Description: This abstract describes a paper that considers the square-root process and its time integral as they occur in pricing options in stochastic volatility models.

Description: This paper presents a solution to the problem of discounting cash flows when the cash flows and discount factors are random variables with given distributions.

Description: The problem of finding the distribution of sums of lognormally distributed random variables is discussed. References going back to the 1930’s are given, as well as some possible solutions. A formula for the characteristic function of one lognormal is stated, and then the moments and distribution of the logarithm of sums of lognormals are considered.

Description: This paper focuses on two aspects of SFAS 87. The first is the availability of the discount rate, and its consequences. The second is the corridor approach to gains and losses amortization. From Actuarial Research Clearing House 1993, VOL. 2.

Description: This paper studies the theoretical and numerical convergence of Gram-Charlier series applied to the pricing of Asian options.

Description: This paper studies the variability of pension costs and fund levels under various funding methods. From Actuarial Research Clearing House 1990, Vol. 1.

Description: Feature article discussing the use of stochastic volatility models in the pricing of investments and options.

Description: This paper addresses the extension of the theory of life contingencies to a stochastic interest environment and its application to solvency valuation and surplus. A discussion of the paper follows. From Transactions of Society of Actuaries 1990, Vol. 42.

Description: Research paper assessing how contributions and fund levels are affected when the rates of return of the plans' assets form an i.i.d. sequence of random variables.

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.