Description: This paper presents a model designed to help actuaries analyze and quantify C3 risk, which is the risk of loss due to changes in interest rates or the shape of the yield curve.

Description: For several years there has been discussion in the insurance industry of the need for additional capital sources to participate in insuring the financial risk posed by natural catastrophes. A number of methods for accessing the capital markets have been introduced and some insurers are using these to hedge exposure to catastrophe loss. This paper summarizes some of the discussion and describes some of the methods proposed. It then focuses on one of these methods, the use of catastrophe insurance options. Results of simulations are given which may serve as a guide to insurers interested in using these options as part of their risk management strategy and also as an indication to investors of the value of these instruments as a new component of an investment portfolio.

Description: A research project with the Society of Actuaries on interest rate hedging on traditional life and health insurance.

Description: This research report that provides a literature survey of methodologies applying machine learning to insurance claim modeling.

Description: Discusses how a small health insurance company may be able to create and successfully maintain its wealth through careful actuarial model considerations used in calculating reserves. A traditional and a conservative approach to calculating the reserves are compared. The conservative approach was found particularly useful when estimating reserves for an individual health insurance company with numerous product lines each relatively small in volume.

Description: In summary, the goal of this report is to foster better industry-wide education, training and practice that decreases the future range and scope of what leading risk management professionals will henceforth label as being unpredictable as events now commonly thought to be so.

Description: This research report that provides a literature survey of methodologies applying machine learning to insurance claim modeling.

Description: This report looks at identifying drivers of systemic risk to China’s insurance industry and develops a measurement method for systemic risk as well as systemic risk dependence between the insurance industry and other industries.

Description: In recent years a lot of time has been spent discussing a variety of different ways to measure the performance of an insurance company. One of the dominating concepts in the discussion of this has been 'Economic Capital' and with this the question of allocation of the [Economic] Capital with the intention of introducing a risk related measure of performance. In this paper, the authors view the performance measuring as a question of determining the appropriate distribution of a risk loading for participants in a risk pool having the overall risk priced by an actuarial premium principle. They show that the covariance principle can be found as the optimal solution to this risk sharing problem and show that the covariance principle fulfills some reasonable requirements. They furthermore suggest how the covariance principle can be at the basis of a target setting procedure for a multi-line insurance company.

Description: In his original monograph on portfolio selection, Markowitz [1952] discusses the tradeoff between the mean and variance of a portfolio. Since then, especially recently, much attention has been focused on asymmetric distributions to minimize risks with given return goal for investors who have special skewness preferences. To address this issue, this paper extends Krokhmal et al. [2002]’s approach by adding CVaR-like constraints to the traditional portfolio optimization problem. The CVaR optimization technique has the advantage of reshaping either the left or right tail of a distribution while not significantly affecting the other. Specifically, this approach is used to manage the skewness of asset-liability portfolios of financial institutions. In addition, the CVaR-like constraints approach is compared with traditional Markowitz method and some other alternatives such as, the CVaR approach [directly optimize CVaR], the Boyle-Ding approach as well as the mean-absolute deviation [MAD] approach. Numerical analysis provides empirical support for the superiority of CVaR-like constraints approach in terms of skewness improvement of mean-variance portfolios.