2018 | ISBN-10: 1498742165 | 402 Pages | PDF
The quantitative modeling of complex systems of interacting risks is a fairly recent development in the financial and insurance industries. Over the past decades, there has been tremendous innovation and development in the actuarial field. In addition to undertaking mortality and longevity risks in traditional life and annuity products, insurers face unprecedented financial risks since the introduction of equity-linking insurance in 1960s. As the industry moves into the new territory of managing many intertwined financial and insurance risks, non-traditional problems and challenges arise, presenting great opportunities for technology development.
Today's computational power and technology make it possible for the life insurance industry to develop highly sophisticated models, which were impossible just a decade ago. Nonetheless, as more industrial practices and regulations move towards dependence on stochastic models, the demand for computational power continues to grow. While the industry continues to rely heavily on hardware innovations, trying to make brute force methods faster and more palatable, we are approaching a crossroads about how to proceed. An Introduction to Computational Risk Management of Equity-Linked Insurance provides a resource for students and entry-level professionals to understand the fundamentals of industrial modeling practice, but also to give a glimpse of software methodologies for modeling and computational efficiency.
Features
Provides a comprehensive and self-contained introduction to quantitative risk management of equity-linked insurance with exercises and programming samples
Includes a collection of mathematical formulations of risk management problems presenting opportunities and challenges to applied mathematicians
Summarizes state-of-arts computational techniques for risk management professionals
Bridges the gap between the latest developments in finance and actuarial literature and the practice of risk management for investment-combined life insurance
Gives a comprehensive review of both Monte Carlo simulation methods and non-simulation numerical methods