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The volatility of a financial asset is an important input for financial decision‐making in the context of asset allocation, option pricing, and risk management. The authors compare and contrast four approaches to stochastic volatility to determine which is most appropriate to each of these various needs.

In this article, we present the results of a study examining the behavior of various inventory stocking methodologies in repair/rework operations. A major area of focus is on the sensitivity of key model parameters to stochastic replenishment lead times, product demand, and overhaul factors. A case study in a US Army depot provides validation for the effort. Simulation results indicate the current depot stocking methodologies are adequate in ideal conditions, but are less effective in more challenging and realistic scenarios. Results also indicate that some commonly used inventory models are quite robust to stochastic operating parameters in the unique/rework environment.

The purpose of this paper is to apply a numerical simulation of stochastic processes to the problem of real estate investment appraisal.

These uncertain operating costs are integrated into an enhanced dynamic simulation. To model the dynamics in the uncertainty of the cost schedule, a range of different types of stochastic processes is used. The operating costs are classified by cost drivers and an appropriate stochastic process is determined for each of the derived cost clusters. To optimise the capital structure in this application, heuristic optimisation with genetic algorithms is used.

The application of the model to real world investment situations shows that linear and deterministic modelling underestimates the risk‐generating effect of uncertain operating expenses, which often can lead to inefficient investment decisions.

In a further application of the model, the authors demonstrate the effect of uncertain operating costs on the optimal capital structure of real estate investments.

In contrast to models in the literature that are usually focussed on the income side, here the focus is on the uncertain dynamics of real estate operating costs as a key factor affecting return.

Procurement planning has always been a huge and challenging activity for business firms, especially in manufacturing. With government legislations about global concern over carbon emissions, the manufacturing firms are enforced to regulate and reduce the emissions caused throughout the supply chain. It is observed that procurement and logistics activities in manufacturing firms contribute heavily toward carbon emissions. Moreover, highly dynamic and uncertain business environment with uncertainty in parameters such as demand, supplier and carrier capacity adds to the complexity in procurement planning. The paper aims to discuss these issues.

This paper is a novel attempt to model environmentally sustainable stochastic procurement (ESSP) problem as a mixed-integer non-linear program. The ESSP optimizes the procurement plan of the firm including lot-sizing, supplier and carrier selection by addressing uncertainty and environmental sustainability. The model applies chance-constrained-based approach to address the uncertain parameters.

The proposed ESSP model is solved optimally for 30 data sets to validate the proposed ESSP and is further demonstrated using three illustrations solved optimally in LINGO 10.

The ESSP model simultaneously minimizes total procurement cost and carbon emissions over the entire planning horizon considering uncertain demand, supplier and carrier capacity.

One of the crucial issues in the contemporary finance is the prediction of the volatility of financial assets. In this paper, the authors are interested in modelling the stochastic volatility of the MAD/EURO and MAD/USD exchange rates.

For this purpose, the authors have adopted Bayesian approach based on the MCMC (Monte Carlo Markov Chain) algorithm which permits to reproduce the main stylized empirical facts of the assets studied. The data used in this study are the daily historical series of MAD/EURO and MAD/USD exchange rates covering the period from February 2, 2000, to March 3, 2017, which represent 4,456 observations.

By the aid of this approach, the authors were able to estimate all the random parameters of the stochastic volatility model which permit the prediction of the future exchange rates. The authors also have simulated the histograms, the posterior densities as well as the cumulative averages of the model parameters. The predictive efficiency of the stochastic volatility model for Morocco is capable to facilitate the management of the exchange rate in more flexible exchange regime to ensure better targeting of monetary and exchange policies.

To the best of the authors’ knowledge, the novelty of the paper lies in the production of a tool for predicting the evolution of the Moroccan exchange rate and also the design of a tool for the monetary authorities who are today in a proactive conception of management of the rate of exchange. Cyclical policies such as monetary policy and exchange rate policy will introduce this type of modelling into the decision-making process to achieve a better stabilization of the macroeconomic and financial framework.

The purpose of this paper is to research stochastic dynamic investment games with stochastic interest rate model in continuous time between two investors. The market interest rate has the dynamics of Duffie‐Kan interest rate.

Recently, there has been an increasing interest in financial market models whose key parameters, such as the bank interest rate, stocks appreciation rates, and volatility rates, are modulated by stochastic interest rate. This paper uses the Duffie‐Kan stochastic interest rate model to develop stochastic differential portfolio games. By the HJB optimality equation, a general result in optimal control for a stochastic differential game with a general utility payoff function is obtained.

Derive a general result in optimal control for a stochastic differential game with a general utility payoff function. The explicit optimal strategies and value of the games are obtained for the constant relative risk aversion utility games of fixed duration.

Accessibility and availability of stochastic interest rate data are the main limitations, which apply.

The results obtained in this paper could be used as a guide to actual portfolio games.

This paper presents a new approach for the optimal portfolio model under compound jump processes. The paper is aimed at actual portfolio games.

Whereas inventory theory traditionally assumes the periodic review inventory model (R, T), with an order‐up‐to level R, has a random demand and lead time coupled with a deterministic review interval T, firms often deviate from a strict adherence to a fixed review interval when they attempt to capture transportation scale efficiencies. Employing this policy introduces additional supply chain variability. This paper aims to provide an expression for the standard deviation of demand during the protection period, important in setting safety stock, as well as an expression for the amount of order variance amplification induced by a stochastic review interval.

Analytical modeling is used to develop the expression for the standard deviation of demand during the protection period as well as the calculation for the amount of order variance amplification induced by a stochastic review interval.

In terms of the variance of demand over the protection period, a stochastic review interval has a similar effect to that of a stochastic lead time, but its impact on demand variance amplification within the supply chain differs fundamentally. Specifically, a stochastic review interval creates an order batching bullwhip effect not identified in existing literature.

This study offers an expression for the standard deviation of demand during the protection period when stochastic review intervals are employed. The expression can be used to more effectively set safety stock. The paper also offers an expression for the order variance amplification induced by a stochastic review interval.

The study offers suggestions for retailers and suppliers regarding when the use of a stochastic review interval is effective in terms of cost efficiencies.

While the existence and effect of lead time variability is well‐established in the literature, traditional approaches the periodic review inventory model ignore the stochastic nature of review interval. This paper highlights the use of stochastic review intervals as a contributing factor to the bullwhip effect.

This paper aims to study the implication of the stochastic gross-profit-per-day objective on the ship profitability and the ship capacity and speed.

The paper has used the mathematical model and the solution methodology given by El Noshokaty, 2013, 2014, 2017a, 2017b, and SOS, 2019.

The paper finds that if the ship owner follows the rate concept and the cargo demand forecast, he can improve the profitability of his company and be able to select the proper capacities and speeds for the ships used.

The findings are not only useful for the shipping or other cargo transport companies but also for businesses like gas reservoir development, car assembly lines in the industry, cooperative farming and crop harvesting in agriculture, port cargo handling in trade and road paving in construction.

The contribution of this paper lies in notifying the ship owners of the possible profitability improvement and the consequences of building ships of larger capacities and slower speeds.