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Fat‐tailed distributions have been found in many financial and economic variables ranging from forecasting returns on financial assets to modeling recovery distributions in bankruptcies. They have also been found in numerous insurance applications such as catastrophic insurance claims and in value‐at‐risk measures employed by risk managers. Financial applications include:

This paper aims to focus on the problem of the optimal relocation policy for a firm that faces two types of uncertainty: one about the moments in which new (and more efficient) sites will become available; and the other regarding the degree of efficiency improvement inherent to each one of these new, yet to be known, potential location places.

The paper considers the relocation issue as an optimal stopping decision problem. It uses Poisson jump processes to model the increase in the efficiency process, where these jumps occur according to a homogeneous Poisson process, but the magnitude of these jumps can have special distributions. In particular it assumes that the magnitudes can be gamma‐distributed or truncated‐exponential distributed.

Particular characteristics concerning the expected optimal timing for relocation, the corresponding volatility and the value of the firm under the optimal relocation policy are derived. These results lead also to the conjecture that the optimal relocation policy is robust in terms of distributions of the degree of improvement of efficiency that are considered, as long as the expected values are the same.

The paper provides an innovative approach to relocation problems, using stochastic tools. Moreover, the use of the truncated exponential and the gamma distribution functions to model the Poisson jumps is particularly suitable, given the situation under study. To the authors' knowledge, this is the first time that this type of setting is used to tackle a real options problem.

This paper aims to review the existing bond-based peridynamic (PD) and state-based PD heat conduction models, and further propose a refined bond-based PD thermal conduction model by using the PD differential operator.

The general refined bond-based PD is established by replacing the local spatial derivatives in the classical heat conduction equations with their corresponding nonlocal integral forms obtained by the PD differential operator. This modeling approach is representative of the state-based PD models, whereas the resulting governing equations appear as the bond-based PD models.

The refined model can be reduced to the existing bond-based PD heat conduction models by specifying particular influence functions. Also, the refined model does not require any calibration procedure unlike the bond-based PD. A systematic explicit dynamic solver is introduced to validate 1 D, 2 D and 3 D heat conduction in domains with and without a crack subjected to a combination of Dirichlet, Neumann and convection boundary conditions. All of the PD predictions are in excellent agreement with the classical solutions and demonstrate the nonlocal feature and advantage of PD in dealing with heat conduction in discontinuous domains.

The existing PD heat conduction models are reviewed. A refined bond-based PD thermal conduction model by using PD differential operator is proposed and 3 D thermal conduction in intact or cracked structures is simulated.

The EOQ formula is definitely the oldest and best known single stage lot sizing technique. Its use reportedly dates back to 1904, even though it was not published until a later date. It is often looked upon with scepticism by practitioners and academicians alike, although the reasons for this may differ; it seems, however, to be the most widely used lot sizing technique overall.

The purpose of this paper is to propose a model for ruin‐contingent life annuity (RCLA) contracts under a jump diffusion model, where the dynamics of volatility is governed by the Heston stochastic volatility framework. The paper aims to illustrate that the proposed jump diffusion process, for both asset price and stochastic volatility, will provide a more realistic pricing model for RCLA contracts in comparison to existing models.

Under the assumption of the deterministic withdrawals, the authors use a partial integro differential equation (PIDE) approach to develop the pricing scheme for the fair value of the lump sum charges of RCLA contracts. Consequently, the authors employ an elegant numerical scheme, finite difference method, for solving the PIDEs for the reference portfolio, as well as the volatility. The findings show that a different pricing behaviour of the RCLA contracts under the authors' model parameters is obtained compared to that in the existing literature.

RCLA pricing in the complete market often underestimates the jump risk and the persistent factor in the volatility process. The authors' generalized model shows how these two random sources of risks can be precisely characterized.

The parameter values used in the numerical analysis require more empirical evidence. Hence, in order for more precise pricing practice, the calibration from real data is needed.

The model, as adopted in this study, for pricing of RCLA contracts should provide a general guideline for the commercialization of this product by insurance companies.

The demand for RCLA contracts as an alternative to the commonly‐practised annuitization option has recently increased, rapidly, among the soon‐to‐retire baby boomers. This paper investigates the fair value of this particular product, which could be beneficial to researchers for a better understanding of the product design.

This is the first research paper which prices the RCLA contracts in the incomplete market. The gap between RCLA contract pricing and studies of jump diffusion models for derivative pricing, in the literature, is therefore filled.

