Description: Institutional investors such as pension plans and insurance companies have enjoyed strong equity returns for a length of time with few historical precedents. Nearly a decade after the end of the financial crisis, strategies to provide downside protection remain expensive and the performance of traditional risk-managed funds has disappointed investors.

Description: In this session, senior pension and insurance investment professionals will share their thoughts on current investment topics. Attendees will hear about leading edge investment ideas and practices from industry leaders.

Description: Panelists discuss: [1] how they determine appropriate issuer and concentration limits in a below-investment-grade portfolio, [2] the best techniques for monitoring and managing exposures as they improve or deteriorate, and [3] opportunities for managing credit risk through synthetic structures and derivatives. From the 2002 Record of Society of Actuaries, Volume 28, No. 3. Session 55PD of the Boston Annual Meeting.

Description: Over a short span of two weeks, the financial system has observed the collapse of two mid-sized U.S. commercial banks, California-based Silicon Valley Bank (“SVB”) and New York-based Signature Bank, and a takeover of troubled Credit Suisse by a rival in Europe. Other banks that serve a similar demographic as SVB, such as First Republic Bank and PacWest Bancorp, have also experienced an elevated level of customer deposit withdrawals while their stock prices plummeted. The U.S. Federal Deposit Insurance Corporation (FDIC) quickly stepped in and guaranteed all deposits from the failed banks to prevent a broader crisis in the banking system. The current turmoil in the financial system is a stark reminder of the importance of effective risk management and regulation.

Description: Applications of Lagrangian distributions to modelling claim frequency data in an insurance portfolio is a relatively new concept. The major difficulty is that the generating functions of these distributions cannot be expressed in terms of elementary functions. Also deriving moments and/or cumulants is somewhat tedious. This article illustrates how to use Maple effectively to overcome these difficulties.

Description: This session will serve as a guide on how to implement a factor-based optimization in the Strategic Asset Allocation process. The session will review a factor-based investing approach and cover the top-down macroeconomic drivers of risk and return for various asset classes. We will then illustrate implementation of this investing approach and determination of an optimal Strategic Asset Allocation through a pension fund case study. There will be an interactive element to the session as the factor sensitivities of various portfolios can be examined in real-time.

Description: This article deals with enterprise risk management and how the techniques contained within the discipline can help prevent unforeseen large mistakes in strategy, product design, and product management

Description: The article discusses the characteristics of the high-yielding bank loan asset class.

Description: Recently, there has been growing interest among insurance and investment experts to focus on the use of a tail conditional expectation as a measure of risk, since it shares properties that are considered desirable and applicable in a variety of situations. This paper examines above risk measure in the case of a multivariate gamma portfolio. We demonstrate the explicit formulas for tail conditional expectation and based on it capital allocation when the proposed multivariate model consists of dependent and independent gamma marginals

Description: Contains two separate articles, called notes. The first note, “A note on correlation in mean variance portfolio theory,” is an elementary discussion of how negative correlation coefficients can be, given any set of N random variables. The second note, “Two essential formulas in actuarial science and financial mathematics,” gives a more or less self-contained introduction to Jensen’s inequality and to another formula, which the author decided to call the “survival function expectation formula.”