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In this paper we study the performance of a two-stage approach to scheduling under uncertainty making use of sequences of groups of permutable operations. Given a sample set of uncertainty realization scenarios, the goal is to compute a sequence of groups of permutable operations representing a partial scheduling decision in the first-stage, that yields the best possible score in the second-stage, when, for a specific scenario, a full operation sequence is obtained via a second-stage decision policy. This approach is described for a single-machine problem and the jobshop problem with stochastic and robust optimization, as well as several commonly studied objectives. We propose new constraint programming models as well as a genetic algorithm meta-heuristic to compute such two-stage solutions. We also investigate a warm-start scheme to work around the difficult search space of sequences of permutable operations. Experiments are carried out to characterize when this two-stage approach yields better results. We also compare the introduced methods with existing ones. Theoretical extensions of the known methods are also described and evaluated. [ABSTRACT FROM AUTHOR]

This study contributes to the unconsolidated cryptocurrency literature, with a systematic literature review focused on cryptocurrency market microstructure. We searched Web of Science database and focused only on journals listed on 2021 ABS list. Our final sample comprises 138 research papers. We employed a quantitative and an integrative analysis, and revealed complex network associations, and a detailed research trending analysis. Our study provides a robust and systematic contribution to cryptocurrency literature by making use of a powerful and accurate methodology—the bibliographic coupling, also by only considering ABS academic journals, using a wider keyword scope, and not enforcing any restrictions regarding areas of knowledge, thus enhancing the contribution of extant literature by allowing the insights of more high-quality peripheral studies on the subject. The conclusions of this study are of extreme importance for researchers, investors, regulators, and the academic community in general. Our study provides high structured networking and clear information for research outlets and literature strands, for future studies on cryptocurrency investment, it also presents valuable insights to better understand the cryptocurrency market microstructure and deliver helpful information for regulators to effectively regulate cryptocurrencies. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the cohesive players and a related axiom called equal surplus of cohesive players. On this basis, we study the subclass of efficient, symmetric, and linear (ESL) values satisfying equal surplus of cohesive players. We first give an analytical formula and also propose two characterizations for this subclass of ESL values. With these characterizations, we discuss the relationships between this subclass and other classical ESL values, in particular the Shapley value. We then characterize each value in the subclass of ESL values satisfying equal surplus of cohesive players by introducing the β -null player surplus property and the β -reward cohesive player property. From this, we obtain new parallel characterizations of the Shapley value and the equal surplus division value. Moreover, we show that equal surplus of cohesive players can replace symmetry in many well-known characterizations of values. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of long-term investment by exploring optimal strategies for allocating wealth among a finite number of assets over multiple periods. Based on the classical Markowitz mean-variance philosophy, we develop a new portfolio optimization framework which can produce sparse portfolios. The sparsity of the portfolio at each and across periods is characterized by the possibly nonconvex penalties. For the constructed nonconvex and nonsmooth constrained model, we propose a generalized alternating direction method of multipliers and its global convergence to a stationary point can be guaranteed theoretically. Moreover, some numerical experiments are conducted on several datasets generated from practical applications to illustrate the effectiveness and advantage of the proposed model and solving method. [ABSTRACT FROM AUTHOR]

Transshipment yards are not usually considered in optimization approaches proposed for solving the log transportation problem. However, they may provide substantive advantages with respect to the usual logistics practice of direct delivering from supply forests to mills. Therefore, this paper evaluates, at the operational level, different supply chain topologies with the purpose of assessing potential costs savings associated to the use of intermediate storage facilities. The researched problem is defined by stated sets of log-suppliers, storage and transshipment yards, and facilities demanding several types of logs over a known planning horizon. The optimal transportation and inventory plan must be found. An optimization algorithm based on Column Generation + GRASP pricing metaheuristic was developed to compute minimal operation costs associated to different supply-chain topologies over a bank of instances generated from realistic information of the forestry activity in the Argentinean province of Misiones. [ABSTRACT FROM AUTHOR]

