Description: Recently researchers, practitioners, and regulators had intense debates about how to treat the data collection threshold in operational risk modeling. There are several approaches under consideration—the empirical approach, the “naive” approach, the shifted approach, and the truncated approach—for fitting the loss severity distribution. Since each approach is based on a different set of assumptions, different probability models emerge. Thus, model uncertainty arises. When possible we investigate such model uncertainty analytically, otherwise we use Monte Carlo simulations. For specific parametric examples we consider exponential and Lomax distributions which are special cases of the generalized Pareto family. Our primary goal is to quantify the effect of model uncertainty on risk measurements. This is accomplished by evaluating the probability of each approach producing conservative capital allocations based on the value-at-risk measure. These explorations are further illustrated using a real data set for legal losses in a business unit (Cruz, 2002).

Description: Many top-performing businesses aren’t just risk aware, but rather risk intelligent. Such a posture helps prepare companies for a major event and can demonstrate to a regulator or auditor that an appropriately strong control framework is in place. Better still, companies gain real strategic value from a “risk adjusted” understanding not only of their threats and vulnerabilities, but also the upside opportunities associated with various risks. It starts with having both qualitative and quantitative information to identify, assess and manage risks. Strategic risk management, at its core, is about creating value by increasing transparency and accountability, and quality of information. More than a mere compliance exercise, effective risk management enables more informed strategic decision making, increased accountability and transparency, a greater likelihood of organizational success, and – ultimately – competitive advantage, as borne out by many studies that correlate risk management and financial results.

Description: This article proposes a framework for estimating mortality improvement models and analyzing the parameter uncertainty in such models.

Description: We apply grouped functional time series forecasting methods to produce point forecasts of mortality rates at older ages and price fixed-term annuities.

Description: Wavelet models are used to analyze risk by time and frequency and provide richer information than time series models.

Description: This “Application of Predictive Models Techniques to Measure Dynamic Policy Holder Behavior in Annuity Contracts” paper by Towers Watson researchers is one of a collection of essays that examine how existing or new methods or models can be applied to the life insurance industry.

Description: Report to give actuaries practical information and guidance on LTD claim credibility for case level pricing and underwriting.

Description: This paper talks about the risks of defined benefit and defined contribution plans.

Description: The abstract for a paper that focuses on default distributions and why they are important.

Description: The abstract for a paper that talks about the Swedish pension system reform.