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Auditors and accountants have an accepted reputation of being conservative. However, Antle and Nalebuff (1991) conclude in their analytical model on auditor‐client negotiations that auditors are not conservative and that a conservative audit report is never issued. This paper extends the Antle and Nalebuff (1991) results. By replacing the Antle and Nalebuff (1991) assumption that an auditor has a symmetric loss function (financial statement overstatements have the same impact as financial statement understatements) with the assumption that an auditor has an asymmetric loss function (losses to an auditor for financial statement overstatement are greater than the losses of an equal understatement), I find that auditors can be conservative and that conservative audit reports are issued to the users.

The purpose of this paper is to examine the hypothesis that a central bank's asymmetric preferences are able to explain inflation rate in a developing country. In addition, it seeks to help comprehend movements of inflation rate in Jordan and to understand Central Bank of Jordan preferences regarding inflation rate and output.

A standard monetary model consists of a central bank's loss function and an economy structure is constructed, which acts as a constraint on the central bank's behavior. Then, a distribute‐lag version of the derived model is estimated using ordinary least squares method.

The empirical evidence from the Jordanian economy shows that inflation rate relies on the variances of inflation rate and the variances of output. This finding supports the hypothesis that a central bank's asymmetric loss function is able to justify inflation rate movements. Moreover, the Jordanian central banker prefers higher inflation rate and higher level of output.

The paper provides evidence from a developing country regarding the ability of the asymmetric central bank preferences to justify inflation rate movement. In addition, the paper links central banks' losses with the uncertainty level and inflation rate in the economy.

The step-stress accelerated test is the most appropriate statistical method to obtain information about the reliability of new products faster than would be possible if the product was left to fail in normal use. This paper presents the multiple step-stress accelerated life test using type-II censored data and assuming a cumulative exposure model. The authors propose a Bayesian inference with the lifetimes of test item under gamma distribution. The choice of the loss function is an essential part in the Bayesian estimation problems. Therefore, the Bayesian estimators for the parameters are obtained based on different loss functions and a comparison with the usual maximum likelihood (MLE) approach is carried out. Finally, an example is presented to illustrate the proposed procedure in this paper.

A Bayesian inference is performed and the parameter estimators are obtained under symmetric and asymmetric loss functions. A sensitivity analysis of these Bayes and MLE estimators are presented by Monte Carlo simulation to verify if the Bayesian analysis is performed better.

The authors demonstrated that Bayesian estimators give better results than MLE with respect to MSE and bias. The authors also consider three types of loss functions and they show that the most dominant estimator that had the smallest MSE and bias is the Bayesian under general entropy loss function followed closely by the Linex loss function. In this case, the use of a symmetric loss function as the SELF is inappropriate for the SSALT mainly with small data.

Most of papers proposed in the literature present the estimation of SSALT through the MLE. In this paper, the authors developed a Bayesian analysis for the SSALT and discuss the procedures to obtain the Bayes estimators under symmetric and asymmetric loss functions. The choice of the loss function is an essential part in the Bayesian estimation problems.

The purpose of this paper is to develop a double-objective economic statistical design (ESD) of (X ‾) control chart under Weibull failure properties with the Linex asymmetric loss function. The authors have expressed the probability of type II error (β) as the statistical objective and the expected cost as the economic objective.

The design used in this study is based on a double-objective economic statistical design of (X ‾) control chart with Weibull shock model via applying Banerjee and Rahim's model for non-uniform and uniform schemes with Linex asymmetric loss function. The results in the least average cost and β in uniform and non-uniform schemes by Linex loss function, compared with the same schemes without loss function.

Numerical results indicate that it is not possible to reduce the second type of error and costs at the same time, which means that by reducing the second type of error, the cost increases, and by reducing the cost, the second type of error increases, both of which are very important. Obtained based on the needs of the industry and which one has more priority has the right to choose. These designs define a Pareto optimal front of solutions that increase the flexibility and adaptability of the X ‾ control chart in practice. When the authors use non-uniform schemes instead of uniform schemes, the average cost per unit time decreases by an average and when the authors apply loss function, the average cost per unit time increases by an average. Also, this quantity for double-objective schemes with loss function compared to without loss function schemes in cases uniform and non-uniform increases. The reason for this result is that the model underestimated the costs before using the loss function.

This research adds to the body of knowledge related to flexibility in process quality control. This article may be of interest to quality systems experts in factories where the choice between cost reduction and statistical factor reduction can affect the production process.

The cost functions for double-objective uniform and non-uniform sampling schemes with the Weibull shock model based on the Linex loss function are presented for the first time.

The process capability indices have been widely used to measure process capability and performance. In this paper, we proposed a new process capability index which is based on an actual dollar loss by defects. The new index is similar to the Taguchi’s loss function and fully incorporates the distribution of quality attribute in a process. The strength of the index is to apply itself to non‐normal or asymmetric distributions. Numerical examples were presented to show superiority of the new index against Cp, Cpk, and Cpm which are the most widely used process capability indices.

