Description: This paper proposes a combinatoric approach to express higher moments of the quadratic variation process in terms of higher order variations of the original process and autocorrelations of the original process.

Description: From an interactive forum session at the 2001 Valuation Actuary Symposium, held in Lake Buena Vista, Florida, November 29-30, 2001 Discussion of the major financial reporting issues and concerns of senior actuaries in life insurance companies. Discussion focused cash flow testing methods and interpretation of results.

Description: This is the abstract for the presentation on the effect of global warming on discounting methodology.

Description: The article announces the 2011 Actuarial Research Conference and discusses the themes of the upcoming conference.

Description: This abstract illustrates a calibration model against 60 years of historical data using a pragmatic mixture of filtering, maximum likelihood and method of moments techniques, and compare the resulting model with interest rates in contemporary economic scenario generators.

Description: In this article, the author gives us the recap of this year’s ARC conference, held at UConn.

Description: 1993 SOA Spring Meeting, Boston. This session is about management of multinational organizations from a home office perspective. It discussed cultural issues, regulatory issues, marketing issues, repatriation of profits, investment and currency risks.

Description: This presentation illustrates a calibration model against 60 years of historical data using a pragmatic mixture of filtering, maximum likelihood and method of moments techniques, and compare the resulting model with interest rates in contemporary economic scenario generators.

Description: This panel discussion session focuses on a proposal to modify the Standard Valuation Law involving statutory maximum valuation interest rates.

Description: The series expansion of a probability density function, known to actuaries by Esscher's name and to statisticians as the saddlepoint approximation, typically gets integrated to approximate probability values under the density, with a location parameter in the expansion chosen to optimize convergence of the integrated series. We review the general form, the accuracy and the derivation of the Esscher approximation. Then we propose using related series expansions for the derivative of a probability density function to approximate the value of the point of maximum likelihood for the density. The key is to make the value of the location parameter an unknown and assume it to be the point of maximum likelihood, which leads to a tractable optimization problem to find its value.