This paper is devoted to the theory of perishable inventory systems. In such systems items arrive and stay 'on the shelf' until they are either taken by a demand or become outdated. Our aim is to survey, extend and enrich the probabilistic analysis of a large class of such systems. A unifying principle is to consider the so-called virtual outdating process V , where V(t) is the time that would pass from t until the next outdating if no new demands arrived after t. The steady-state density of V is determined for a wide range of perishable inventory systems. Key performance measures like the rate of outdatings, the rate of unsatisfied demands and the distribution of the number of items on the shelf are subsequently expressed in that density. Some of the main ingredients of our analysis are level crossing theory and the observation that the V process can be interpreted as the workload process of a specific single server queueing system. [ABSTRACT FROM AUTHOR]

As the only largest source of revenue and cost in a hospital, the operation room (OR) scheduling problem is a hot research topic. Nonetheless, an integrated model is the missing key to managing and improving the efficiency of ORs. This paper presents a fully integrated model regarding three concepts: meditating elective patients and emergency patients together, considering ORs and downstream units, and proposing hierarchical weekly, daily, and rescheduling models. Due to the inherent randomness in emergency patient arrival, a random forest machine learning model and geographical information systems are used to obtain the emergency patient surgery duration and arrival time, respectively. According to the machine learning model in weekly and daily scheduling, initially, fixed capacity is reserved for emergency patients. When an emergency patient arrives, the surgery starts if a reserved OR is available. Otherwise, the first available OR will be dedicated to the patient due to an emergency patient's higher priority than an elective patient. In this case, it is needed to reschedule the OT schedule for the remaining patient. Moreover, the three-phase model guarantees that an emergency patient assigns to an OR within a specific time limit. To solve the models, genetic algorithm and particle swarm optimization are developed and compared. In addition, a real-world case study is undertaken at a hospital. The results of comparing the proposed approach to the hospital's current scheduling show that the three-phase model had a considerable positive effect on the ORs schedule. [ABSTRACT FROM AUTHOR]

The multi-objective integer programming problem often occurs in multi-criteria decision-making situations, where the decision variables are integers. In the present paper, we have discussed an algorithm for finding all efficient solutions of a multi-objective integer quadratic programming problem. The proposed algorithm is based on the aspect that efficient solutions of a multi-objective integer quadratic programming problem can be obtained by enumerating ranked solutions of an integer quadratic programming problem. For determining ranked solutions of an integer quadratic programming problem, we have constructed a related integer linear programming problem and from ranked solutions of this integer linear programming problem, ranked solutions of the original integer quadratic programming problem are generated. Theoretically, we have shown that the developed method generates the set of all efficient solutions in a finite number of steps, and numerically we have elaborated the working of our algorithm and compared our results with existing algorithms. Further, we have analyzed that the developed method is efficient for solving a multi-objective integer quadratic programming problem with a large number of constraints, variables and objectives. [ABSTRACT FROM AUTHOR]

In this paper, we study large losses arising from defaults of a credit portfolio. We assume that the portfolio dependence structure is modelled by the Archimedean copula family as opposed to the widely used Gaussian copula. The resulting model is new, and it has the capability of capturing extremal dependence among obligors. We first derive sharp asymptotics for the tail probability of portfolio losses and the expected shortfall. Then we demonstrate how to utilize these asymptotic results to produce two variance reduction algorithms that significantly enhance the classical Monte Carlo methods. Moreover, we show that the estimator based on the proposed two-step importance sampling method is logarithmically efficient while the estimator based on the conditional Monte Carlo method has bounded relative error as the number of obligors tends to infinity. Extensive simulation studies are conducted to highlight the efficiency of our proposed algorithms for estimating portfolio credit risk. In particular, the variance reduction achieved by the proposed conditional Monte Carlo method, relative to the crude Monte Carlo method, is in the order of millions. [ABSTRACT FROM AUTHOR]

