within quantitative risk management literature. It cautions that relevant work is dispersed across publications of uneven depth, including technical papers and presentations. For further searching, it suggests terms around internal models, European solvency regulation, enterprise risk management, and economic scenario generators. This is a reading map rather than a comparison of methods or evidence from an empirical study; it does not assess the recommended sources’ coverage in detail or establish a definitive reference for life insurance.
Key ideas
- Dynamic financial analysis applies stochastic simulation to insurance risk and financial planning.
- Insurance simulation may require modeling risk management, economic scenarios, and insurer operations beyond derivative pricing.
- Useful material appears in actuarial overviews, loss-modeling texts, and quantitative risk management references.
- The literature is fragmented, and its depth and quality vary across publication types.
- Search terms related to internal models, solvency rules, enterprise risk, and scenario generators can help locate relevant work.
Tags
Full text
# References for Monte Carlo in insurance # References for Monte Carlo in insurance As the title suggests, I'm looking for reference works on Monte Carlo methods in insurance. Wikipedia tells me that the terminus technicus here is dynamic financial analysis. I'm about to start a carreer in this field and have a strong math background. So I'm trying to catch up on the financial/numerical side of things. I found the book "Monte Carlo Methods in Financial Engeneering" by Glasserman to strike a nice balance between application and background. For my purposes, though, it has a few shortcomings: 1) It focuses on derivative pricing more than on risk management/macroeconomic scenario generation etc. 2) The 15 years since publication are a long time in such a fast-evolving technical field, I guess? 3) There is no hint whats-o-ever on how to construct a stochastic representation of the business modell of a life/non-life insurer. The only DFA reference Glasserman gives is to the paper Kaufmann, Gardmer, Klett, which is from 2001 and is concerned with non-life insurance. Is there a comprehensive reference in this direction, covering life insurance, aswell? ## Answer by g g (score 1) https://quant.stackexchange.com/a/41379 The only attempt at a comprehensive overview I know of is this book by the IAA (International Actuarial Association). A standard textbook on non-life insurance Loss models has a section on simulation. The excellent and comprehensive coverage in Quantitative Risk Management contains many implicit and explicit references to simulation. That said, most of the literature on stochastic simulation in insurance is scattered in white papers, presentations and various publications of vastly varying depth and quality. In addition to "Dynamic Financial Analysis" you could use appropriate keyword combinations containing "internal models", "Solvency II" , "insurance enterprise risk management" and "economic scenario generators". If you are specifically interested in economic scenario generators, white papers by Moody's, who are a market leader in the insurance sector, might be a start.
Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.