Description: Modeling experience sharing by David Yu who leads the retirement product and asset model development team within Prudential Financial

Description: With this paper, a new theory of immunization is introduced in which the approach is multivariate, and the goal is stochastic in the sense of minimizing stochastic risk. Discussions of this paper are included.

Description: Presentation discussing modeling of daily and hourly rainfall, both occurrence and amount. These distributions would be needed to price derivative or insurance contracts involving rainfall amounts.

Description: This presentation considers the pricing of European derivatives in a Black-Scholes model with stochastic volatility. The presentation shows how Parseval’s theorem may be used to express those prices as Fourier integrals. This is a significant improvement over Monte Carlo simulation in many cases. The main ingredient in this presentation's method is the Laplace transform of the ordinary [constant volatility] price of a put or call in the Black-Scholes model, where the transform is taken with respect to maturity [T]. The presentation derives these formulas and then applies them to the case where volatility is modelled as a Markov chain in continuous time, the so-called 'Markov regime switching model'. This model has been used previously in stochastic volatility modelling, but mostly with only N=2 states. The presentation shows how to use N=3 states without difficulty, and how larger number of states can be handled. Numerical illustrations are given, including the volatility smile in a three-state model.

Description: In this paper we discuss some properties of the nth stop-loss order and their application in risk premium principles. We give a necessary condition and a sufficient condition for nth stop-loss orders. They are convenient tools to construct risk pairs with nth stop-loss orders. We show that exponential premium principles can diferentiate between losses more finely than the net premium principles under some conditions.

Description: Presented at May 2005 Spring Meeting. Panelists discuss the use of stochastic embedded value 'EV' in financial management and risk measurement, including considerations in reconciling period-to-period results.

Description: This paper illustrates a current approach to capital allocation, referred to herein as Tail Contribution Analysis TCA, using it to evaluate the impact of changes in an insurer’s operations by allocating capital to individual lines of business. It then highlights a common problem in the implementation of the procedure arising from reserve volatility and briefly notes the failure of the methodology to account for a firm’s changing levels of risk under various strategies. Finally, it introduces an alternative method for analyzing business segments and strategies called Economic Profit Analysis EPA. We will discuss EPA’s key feature, “volatility replication,” an intuitively appealing process of building investor expectations and capital market information into the risk-and-return measurements used to evaluate strategic decisions. Finally, we will illustrate the benefit of using EPA in overcoming the problems of TCA and thereby providing concise reliable information for business decision making.

Description: This paper discusses the binominal financial and insurance models. In addition, the paper expands the discussion to the martingale probabilities measures for insurances, annuities, endowment insurances, and equity-linked products.

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: A European call option pricing model similar to the Black-Scholes equation [1] is derived. Like the Black-Scholes equation, the model is based upon an assumption of a lognormal distribution of the price of a risky, non-dividend-paying security at a given point in the future. However, unlike Black-Scholes, the market's risk preference is recognized through a parameter that permits refinement of the model to agree either with experience or with future advances in the underlying theory.