In this paper, we consider a single server production inventory model with server breakdown and customer impatience while server is down. We assume that customer arrivals are according to a Markovian Arrival Process. If the server is busy at the time of arrival of a customer, the customer joins a queue of infinite capacity. The server is subjected to breakdowns, which occur according to a Poisson process. The service time of customers follows independent and identical phase type distribution and one unit of the inventory is required for each service. Repair starts immediately after a breakdown and the time taken for repair follows phase type distribution. The customer whose service was interrupted waits for the remaining service while the server is under repair. When repair is completed, the service of the interrupted customer is resumed from the interrupted stage. When the server is under repair, loss of customers from the infinite queue due to impatience occurs at exponentially distributed intervals. Such impatient customers go to a buffer of finite capacity, provided waiting space is available, else such customers leave the system for ever. The production of each inventory occurs in k stages of production and production time of each stage follows independent phase type distributions. The production of inventory is according to (s, S) policy. Here the steady state analysis has been done. Various performance measures have been evaluated and we derived distributions of certain important performance characteristics. We analysed the model numerically and graphically. [ABSTRACT FROM AUTHOR]

In this paper we find stability conditions of a two-class retrial system with unreliable server, in which the new customer joins a class-dependent orbit queue regardless of the state of the server. The interrupted customer joins the top of the corresponding orbit and tries to occupy the server after a class-dependent exponential retrial time. To find stability conditions, we apply two different approaches, regenerative approach and the stability analysis of a Markov chain (the MC approach) which has been developed in Fayolle et al. (Topics in the constructive theory of countable Markov chains, Cambridge University Press, Cambridge, 1995). The former approach allows to obtain a transparent and intuitive necessary stability condition. Then we apply the MC approach to obtain the stability criterion of the embedded two-dimensional Markov chain describing the state of orbits at the instances when the server becomes free. We use a necessary stability condition obtained by the regenerative method to present the stability criterion in a compact form. Moreover we discuss a modified controllable system and compare stability zones of these two systems. Some numerical examples based on simulation results are included as well which illustrate the theoretical issues. [ABSTRACT FROM AUTHOR]

In many service systems, the service rates of the servers or the machines degrade over time due to a variety of reasons like fatigue, deficiency (due to manufacturing defect or energy loss, deformation, or excessive heat), and lack of proper maintenance. To make a service system beneficial for both customers and management points of view, it is imperative that models are developed to understand the impact of degradation. In this paper, we study M A P / P H / 1 - type queueing models incorporating degradation, failures/breakdowns and repairs. The degradation and to restore to normalcy in the service rate we look at two scenarios. In the first one, the service is restored to normalcy immediately after the server becomes idle or a fixed number of services is offered. In the second one, the restoration takes a random time that is modeled using a phase type distribution. Also, there are a variety of reasons that lead to failures/breakdowns of the server. We model these using (possibly) different phase type distributions. Both the models are studied analytically using matrix-analytic methods and illustrative numerical examples bringing out the qualitative behavior of the impact of degrading services are discussed. Two cost optimization problems whose solutions are obtained using particle swarm optimization technique along with a few illustrative examples are presented. [ABSTRACT FROM AUTHOR]

An MIAPP-NALT system is an order picking system in which cases are picked at multiple in-the-aisle pick positions (MIAPP) and storage and retrieval operations are performed by a narrow aisle lift truck (NALT). In this paper, the operation of such a system involving three classes of stock keeping units with random demands for storage and retrieval operations is modeled as an M/G/1 queue, where "customers" are storage and retrieval requests, the "server" is the NALT, and retrieval requests have non-preemptive priority over storage requests. Our goal is to explore a methodology and solution method to obtain the optimal layout design of a class-based MIAPP-NALT system with stochastic demands and priority service. To this end, an operation time model of the system is developed and the first two moments for the operation time are derived. To overcome the challenge in finding the desired optimal layout, a near-optimal layout obtained via a heuristic approach is obtained at first and is improved afterwards. Based on the optimal layout, some valuable queueing results demonstrate the benefit of using a priority-based discipline. Moreover, some useful insights regarding the selection of dedicated versus random storage policies are obtained. [ABSTRACT FROM AUTHOR]

In this paper we study a simultaneous service multiserver system which we call speed-scaling supercomputer, where speed-scaling is used to address the performance/power demand tradeoff. We treat the system by three-level modeling approach, using matrix-analytic method, generalized semi-Markov processes and small-scale technical system as the three levels of modeling. An explicit form of stability condition is obtained for a two-server system with heterogeneous customer classes. Regenerative estimation approach is used for confidence estimation of performance measures both in simulation and technical models. We demonstrate the potential of the three-level modeling approach on a relatively sophisticated and interesting model by performing extensive experiments. [ABSTRACT FROM AUTHOR]

