Description: Many risk management decisions involve the timing of implementing risk strategies. The cost of hedging, capital raising and securitization changes with the economic environment. The timing of de-risking and investment in new risk management functions affect the cost and benefit of these projects as well. Like investment timing, it is important to consider the timing in risk management decisions to maximize the gain of risk management projects. This paper discusses methods of determining the appropriate timing of implementing a risk management strategy or investing in risk management projects. It explains the human biases that may lead to inferior timing decisions. It also covers the costs and benefits of risk management projects and the impact of new information on the decision-making process. Timing considerations for financial risk hedging, insurance risk hedging and investment in new risk management projects are explained. Factors used for timing decisions include economic conditions, market volatility, credit rating, borrowing cost, capital adequacy and financial resources.

Description: Review of stochastic, modeled geo-spatial risk metrics for insurance loss concentration and clustering and their counter point use for diversification

Description: This article is a summary of the authors’ two other articles where they discuss pension systems and whether they do in fact distort retirement decisions.

Description: This paper provides an overview of those cognitive biases most often responsible for flawed risk assessments and provides practical techniques to mitigate them. Through a combination of mathematical rigor, case study, metaphor, and logic, the author highlights systematic errors in our thinking about risk and provides ways to avoid them.

Description: In this paper, we derive three important longevity Greeks on the basis of an extended version of the Lee-Carter model that incorporates stochastic volatility.

Description: This paper discusses retirement planning information and the signals it provides. It focuses on the issue of communicating lump sum versus income benefits. It discusses challenges in communicating income benefits and provides some alternatives.

Description: Many risk management decisions involve the timing of implementing risk strategies. The cost of hedging, capital raising and securitization changes with the economic environment. The timing of de-risking and investment in new risk management functions affect the cost and benefit of these projects as well. Like investment timing, it is important to consider the timing in risk management decisions to maximize the gain of risk management projects. This paper discusses methods of determining the appropriate timing of implementing a risk management strategy or investing in risk management projects. It explains the human biases that may lead to inferior timing decisions. It also covers the costs and benefits of risk management projects and the impact of new information on the decision-making process. Timing considerations for financial risk hedging, insurance risk hedging and investment in new risk management projects are explained. Factors used for timing decisions include economic conditions, market volatility, credit rating, borrowing cost, capital adequacy and financial resources.

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: This paper demonstrates the use of Quantile Regression in the development and understanding of conditional value at risk (VaR) models for enterprise risk management.

Description: We develop a model based on causal mortality in order to better understand inequalities in life expectancy across socioeconomic circumstances.