Description: In a stochastic world consideration of only a few deterministic scenarios can potentially be dangerous. The aim of the present research is to study the computation of the cost of mismatch using stochastic interest rate models instead of scenario testing. In this paper, two commonly used Stochastic interest rate models are considered: the one introduced by Vasicek 1977 and the one proposed by Cox, Ingersoll and Ross 1985.

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.

Description: In a series papers by Willmot and Lin, both exponential and non-exponential bounds for the tail probability of various compound distributions have been derived. In Wilmot, it was suggested that non-exponential bounds for the ruin probability were difficult to obtain using martingale arguments. In this note, we show how to use martingale inequalities to obtain some upper bounds for the ruin probability.

Description: The article discusses that drawbacks of the HJM Heath-Jarrow-Morton class of stochastic interest models and suggests the use of the Longstaff-Schwartz string model. It describes the use and benefits of the Lonstaff-Schwartz string model.

Description: In the present paper, we propose and investigate a numerical method of computing the probability distribution of S. Inversion of the Laplace transform moment generating function is shown to be a better approach than the existing recursive method for find the distribution of sum of several independent random variables. In the continuous case, in many situations, numerical inversion needs to be used to compute the probability distribution function of the sum.

Description: An insurance company starts with an initial surplus, collects premium, pays claims to policyholders and pays dividends to stockholders. What remains is the following year’s surplus. The process continues. This paper describes how to calculate the distribution of surplus for any given year. With these distributions, one can make the calculations of premiums and dividends.

Description: We propose asymptotically correct two-sided bounds for random sums where the number of summands has an arbitrary distribution which can be viewed as ruin probabilities or accumulated claim sizes in various risk processes. The bounds are fairly new and they reveal the real accuracy of some well-known and widely used asymptotic formulas. It turns out that this accuracy can be poor and the range where these formulas give appropriate approximations depends on the shape of the tail distribution of the summands. This dependence is examined in the research.

Description: This paper shows how the results of copula based capital aggregation models can always be locally approximated by relatively simple formulas. The paper defines the concepts of diversification factor and tail correlation matrix and describes methods for estimating these quantities from simulated data. We show how these ideas can be put into practice as both computational shortcuts and presentation tools.

Description: As stochastic modeling has evolved, it has divided down two paths—real-world and risk-neutral. This article discusses this division and shows how these paths may be re-united through the use of deflators.

Description: We consider a class of compound renewal risk process with claim waiting times have a discrete Km distribution. The classical compound binomial risk model is a special case when m = 1. Both recursive and explicit formulas are derived for the expected discounted penalty function due at ruin, for the surplus before ruin and the deficit at ruin. Many ruin related quantities can be analyzed through the penalty function, e.g., ruin probability, the p.g.f. of the time of ruin, joint and marginal distributions of the surplus before ruin and the deficit at ruin, claim causing ruin, as well as their moments. Detailed discussions are given in two special cases: claim sizes are rationally distributed, or the claim sizes distribution have a finite support.