Effective management of resources in a dynamic, multiproject environment requires consideration of two key issues: the availability of each of the multiple resources and the method of scheduling these resources to complete activities and, subsequently, projects. Identifies the trade‐offs between performance and the availability of multiple resources, when some resources are more costly than others. Finds that there are significant effects when the level of either the costly resources or the cheaper resources are varied, that trade‐offs can be made by reducing the availability of the costly resources and increasing the availability of the cheaper resources and that the improvement in completion time performance is reasonably linear over the tested ranges and the rates of improvement differ over the ranges. Describes the resource allocation factors and treatments as well as the scheduling heuristics. Uses a finite scheduling algorithm along with the prioritization heuristics to schedule the constrained multiple resources simultaneously and a simulation to replicate the environment. Develops linear regressions to provide further insight.

With the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from the option prices and the historic returns.

The authors propose a nonparametric kernel smoothing approach that removes the adverse effects of pricing errors and leads to consistent estimation for both the implied variance and the VRP. The asymptotic distributions of the proposed VRP estimator are developed under three asymptotic regimes regarding the relative sample sizes between the option data and historic return data.

This study reveals that existing methods for estimating the implied variance are adversely affected by pricing errors in the option prices, which causes the estimators for VRP statistically inconsistent. By analyzing the S&P 500 option and return data, it demonstrates that, compared with other implied variance and VRP estimators, the proposed implied variance and VRP estimators are more significant variables in explaining variations in the excess S&P 500 returns, and the proposed VRP estimates have the smallest out-of-sample forecasting root mean squared error.

This study contributes to the estimation of the implied variance and the VRP and helps in the predictions of future realized variance and equity premium.

This study is the first to propose consistent estimations for the implied variance and the VRP with the presence of option pricing errors.

The purpose of this paper is to investigate the impact of bias error resulted from using Monte Carlo simulation in evaluating the American-style option value.

The authors develop an analytical approximation formula to quantify the bias error under the assumption of conditionally independent and identically distributed samples of asset prices. The bias arises from the nested optimization and expectation calculation. The formula is then used to numerically quantify the bias and as an objective function for bias minimization for a given budget of samples.

Monte Carlo methods used in valuation of American-style options can results in bias error ranging from 2 to 10 per cent of the option value. The bias error can be reduced up to 50 per cent either by performing a better scheme for sampling or by efficiently allocating sample size.

The running time of the proposed procedure can be improved by using a specialized algorithm to solve the sample size allocation problem instead of using a commercially available subroutine MINOS. Other sampling procedures for bias reduction may be extended and applied to this multi-stage problem.

The methodology can help to more accurately approximate the option value.

The paper provides a method to develop an analytical approximation for bias error and provide a numerical experiment to test the methodology.

To address formally the issue of uncertainty in valuing real estate.

Monte Carlo simulations are used to incorporate the uncertainty of valuation parameters. The probability distributions of the various parameters are constructed using empirical data and a simple model is suggested to compute the discount rate.

The central values of the simulations are in most cases slightly less than the hedonic values. The confidence intervals are found to be most sensitive to the long‐term equilibrium interest rate being used and to the expected growth rate of the terminal value.

Further research should focus on the stability of the model when other portfolios are used and for different periods of the real estate cycle. It would also be fruitful to dig deeper in the relation between capital expenses and property values.

Risk can be assessed by valuers as they can measure the probability that the value of a property be less than a given threshold.

By incorporating uncertainty, the analysis does not yield merely a point estimate of the property's value but rather the entire distribution of values. Also this paper constitutes a contribution to the debate about valuation variation and the margin of error in valuing properties.

Three simple, single pass multi‐stage lot‐sizing heuristics are examined using simulation. The heuristics are based on using different cost policies in single stage lot‐sizing procedures when applied to a multi‐stage setting. The focus is on the echelon holding cost policy and its performance relative to using “full value” holding costs and McLaren's adjusted setup costs. It is shown that echelon holding costs can lead to an extremely poor overall cost performance. A simple measure that will detect situations for which the echelon holding cost policy is potentially not suitable is suggested and evaluated. Application of the proposed measure results in substantial cost improvements for the echelon holding cost policy; despite this, the policy was outperformed by the MLSA policy in most cases. More research is needed, however, before any conclusive evidence can be presented on the effectiveness of echelon holding costs in multi‐stage lot‐sizing.