Queueing systems where certain inventory items are required to provide service to a customer have become popular in the literature from early 1990th. Such systems are similar to those models analysed in the literature models with paired customers, assembly-like queues, passenger–taxi models, etc. During the last few years they are considered in the context of modelling operation of the nodes of a wireless sensor network with energy harvesting. Distinguishing feature of the model considered in this paper, besides the suggestion that arrival flow of customers is described by the Markovian arrival process, is the assumption about a general distribution of the service time while only exponential or phase-type distribution was previously assumed in the existing literature. We apply the well-known technique of M/G/1 type Markov chains and semi-regenerative processes to obtain the ergodicity criterion in a transparent form, stationary distribution of the system under study and the Laplace–Stieltjes transform of the sojourn time distribution. This creates an opportunity to formulate and solve various optimization problems. A number of numerical examples illustrate the computational tractability of the theoretical results and illustrate the behavior of the system performance measures depending on its parameters. [ABSTRACT FROM AUTHOR]

This paper examines a two-period dynamic contracting in a supply chain under information asymmetry, where a supplier sells a product to a retailer via a trade credit contract. It is found that the retailer always prefers to conceal her actual cost information thus signal as a higher-cost type in the first period to pursue a higher information rent, which would decrease the supplier's profit and thereby the overall profit of the supply chain. To mitigate this ratchet effect, we introduce a reputation compensation mechanism in the two-period trade credit setting. This mechanism could alleviate the information asymmetry to a certain extent as there exists a threshold that incentivizes the retailer to share her true cost information in the earlier period. Moreover, the retailer might claim as a lower-cost type when the supplier offers a relatively higher reputation compensation to take full advantage of her information. Therefore, the supplier should provide trade credit with a reasonable reputation compensation in a two-period setting to enhance his expected profit. [ABSTRACT FROM AUTHOR]

This paper considers a capital-constrained online retailer (OR) selling products through an e-commerce platform (EP) who also offers financial services to retailers. During the selling season, the OR exerts an effort to promote market demand through activities like sales promotions, advertising and live-streaming selling events. To investigate the EP-based financing scheme, a game-theoretic model is developed where the EP functions as the leader determining the interest rate and platform usage fee rate, and the OR functions as the follower determining the order quantity and effort level. We explore the impacts of the OR's risk-aversion and find that when the OR is risk-averse (1) she sets a high effort level, and the EP sets a high usage fee rate; (2) a high risk-averse OR orders less products than low risk-averse OR. We design specific revenue-cost sharing contracts to coordinate the supply chain and demonstrate that the designed contracts are feasible. Moreover, we find that the OR consistently prefers EP financing compared to bank financing. [ABSTRACT FROM AUTHOR]

The pricing of tickets is a crucial decision when managing entertainment events like concerts, operas, ballets, and theater performances. In this study, we examine the pricing decisions made by two major decision-makers before an entertainment event: the venue and the online retailer. In a game-theoretic approach with uncertain information, the interactions between decision-makers are studied in a single and dual-channel structure. A confidence-level-based uncertainty theory is utilized in order to formulate the amount of uncertainty, and the optimal solutions are analytically computed to maximize the total profit for both players. There are three distinct cases studied, including two single-channel market cases (one in which only the venue sells the tickets, and another in which only the online platform sells the tickets) as well as a dual-channel market case (in which both players sell tickets). Stackelberg and Nash games are used to illustrate the interactions between players in each game. The results indicate the optimal pricing, service level improvement, and total profit for both the venue and the online retailer. Additionally, it is shown that the amount of risk (confidence level) the decision-makers are willing to accept strongly affects the optimal solution. In some cases, the roles of the market participants do not have a significant impact on the optimal solution. The findings of this paper can help practitioners in the entertainment industry make better ticket pricing decisions in an uncertain and competitive environment. [ABSTRACT FROM AUTHOR]