The principle of prudence plays a critical role in the design of national and international public sector accounting. Whereas in private sector accounting there is a substantial body of literature with regard to conservatism, the academic debate on the prudence principle in public sector accounting has only started recently. The aim of this chapter is to analyse whether the International Public Sector Accounting Standards (IPSASs) address asymmetric prudence with respect to measurement. This chapter shows that the existence of requirements leading to asymmetric prudence with regard to the measurement of assets is widespread throughout the suite of IPSASs.

The purpose of this research is to provide a new loss function‐based risk assessment method so the likelihood and consequence resulting from the failure of a manufacturing or environmental system can be evaluated simultaneously.

Instead of using risk matrices of the occurrence and consequence separately for evaluating manufacturing and environmental risks, an integrated approach by exploring the relationship between process capability indices: C_p, C_pk and C_pm, and three different loss functions: Taguchi's loss function; Inverted normal loss function (INLF); and Revised inverted normal loss function (RINLF) is proposed.

The new method of quantitative risk assessment linking the likelihood and expected loss of failure is illustrated by two numeric examples. The results suggest that the revised inverted normal loss function (RINLF) be used in assessing manufacturing and environmental risks.

It gives decision‐makers a concrete tool to assess the likelihood and consequence of their processes. Linking the process capability indices and loss functions is particularly promising, as this provides a useful risk assessment tool for practitioners who want to reduce hazardous waste and manufacturing losses from their facilities.

The manufacturing and environmental risks are determined by paring the process capability indices and loss function. From the loss function‐based estimation, one can quantify the consequence of a manufacturing loss and get the severity rating in an objective way.

The service industry is a major component of the economy. Raw material, components, assemblies, and finished products are shipped between suppliers, manufacturers, distributors, and retailers. Accordingly, timely receipt of shipped goods is crucial in maintaining the efficiency and effectiveness of such service processes. A service provider offers an incentive to the customer by specifying a competitive target time for delivery of goods. Further, if the delivery time is deviant from the target value, the provider offers to reimburse the customer for an amount that is proportional to the value of the goods and the degree of deviation from the target value. The service provider may set the price to be charged as a function of product value. This price is in addition to the operational costs of logistics that are not considered in the formulated model. For protection against deviation from target due dates, the service provider agrees to reimburse the customer. The reimbursement could be based on an asymmetric loss function influenced by the degree of deviation from the target due date as well as product value. The penalties could be different for early and late deliveries since the customer may experience different impact and consequences accordingly. The chapter develops a model to determine the amount (price) that the provider should add to the cost estimate of the delivery contract for protection against delivery deviations. Such a cost estimate will include the operational costs (fixed and variable) of the shipment, to which an amount is added to cover the expected payout to customers when the delivery time deviates from the target value. The optimal price should be such that the expected revenue will at least exceed the expected payout.

The purpose of this paper is to apply alternative GARCH‐type models to daily volatility forecasting, and apply Value‐at‐Risk (VaR) to the Taiwanese stock index futures markets that suffered most from the global financial tsunami that occurred during 2008.

Rather than using squared returns as a proxy for true volatility, this study adopts three range‐based proxies (PK, GK and RS), and one return‐based proxy (realized volatility), for use in the empirical exercise. The forecast evaluation is conducted using various proxy measures based on both symmetric and asymmetric loss functions, while back‐testing and two utility‐based loss functions are employed for further VaR assessment with respect to risk management practice.

Empirical results demonstrate that the EGARCH model provides the most accurate daily volatility forecasts, while the performances of the standard GARCH model and the GARCH models with highly persistent and long‐memory characteristics are relatively poor. In the area of risk management, the RV‐VaR model tends to underestimate VaR and has been rejected owing to a lack of correct unconditional coverage. In contrast, the GARCH genre of models can provide satisfactory and reliable daily VaR forecasts.

The unobservable volatility can be proxied using parsimonious daily price range with freely available prices when applied to Taiwanese futures markets. Meanwhile, the GARCH‐type models remain valid downside risk measures for both regulators and firms in the face of a turbulent market.

The purpose of this paper is to bring out the development of a flexible model for nonstationary crop yield distributions and its applications to decision-making in crop insurance.

The authors design a nonparametric Bayesian approach based on Gaussian process regressions to model crop yields over time. Further flexibility is obtained via Bayesian model averaging that results in mixed Gaussian processes.

Simulation results on crop insurance premium rates show that the proposed method compares favorably with conventional estimators, especially when the underlying distributions are nonstationary.

Unlike conventional two-stage estimation, the proposed method models nonstationary crop yields in a single stage. The authors further adopt a decision theoretic framework in its empirical application and demonstrate that insurance companies can use the proposed method to effectively identify profitable policies under symmetric or asymmetric loss functions.