Asia-Pacific Journal of Operational Research

This study examines the return and volatility transmission/spillover between (Precious and Industrial) metals and stocks in the emerging Asian markets in the entire studying period and the two crisis sub-periods: the global financial crisis (GFC) and the Chinese Stock market crash sub-periods, and the normal sub-period that does not have any crisis. In addition, we estimate the optimal weights and hedge ratios for both metals and stocks. Employing the VAR-AGARCH model to estimate spillover, the results reveal the unidirectional return spillover from both precious and industrial metals to most of the Asian equity markets in the entire period as well as in the GFC and normal sub-periods but not the sub-period of the Chinese stock market crash. Besides, we reveal that there are unidirectional or bidirectional volatility transmissions between most of the precious metals and the Asian stock markets during the entire period and all the sub-periods. In contrast, the volatility spillover is not significant between most of the industrial metals and Asian stock markets during the entire period and all the sub-periods. On the other hand, our analysis on both optimal weight and hedge ratios suggests that adding nearly any metal to a portfolio of emerging Asian stocks improves its risk-adjusted return and helps to effectively hedge against stock risk exposure over both crisis and non-crisis sub-periods. Overall, these findings provide useful insights for portfolio diversification, asset pricing, and risk management.

Conducting product recovery and remanufacturing not only help manufacturers decrease the unit cost of production, but also benefit the environment. However, most manufacturers are hampered by the huge initial investment of related operations. In order to alleviate the manufacturers’ financial pressure of product recovery and remanufacturing, some governments implement the production subsidy (subsidy P) and recycling subsidy (subsidy I). Meanwhile, retailers can provide the revenue-sharing contract (contract S) and cost-sharing contract (contract C). Hence, this paper mainly studies the incentive designs of the government and retailer, and the effects of these incentives on the closed-loop supply chain. We first establish a Stackelberg game model consisting of a government, a manufacturer and a retailer, then investigate and compare the optimal decisions and payoffs of each member under each incentive combination of the government and retailer. Our results first show that, on the other hand, the government’s subsidy type cannot affect the retailer’s design of contract C, but subsidy I can induce the retailer to share a higher rate of sale revenue, comparing to subsidy P. On the other hand, the retailer’s contract S could induce the government to increase subsidy rate in most cases, comparing to contract C. Second, the subsidy P can always lead to a higher collection rate, lower wholesale and retail prices, and higher payoffs for the government, manufacturer and retailer, comparing to subsidy I. Besides, under subsidy I, contract S always leads to a higher collection rate, lower wholesale and retail prices, and higher payoffs for the government, manufacturer and retailer, comparing to contract C. However, under subsidy P, contract S can lead to a higher collection rate, a lower wholesale price, and higher payoffs for the manufacturer and retailer, comparing to contract C only when the manufacturer’s recovery efficiency is high. Moreover, the retail price is always higher and the government payoffs is always lower under contract C. Third, the government prefers to implement the subsidy P and then which contract is chosen by the retailer depends on the collection efficiency of the manufacturer. Therefore, subsidy P combining with contract S or C is the equilibrium incentive combination.

This paper investigates the potential portfolio diversification between Bitcoin, bonds, equities, and the US dollar. We make use of two approaches for constructing the portfolio. The first is the standard minimum variance approach, and the alternative is based on combining risk and return when the portfolio is constructed. The portfolio based on the minimum variance approach does not result in increasing the return per unit of risk compared to the corresponding value for the best single asset, in this case, Bitcoin. However, the portfolio based on the approach that combines risk and return in the optimization problem does show a return per unit risk higher than the corresponding value for any of the four assets. Thus, the portfolio diversification benefit with respect to these four assets, in terms of return per unit risk, exists only if the portfolio is constructed via the new approach.

This paper applies stochastic dominance techniques for income distribution analysis and develops tests of richness and poorness to achieve more accurate characterizations of relative welfare in populations than was previously possible. Results from our empirical application, using Hong Kong data, are consistent with predictions of the life-cycle theory of income and savings. Among other things, we find high concentrations of poor individuals among the younger cohorts, and at the same time, there are high concentrations of rich individuals amongst the oldest cohorts. Our results help to explain Hong Kong’s persistently high levels of income inequality in the population.

