Description: The main aim of this paper is to apply some of the techniques of modern time series and econometric analysis to analyse investment data in order to better understand the nature of long run relationships in investment series typically used in stochastic investment models. The paper attempts to identify and address fundamental issues that need to be considered before developing a particular stochastic investment model.

Description: One of the most important problems in Finance is the valuation of financial securities written on underlying assets whose prices are subject to uncertainty. This paper presents a technique to estimate the unknown stochastic volatilities by solving the inverse problem associated with the parabolic partial differential equation governing risk neutral derivative security prices. This technique can be applied in a very general multi-factor setting and numerical examples are provided.

Description: This paper addresses the evaluation of long-term returns when the short interest rate is modeled by a general diffusion process. By deriving the long-term return dynamics and invoking the Feyman-Kac formula, the long-term return is represented as the solution of a partial differential equation. A finite difference method is derived for the valuation of the long-term return. The method could be used for hedging interest rate risk, pricing and managing interest rate derivatives and interest rate sensitive insurance products. Numerical examples are included.

Description: This paper represents a brief progress report on research, being sponsored by the Casualty Actuarial Society and the Society of Actuaries, in the area of modeling of economic series. In particular, research is being undertaken to provide a foundation for the actuarial community’s modeling of a variety of economic variables, including interest rates, equity returns, inflation, unemployment rates, and real estate price levels. The specific objectives of this research are to provide a summary of the literature in the area of economic scenario modeling, identify data sources and methodologies in this area useful to the actuarial profession, and produce working software for economic modeling, including appropriate documentation, to be made available to the general actuarial community in order to allow actuaries to use and build upon this research. From Actuarial Research Clearing House 2004, Vol. 1.

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.

Description: This is the User's Guide that accompanies the Inflation Generator Excel spreadsheet which is associated with the research report entitled 'The Effect of Deflation or High Inflation on the Insurance Industry.'

Description: This abstract describes a paper that presents preliminary observations with respect to probabilistic fuzzy systems.

Description: This paper develops a new technique, which allows the analyst to maximally use all thte historical interest rate information available in forecasting interest rates. Interest rates' daily change do not follow a stationary random process over long time periods. Instead, the process is comprised of stationary periods, each a few years long, with relatively short transitional periods between different stationary periods.

Description: This research was funded by the Natural Sciences and Engineering Research Council of Canada [NSERC] Discovery Grant 36860–06. Loss reserving is one of the most challenging tasks facing actuaries. Numerous approaches have been developed to give reasonable estimates. Generalized linear models [GLMs] are becoming quickly popular statistical analysis methods to estimate loss reserves. However, most of these models are aggregate reserving methods based on loss development triangles, without using information with regard to the actual claims processes. This paper establishes a more sophisticated structural reserving method incorporating more detailed information, such as the premium exposure emergence pattern, the loss emergence pattern and the loss development pattern, within the framework of GLMs.

Description: Prior work indicates that a regime-switching stochastic model with randomized regime parameters creates a more plausible set of extreme paths than do the usual stochastic interest rate models. Generalizing the Black-Karasinski model by randomizing the mean reversion target provides an example. Better to understand such models, as well as to calibrate their parameters without trial and error runs of a stochastic model, this paper uses an asymptotic expansion to estimate the moments of the integrated stochastic process over time. Mention is made of a family of related asymptotic expansions that could be of wider interest.