Asia-Pacific Journal of Operational Research

Risks are traditionally defined as the combination of probability and severity, but are actually characterized by additional factors. We believe the characteristics of risks include uncertainties, dynamics, dependence, clusterings and complexities, which motivate the utilization of various operational research tools. The objective of this issue is to survey the practice of using operational research tools in risk management, especially Asian risk management.

In this article, the dependence structure of the asset classes stocks, government bonds, and corporate bonds in different market environments and its implications on asset management are investigated for the US, European, and Asian market. Asset returns are modelled by a Markov-switching model which allows for two market regimes with completely different risk-return structures. Using major stock indices from all three regions, calm and turbulent market periods are identified for the time period between 1987 and 2009 and the correlation structures in the respective periods are compared. It turns out that the correlations between as well as within the asset classes under investigation are far from being stable and vary significantly between calm and turbulent market periods as well as in time. It also turns out that the US and European markets are much more integrated than the Asian and US/European ones. Moreover, the Asian market features more and longer turbulence phases. Finally, the impact of these findings is examined in a portfolio optimization context. To accomplish this, a case study using the mean-variance and the mean-conditional-value-at-risk framework as well as two levels of risk aversion is conducted. The results show that an explicit consideration of different market conditions in the modelling framework yields better portfolio performance as well as lower portfolio risk compared to standard approaches. These findings hold true for all investigated optimization frameworks and risk-aversion levels.

Information technology (IT) involve a wide set of risks. Enterprise information systems are a major developing form of information technology involving their own set of risks, thus creating potential blind spots. This paper describes risk management issues involved in enterprise resource planning systems (ERP) which have high impact on organizations due to their high cost, and their pervasive impact on organizational operations. Alternative means of acquiring ERP systems, to include outsourcing to application service providers (ASPs) are available. But outsourcing ERP involves many risks that are often overlooked. After identification of typical risks involved with representative alternative forms of ERP, multiple criteria analysis is proposed as a useful tool for tradeoff analysis in this selection decision. SMART is compared with popular approaches such as DEA and PCA- based DEA. A demonstration of how multiple criteria analysis can be applied in the international ERP alternative selection decision is given by including outsourcing to China and South Korea.

In the original Markowitz model for portfolio optimization the risk is measured by the variance. Several polyhedral risk measures have been introduced leading to Linear Programming (LP) computable portfolio optimization models in the case of discrete random variables represented by their realizations under specified scenarios. The LP models typically contain the number of constraints (matrix rows) proportional to the number of scenarios while the number of variables (matrix columns) proportional to the total of the number of scenarios and the number of instruments. They can effectively be solved with general purpose LP solvers provided that the number of scenarios is limited. However, real-life financial decisions are usually based on more advanced simulation models employed for scenario generation where one may get several thousands scenarios. This may lead to the LP models with huge number of variables and constraints thus decreasing their computational efficiency and making them hardly solvable by general LP tools. We show that the computational efficiency can be then dramatically improved by alternative models taking advantages of the LP duality. In the introduced models the number of structural constraints (matrix rows) is proportional to the number of instruments thus not affecting seriously the simplex method efficiency by the number of scenarios and therefore guaranteeing easy solvability.

In this paper, we study risk measures and portfolio problems based on a Stochastic Volatility Factor Model (SVFM). We analyze the sensitivity of Value at Risk (VaR) and Expected Shortfall (ES) to the changes in the parameters of the model. We compare the positions of a linear portfolio under assets following a SVFM, a Black–Scholes Model and a model with constant dependence structure. We consider an application to a portfolio of three selected Asian funds.

This article investigates the valuation of executive stock options when the stock return volatility is governed by the general error distribution stochastic volatility model, involving both the features of the stock return volatility and the abnormal fluctuations of the stock price at the expiration date. We estimate the parameters in the general error distribution stochastic volatility model using the Markov Chain Monte Carlo method with Shanghai & Shenzhen 300 Index in China as a sample, and compare the executive stock option values calculated by Black-Scholes option pricing model and the option pricing model under general error distribution stochastic volatility model. The results show that the general error distribution stochastic volatility model has greater veracity in describing the volatility of stock market returns, and there is divergence between the two values estimated by Black-Scholes option pricing model and the option pricing model under general error distribution stochastic volatility model. The divergence varies with the discrepancy between the price of underlying stock at the granting date and the strike price of the option.

Effectively predicting corporate financial distress is an important and challenging issue for companies. The research aims at predicting financial distress using the integrated model of rough set theory (RST) and support vector machine (SVM), in order to find a better early warning method and enhance the prediction accuracy. After several comparative experiments with the dataset of Chinese listed companies, rough set theory is proved to be an effective approach for reducing redundant information. Our results indicate that the SVM performs better than the BPNN when they are used for corporate financial distress prediction.

In this paper, we present an empirical study on the efficiency of the major ports in Northeast Asia. To become the global hub in a newly emerging Northeast Asian logistics network, the efficiency of the ports and their success factors are important for sustainable development. Thus, the comparison of port efficiency may shed light on this rapidly changing and thus very risky logistics network in Asia. The paper analyzes empirical results on the efficiencies of major ports by applying the generally acceptable methodology of data envelope analysis (DEA) and its variant models.

In our empirical results, most of ports showed the stronger position in pure efficiency, but weak position in scale efficiency. It implies that the promotion policies to become a hub under the rapidly changing logistics network should be based on the operational innovation such as the effective governance by the anchor companies or strategic alliances with global partners. So the paper strongly requests that the government should focus on the governance, instead of the infrastructure or facility to avoid the operational risks under the rapidly changing logistics network. Therefore, all the seaports should have sustainable development strategies that adapt to technological change.

This paper concludes that the scale- or supply-oriented policies should be changed to emphasize more the governance of port operations. Also, the paper concludes that investment in infrastructure does not improve efficiency; rather self-created logistics demand and strategic alliances, such as anchor companies, do improve the efficiency as demonstrated by the Tobit model.

In this work, we propose a single period stochastic inventory decision-making model that captures the trade-off between inventory policies and disruption risks for unreliable (both uncapacitated and capacitated) dual-sourcing reverse supply chain networks. Risk-management has emerged high at the corporate agenda as globalised supply chain networks are more stretched than ever due to offshoring and thus are more exposed to disruptions, while reverse logistics has been proven to constitute a profit center. In this environment, global companies have to scrutinize especially the role of major Asian economies (with large manufacturing capacities and huge markets), while conducting their strategic procurement planning. The developed model can be applied to a number of different scenario types encompassing various instances of disruptions to the collection of the end-of-life products, of the transportation system, and of the remanufacturing yield and capacity. Analytical closed-form solutions are obtained and important managerial insights on the merit of contingency strategies in managing uncertainties for reverse logistics networks are discussed.