A. Voudouri

A stochastic model for proactive risk management decisions

The analysis of stochastic discounting models provides one of the more powerful tools available for decision-making under conditions of uncertainty. This paper is mainly devoted to the establishment of properties and applications of a particular stochastic discounting model. More precisely, the paper investigates some of the theoretical problems encountered in a stochastic discounting model when assessing mutually exclusive options, and establishes the advantages of considering risk retention in proactive risk-management programs.

Certain selecting and underreporting processes

In this paper, we present a model for promotional advertising and underreporting of incomes. The model is based on the integral part of the product of a discrete random variable with a continuous uniformly distributed random variable.

Analytical and simulation techniques for discounting binomial random sums

Present-value models have often proved invaluable within the financial decision-making process. Such models are becoming increasingly more complex in their stochastic formulation and more sophisticated in their statistical objectives. Recent articles attempt to make exact inferences regarding the probability distribution of the present value. This paper establishes the result that whenever exponential timing is associated with the investment, then @a-unimodality is automatically induced into the present-value distribution irrespective of the selection criteria of the components of that investment. Furthermore, the conditions leading to the familiar (0) unimodality are determined, whereby it often appears to be financially more prudent to delay the timing mechanism than to exercise caution in the selection of the components.

A stochastic integral arising in discounting continuous cash flows and certain transformed characteristic functions

Abstract Transformed characteristic functions are universally recognized as the most powerful tools for investigating distribution functions of complicated stochastic models. The paper is mainly devoted to the establishment of properties and applications of a particular convolution model. More precisely, the paper derives the characteristic function of a convolution model based on a stochastic integral and provides applications of this model in discounting continuous cash flows.

Analytical and computer simulation techniques for a stochastic model arising in discounting continuous uniform cash flows

Present-value models are currently available for both single cash flows and continuous uniform cash flows under uncertain timing. Recent work by the authors has concentrated mainly on establishing theoretical results concerning the conditions under which unimodality will be introduced into the present-value distribution, particularly under exponential timing. Apart from the conventional (0) unimodality, there are two other forms of unimodality available which refer more to the nature of the unimodal behaviour rather than its location. When the timing mechanism operating for a continuous uniform cash flow is modelled by a geometrically distributed sum of exponential inter-assessment times, this paper establishes that the present-value distribution adopts a form of unimodality which is conceptually and structurally distinct from that form of unimodality adopted within the single cash flow analogue. Each present-value distribution will therefore become (0) unimodal under different prevailing economic conditions. One financial implication of these results is that it should be possible to develop coherent funding strategies for selecting a single cash flow option or a continuous uniform cash flow option having due regard to the current financial climate.

A stochastic discounting model arising in competing risks management

Abstract Stochastic discounting models have been proved very important for decision making under conditions of uncertainty in many applied fields. This paper is mainly devoted to the establishment of properties for the distribution of a stochastic discounting model. The paper, also, provides applications of this model in assessing competing risks.

Incorporating an Integral equation for characteristic functions in investigating a class of distributions

Abstract Integral equations of characteristic functions are very strong analytical tools for investigating the distributions of stochastic models. The paper makes use of an integral equation, incorporating the characteristic function of a renewal distribution and the characteristic function of a uniform random contraction, for establishing a characterization of a class of selfdecomposable distributions. Moreover, the paper establishes interpretations in stochastic modeling of risk severity reduction operations of the uniform random contraction and the characterization of that class of selfdecomposable distributions.

Limiting behavior of certain mixtures of distributions

AbstractПустьU — случайная ве личина равномерно распределенная на [0, 1] иY — не зависящая от нее некоторая случ айная величина. В стат ье изучаются свойства ф ункций распределени й случайных величинX=[1−U1/v/Y], построенных поU иY.

Stochastic vectors in modelling risk control operations for supporting intelligent behaviour of information systems

Random contractions constitute a kind of stochastic multiplicative models with particular theoretical and practical interest. Random sums are also recognised as important stochastic models with very useful applications in a wide variety of theoretical and practical disciplines. The paper concentrates on the formulation of a random vector based on two random sums of random contractions and the establishment of sufficient conditions for evaluating the corresponding characteristic function. Moreover, the paper establishes interesting interpretations of the formulated random vector in some fundamental areas of informatics.

Risk management decision making based on random sums incorporating thinned renewal processes in random time

The contribution of stochastic models to the development of risk management as a fundamental organizational discipline has been proved extremely important. It is now generally accepted that stochastic models are the most powerful analytical tools for evaluating the issues and alternatives and charting courses of action of an efficient risk management program. The main purpose of the present paper is to establish properties and applications in risk management decision making related to total risk severity measurement of a new stochastic model, which is based on a thinned stochastic renewal process and the product of two continuous, independent and nonnegative random variables.