Description: This paper investigates techniques for the assessment of model error in the context of insurance risk analysis.

Description: This paper investigates techniques for the assessment of model error in the context of insurance risk analysis.

Description: The direct and indirect effects of the pandemic have impacted many actuaries’ work since the spring of 2020. This article addresses three considerations actuaries should account for when using risk evaluation models which are focused on projecting remote, highly unlikely losses: model effectiveness, asset risk, and insurance (mortality and mortality) risk.

Description: The abstract for the paper Property/Casualty Insurer Economic Capital Using a VaR Model.

Description: A presentation at the Actuarial Research Conference in August 2006 in Montreal. This paper discusses the Basel VaR Value at Risk test of a bank's VaR model. It proposes an alternative, Quality Control of Risk Measures QCRM Hypothesis test. The alternative is more likely to reject an incorrect VaR model.

Description: This paper discusses a natural and convenient statistic, the nested L-statistic, investigates its performance using a simulation study, and considers an example involving the comparison of risk measure values where the risks of interest are those associated with tornado damage in different time periods and different regions. From the Actuarial Research Clearing House, 2008, Vol.1.

Description: The editorial cautions that while the development of more sophisticated models may increase efficiency, it is important to consider the very real risk management costs associated with the increased complexity, efficiency and overconfidence in the predictive power of models.

Description: Findings from EY Insurance Risk Management Team's 2016 (annual) Insurance Chief Risk Officer Survey.

Description: Value-at-Risk—an Overview Part Two of Two by Glyn Holton from The Financial Reporter, May 1999, Issue No. 39.

Description: Sometimes, rare things happen and the least expected occurs. Indeed, some events occur once or twice in a lifetime leaving little room to learn from experiences. In financial markets, extreme events, no matter how rare, could have a profound impact on a company or even the whole country. Insurers are also not free from the impact of catastrophic large-scale extreme events. As a result, with pervasive economic and financial revolutions sweeping our world and potential devastative catastrophes, the increasing interest in tail risk management is fuelled by practical issues, including investment downside risk and insurance catastrophe risk. Thus, developing statistical techniques to model extreme investment and insurance losses is certainly a major task for risk managers. the purpose of our paper is to apply moment methods to estimate the joint events such as concurrent extreme investment loss and insurance loss. A novel aspect of this article is that it takes into account the correlation between different assets and insurance lines of business.