Safety Margins for Systematic Biometric and Financial Risk in a Semi-Markov Life Insurance Framework

Abstract

Insurance companies use conservative first order valuation bases to calculate insurance premiums and reserves. These valuation bases have a significant impact on the insurer's solvency and on the premiums of the insurance products. Safety margins for systematic biometric and financial risk are in practice typically chosen as time-constant percentages on top of the best estimate transition intensities. We develop a risk-oriented method for the allocation of a total safety margin to the single safety margins at each point in time and each state. In a case study, we demonstrate the suitability of the proposed method in different frameworks. The results show that the traditional method yields an unwanted variability of the safety level with respect to time, whereas the variability can be significantly reduced by the new method. Furthermore, the case study supports the German 60 percent rule for the technical interest rate.

An Application of a Semi Markov Model in Predicting the Fading Occurrences in Wireless Communication Channels

“…In the Markov model, the next state can be identified by a transition probability matrix. However, Semi-Markov processes (SMPs) are a broad class of stochastic processes which overgeneralize the Markov and renewal processes at the same time which are applied in many fields such as computer science [1], system reliability [2], finance [3], biology [4], seismic risk analysis [5], insurance [6], wireless networks [7][8][9], etc.…”

Business risk evaluation and management of Iranian commercial insurance companies

“…However, no comprehensive studies have been carried out on this subject in the insurance industry. Niemeyer (2015) presented a new to evaluate the risk in life insurance. Another goal of the study was to examine the correlation between the two consecutive states at different times.…”