This paper studies the green new product development (GNPD) problem of a risk-averse capital constrained supply chain (SC). The SC is managed by an SME entrepreneur, seeking financial support from a multi-sided FinTech platform (MSP) to develop a portfolio of green and non-green products. The MSP offers the SC a combination of equity financing (EF) and debt financing (DF) facilities and must decide on the interest rate of its DF facility. Using a benchmark model, we first characterize the SC's production and the MSP's financing decisions under a deregulated scenario. Focusing on an alternative case with government intervention (i.e., hybrid environmental-green entrepreneurship policy), we next develop a three-level game theoretical model and sequentially characterize the decision-making behavior of government, MSP, and SC. The model outcomes are analyzed by considering the policy approach (i.e., economic influence vs. social welfare) and the platform's risk attitude. The results reveal that, when coupled with an appropriate government intervention policy, a regulated scenario leads to a better outcome, particularly when the MSP is risk-neutral and strikes a right balance between the EF and DF. The win–win situation may not be realized when the MSP is risk-averse and the host government is merely focused on its economic influence. To successfully promote sustainable supply chain finance (SSCF) through digital platforms, policy makers are urged to leverage their legislative power and prioritize green entrepreneurship and social welfare over their financial maximization agenda. [ABSTRACT FROM AUTHOR]

Supply chain finance (SCF) is a financial service that provides convenient loan transactions for small- and medium-sized enterprises (SMEs) upstream and downstream of the supply chain. SCF can help to smooth the capital flow of many SMEs. However, it is difficult for agricultural SMEs to participate in SCF because this kind of SME usually has various risks. Objectively, evaluating the credit rating of agricultural SMEs is difficult for commercial banks. Furthermore, unlike general manufacturing enterprises, agricultural enterprises produce directly based on nature. Sustainable development based on the natural environment is very important for agricultural enterprises. In this paper, considering sustainability, we construct a criteria system to evaluate the credit ratings of agricultural SMEs for SCF. Moreover, we study a method for processing the hybrid heterogeneous evaluation information of SMEs. A co-decision method is proposed to classify the credit ratings of agricultural SMEs with the help of three-way decisions. Agricultural SMEs are evaluated by both criteria evaluation information and misclassification loss. Finally, a credit rating evaluation example is presented to demonstrate the application of our method. The results show that our proposed method can be used to fully evaluate agricultural SMEs with fine classification effects. It can also provide a reference for commercial banks to determine the credit of agricultural SMEs with a low decision risk. [ABSTRACT FROM AUTHOR]

Today, supply chain finance is a very important topic. Traditional supply chains rely on banks to support the related financing activities and services. With the emergence of blockchain technology, more and more companies in different industries have considered using it to support supply chain finance. In this paper, we study supply chain financing problems in supply chains selling fashionable products. Modeling under the standard newsvendor problem setting with a single manufacturer and a single retailer employing a revenue sharing contract, we develop analytical models for both the traditional and blockchain-supported supply chains. We derive the optimal contracting and quantity decisions in each supply chain with Nash bargaining between the manufacturer and retailer. We analytically show how the revenue sharing contract can coordinate both types of supply chains. We then compare the optimal systems performances between the two supply chains. We prove that the blockchain-supported supply chain incurs a lower level of operational risk than the traditional supply chain. We have shown that if the service fees by banks are sufficiently high, adopting blockchain technology is a mean-risk dominating policy which brings a higher expected profit and a lower risk for the supply chain and its members. For robustness checking, we examine other commonly seen supply chain contracts and alternative risk measures, and analytically reveal that the results remain valid. [ABSTRACT FROM AUTHOR]