With the impact of foreign exchange markets, risks in financial markets are becoming more complex and diversified, which underlines the importance of risk management in financial supervision. In this paper, China’s non-ferrous metal futures market is selected as the research object, and Shanghai Futures Exchange’s Industrial Metal Commodity Index (IMCI) data are used to measure risk using the conditional autoregressive value at risk (CAViaR) model. The US dollar index (USDX) is incorporated into the CAViaR model to study its impact on the risk. Through empirical analysis, we arrive at the following conclusions: First, the asymmetric slope CAViaR model (AS-CAViaR) is more suitable for measuring the risk in China’s non-ferrous metal futures market. Second, the risk is positively impacted by the lagged risk. Moreover, the impacts of positive and negative returns on the risk are asymmetric, with a negative return having a greater impact. Third, the positive and negative shock of USDX has significant and different impacts on the risk. These impacts can be caused by global capital flows. In addition, the impact of the vector of explanatory variables on the IMCI at different quantile levels is discussed based on the CAViaR-USDX model, which reflects the comprehensive advantages of the quantile regression method and the model’s applicability. The above conclusions verify the impact of USDX on China’s non-ferrous metal futures market and provide a theoretical basis and direction for risk monitoring.

This paper studies the problem of target setting for a generated entity from a merger among two or more decision making units. Identification of the inherited input/output levels from merging decision-making units is an important issue. In this study, a novel inverse data envelopment analysis model is introduced for target setting of a merger in the presence of fuzzy data. This model enables the merged unit to recognize the required input/output levels from merging units to achieve a predefined efficiency target. Moreover, a fuzzy linear programming model is presented for estimating the minimum attainable efficiency score through a given merging. Then, the performance of the proposed method is examined through a banking application.

Applications of machine learning (ML) and data science have extended significantly into contemporary accounting and finance. Yet, the prediction and analysis of taxpayers’ status are relatively untapped to date. Moreover, this paper focuses on the combination of feature transformation as a novel domain of research for corporate firms’ tax status prediction with the applicability of ML approaches. The paper also applies a tax payment dataset of Finish limited liability firms with failed and non-failed tax information. Seven different ML approaches train across four datasets, transformed to non-transformed, that effectively discriminate the non-default tax firms from their default counterparts. The findings advocate tax administration to choose the single best ML approach and feature transformation method for the execution purpose.

In view of explosive trends and excessive trades in the cryptocurrency markets, this paper contributes to the existing literature by bringing in the limelight the effect of liquidity on the herding behavior in the cryptocurrency market. Results from a first applied herding model including contemporaneous and lagged squared market returns demonstrated that market-wide herding exists within falling markets. The incorporation of liquidity highlights further evidences on herding behavior across cryptocurrencies during high and low liquid days, which varies across percentiles. Our findings bring handy implications for topics of portfolio and risk management, as well as regulation.

This paper uses the real-option approach to find the optimal amount and time of investment in a real project. This approach is better than the net present value approach because it captures the uncertainty in the future expected cash flow. The real option has been applied to many industries; for example, oil and gas, telecommunications, and large-scale energy projects. The model introduced in this paper adds two features to the standard real options. The first feature is the two-stage investment. In reality, it is common for the project manager to not invest all budget at once. The second feature is the early termination event. The finding is that such a sudden death event causes the waiting period to be longer for both one-stage and two-stage investments. With low volatility, the lower optimal investment proportion of the total project budget is expected, when the mean arrival rate of the jump downside risk is higher. With medium or higher volatility, the higher optimal proportion is associated with a higher probability of jump risk. When the rate of one-time event is constant, volatility increases the optimal investment amount and increases the waiting time. The project has more value when the investment is divided into two stages rather than one stage. All other things being equal, the waiting time of one-stage is shorter than the waiting time of the first stage but longer than that of the second stage.

This study investigates gold as a hedge or a safe haven against inflation in four countries. We propose two standard and quantile techniques in the volatility models, with a time-varying conditional variance of regression residuals based on TGARCH specifications. Gold exhibits considerable evidence of a strong hedge in the US and China. Nevertheless, gold provides shelter at different times and not consistently across countries. With regards to be a safe haven, gold retains its status as a key investment in China. On the other hand, gold only plays a minor role in the UK and India. These findings indicate that gold can secure Chinese investment during the high inflationary periods, while gold is a profitable asset to hold over a long period of time in the US. In contrast, UK and Indian investors should hold a well-diversified portfolio for sustainable return and protection from purchasing power loss.