Green product development depends on the green innovation behavior of upstream and downstream companies in the supply chain. This paper focuses on a green supply chain consisting of a supplier and a manufacturer and considers two financing schemes wherein the manufacturer's operating capital and green innovation capital are both constrained: bank credit financing (BCF) and supplier green investment (SGI). We investigate the financing strategy of a manufacturer with different financing decision preferences and supply chain coordination contract designs. The results show that when the manufacturer's financing decision preference is low bankruptcy risk, it should choose BCF when the bank loan interest rate is lower than a threshold and SGI when the bank loan interest rate is higher than this threshold; when the manufacturer's financing decision preference is high R&D benefit, it should choose BCF when the initial capital is lower than another threshold and choose SGI when the initial capital is higher than this threshold. Then, we compare whether cost-sharing, quantity discount and revenue-sharing contracts can coordinate the supply chain, and discuss the selection strategies of supply chain members for coordination contracts. Research shows that revenue-sharing contracts fail to coordinate; however, when the cost-sharing ratio and quantity discount rate are appropriate, both cost-sharing and quantity discount contracts can achieve supply chain coordination. The lower the supplier's unit production cost, the stronger its motivation to accept a higher cost-sharing ratio and quantity discount rate. For the supplier, the key to choosing any coordination contract lies in the relative height of the cost-sharing ratio and quantity discount rate while the manufacturer should always choose a quantity discount contract when the quantity discount rate is high enough. [ABSTRACT FROM AUTHOR]

In practice, selling goods through a powerful retailer such as Wal-Mart enables the supplier to access the retailer's ERP for accurate demand information (e.g., Wal-Mart's Retail Link). However, in the recent years, we observe the suppliers are suffering from longer and longer average account period when they contract with powerful retailers. Therefore, whether partnering with a powerful retailer at the cost of a longer account period becomes the supplier's strategic decision. In this paper, we formulate the supplier's tradeoffs among the information advantage, payment disadvantage, and channel competition when it makes retailing decisions. We study the supplier's two representative strategies: (1) relying on a small retailer that does not accumulate much information but can settle accounts immediately (referred to as Real-time Payment Retailing) or (2) relying on a powerful retailer that shares accurate demand information but incurs deferred payment (referred to as Deferred Payment Retailing). We built game-theoretical models and found that, interestingly, the supplier will prefer Deferred Payment Retailing when the supplier's cash opportunity cost is high. We identify three interactive forces, namely, the pricing power effect, the demand size effect, and the information value, to interpret the rationality of the supplier's preferences over Real-time and Deferred Payment Retailing strategies. [ABSTRACT FROM AUTHOR]

Due to the significant impact of COVID-19, financial markets in various countries have undergone drastic fluctuations. Accurately measuring risk in the financial market and mastering the changing rules of the stock market are of great importance to macro-control and financial market management of the government. This paper focuses on the return rate of the Shanghai Composite Index. Using the SGED-EGARCH(1,1) model as a foundation, a quantile regression is introduced to establish the QR-SGED-EGARCH(1,1) model. Further, the corresponding value at risk (VaR) is calculated for a crisis and stable period within each model. To better compare the models, the Cornish-Fisher expansion model is included for comparison. According to the Kupiec test, VaR values calculated by the QR-SGED-EGARCH(1,1) model are superior to other models at different confidence levels most of the time. In addition, to account for the VaR method's inability to effectively measure tail extreme risk, the expected shortfall (ES) method is introduced. The constructed model is used to calculate the corresponding ES values during different periods. According to the evaluation index, the ES values calculated by the QR-SGED-EGARCH(1,1) model have a better effect during a crisis period with the model showing higher accuracy and robustness. It is of great significance for China to better measure financial risk under the impact of a sudden crisis. [ABSTRACT FROM AUTHOR]

This paper examines the shock spillovers between US sectors and their dependence on the intersectoral business linkages. Our forecast error variance decompositions reveal significant shock transmissions among trading sectors, especially in turbulent periods such as the financial crisis and the COVID-19 pandemic. The dymamics of shock spillovers reflect the impacts of the pandemic on economic sectors. Shock spillovers are shown to be influenced by the strength of the intersectoral trading relationships. Shocks to a sector's important supplier have a strong impact on the forecast error variance of the sector's stock return. The total directional spillovers from/ to a sector are linked with the number of close commercial linkages between that sector and other sectors. [ABSTRACT FROM AUTHOR]

The concept of resilience—i.e., the ability of a unified structure to absorb shocks—is of high relevance in the context of network modelling and analysis, mainly when referred to finance. This paper starts from this premise, and deals with the resilience of a financial interbanking system. At this aim, we firstly introduce a new measure of the resilience of a network, by taking into full consideration the influence of the topology of the network and the weights of its links in the shocks propagation; then, we build one financial network model related to the quarterly-based interbanking sector, whose weights are calibrated on high quality empirical data; lastly, we compute the resilience measure of the considered networks. A discussion of the results is provided, by considering both finance and network theory perspectives. [ABSTRACT FROM AUTHOR]

This paper analyses the volatility transmission between European Global Systemically Important Banks (GSIBs) and implied stock market volatility. A Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model is applied to determine the dynamic correlation between returns of Europe's GSIBs and the world's most prominent measure of market "fear", the CBOE Volatility Index (VIX). The results identify a higher negative co-relationship between the VIX and GSIB returns during the COVID-19 period compared with the Global Financial Crisis (GFC), with one-day lagged changes in the VIX negatively Granger-causing bank returns. The asymmetric impact of changes in implied volatility is examined by quantile regressions, with the findings showing that in the lower quartile–where extreme negative bank returns are present–jumps in the VIX are highly significant. This effect is more pronounced during COVID-19 than during the GFC. Additional robustness analysis shows that these findings are consistent during the periods of the Swine Flu and Zika virus epidemics. [ABSTRACT FROM AUTHOR]

This paper is concerned with the risk management practices of an electricity retailer motivated by the Dutch electricity market. We examine the effectiveness of the existing base- and peak-load futures contracts as a risk management tool for the electricity retailers. We analytically characterize the retailer's optimal hedging policy as a function of the serial correlation of the prices and the demand profiles of its customers. We find that the retailer typically over-hedges in the futures market, and the over-hedging amount increases when both base- and peak-load contracts are used. Our findings indicate that although the existing contracts in the futures market are quite efficient to replicate the exposure from profiled customers, when industrial consumers and renewable generation are included to the retailer's portfolio, the effectiveness of such contracts decreases substantially. In our motivating example, hedging the risk of the profiled customers with base-load contracts, the firm may reduce the variance of its cash flows by 85.9%. In addition to the base-load contracts, including peak-load contracts into the hedging portfolio of the retailer increases the efficiency of hedging to 89.3%. However, when we consider the aggregate portfolio of the retailer including profiled customers, industrial consumers and renewable contracts, the efficiency of hedging through the existing futures contracts goes down as low as 32.8% during certain periods. [ABSTRACT FROM AUTHOR]

It is well documented that the biopharmaceutical sector has exhibited weak financial returns, contributing to underinvestment. Innovations in the industry carry high risks; however, an analysis of systematic risk and return compared to other asset classes is missing. This paper investigates the time–frequency interconnectedness between stocks in the biotech sector and ten asset classes using daily cross-country data from 1995 to 2019. We capture investors' heterogeneous investment horizons by decomposing time series according to frequencies. Using a maximal overlap discrete wavelet transform (MODWT) and a dynamic conditional correlation (DCC)-Student-t copula, diversification potentials are revealed, helping investors to reap the benefits of investing in biotech. Our findings indicate that the underlying assets exhibit nonlinear asymmetric behavior that strengthens during periods of turmoil. [ABSTRACT FROM AUTHOR]

With the application of the optimal stopping techniques, this paper proposes a filter rule for investors in emerging stock markets. In a bull market, once the stock price falls down to the optimal filter size, investors should sell the stock to avoid massive losses. We show that the optimal filter size is a function of the historical highest price, the weights of the future returns and the current drawdown in the investor's utility function, the characteristics of the underlying stochastic price process, and the discount rate. Out-of-sample tests verify that this filter rule is valid, and the selling signals generated by the filter rule are at the beginning of the downtrend in the most emerging stock markets. [ABSTRACT FROM AUTHOR]

In this paper, we examine extreme spillovers among the realized volatility of various energy, metals, and agricultural commodities over the period from September 23, 2008, to June 1, 2020. Using high-frequency (5-min) price data on commodity futures, we compute daily realized volatility and then apply quantile-based connectedness measures. The results show that the connectedness measures estimated at the lower and upper quantiles are much higher than those estimated at the median, implying that realized volatility shocks circulate more intensely during extreme events relative to normal periods, which endangers the stability of the system of volatility connectedness under extreme events such as the COVID19 outbreak. There is evidence of a strong asymmetry between the behaviour of volatility spillovers in lower and upper quantiles, given that the connectedness measures estimated at the upper quantile are the highest. The main results are robust to rolling window size and other alternative choices. Our analyses matter to investors and policy makers who are concerned with the stability of commodity markets. [ABSTRACT FROM AUTHOR]

This study examines whether nonlinear effects of income diversification on bank stability occurs in the European banking system. Based on the framework developed by Tone and Tsutsui (Omega 42(1):124–131, 2014), we propose a novel three-stage (deposit producing, intermediation, and value maximization) dynamic network slacks-based measure model to assess bank stability in a sample of 114 European commercial banks during the post financial crisis period (2010–2019). We use the panel smooth transition regression model (PSTR), as introduced by González et al. (Panel smooth transition regression models, CREATES research paper 2017-36. Department of Economics and Business Economics, Aarhus University, 2017), to investigate the potential regime-switching behavior of the relationship between income diversification and stability. Our findings show that higher levels of income diversification, into and within nontraditional banking generating activities, worsen bank stability. It results that, in the case of European banks, income diversification is sub-optimal as no benefits are found from "over diversification". Important policy implications arise from our findings pertaining to the optimality of income diversification and stability, which could be in conflict with banks' traditional lines of business aiming at promoting lending activities. [ABSTRACT FROM AUTHOR]

This paper examines optimization models that use liquidity constraints to track an index. Liquidity is relevant from a risk management perspective but has hardly been explored in the portfolio optimization literature. The liquidity aspect is especially critical in emerging markets. We present two modeling approaches to instill liquidity in index tracking portfolio optimization. The first one defines the portfolio liquidity as the weighted average of assets' liquidities and accounts for financial volume, turnover, and Amihud's metric. The second one models liquidity with the introduction of financial practice parameters related to the liquidation level and the monetary value of the constructed portfolio. An extensive empirical analysis is conducted to replicate two Brazilian stock market indices: Ibovespa and SMLL. As expected, liquidity-constrained portfolios show higher liquidity and higher tracking errors. A counter-intuitive result is observed for the first liquidity-constrained approach in which the number of assets included in the portfolio decreases as the liquidity requirement gets tighter. In the second approach, as liquidity becomes tighter, the number of assets in the portfolio increases. This observation is confirmed by investigating the diversification of the constructed portfolios using the Gini index. [ABSTRACT FROM AUTHOR]

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil. [ABSTRACT FROM AUTHOR]

Risk aggregation and capital allocation are of paramount importance in business, as they play critical roles in pricing, risk management, project financing, performance management, regulatory supervision, etc. The state-of-the-art practice often includes two steps: (i) determine standalone capital requirements for individual business lines and aggregate them at a corporate level; and (ii) allocate the total capital back to individual lines of business or at more granular levels. There are three pitfalls with such a practice, namely, lack of consistency, negligence of cost of capital, and disentanglement of allocated capitals from standalone capitals. In this paper, we introduce a holistic approach that aims to strike a balance of optimality by taking into account competing interests of various stakeholders and conflicting priorities in a corporate hierarchy. While unconventional in its objective, the new approach results in an allocation of diversification benefit, which conforms to the diversification strategy of many risk management frameworks including regulatory capital and economic capital. The holistic capital setting and allocation principle provides a remedy to aforementioned problems with the existing two-step industry practice. [ABSTRACT FROM AUTHOR]

This study utilizes classification models to provide a robust algorithm for imbalanced data where the minority class is of the interest, that is, in the context of default payments. In developing an integrated predictive accuracy algorithm, this study proposes machine learning classifiers and applies DNN, SVM, KNN, and ANN. The proposed algorithm utilizes a 30,000 imbalanced dataset to improve the accuracy of the prediction of default payments by implementing oversampling and undersampling strategies, such as synthetic minority oversampling technique (SMOTE), SVM SMOTE, random undersampling, and ALL-KNN. The results indicate that the SVM under the ALL-KNN sampling technique is able to achieve an accuracy of 98.6%, with the lowest cross entropy loss measurement of 0.028. Through the accurate implementation of the neural networks and neurons used in the proposed algorithm, this paper presents better insights into the functioning of the neural networks when used in conjunction with the resampling techniques. Using the methodology and algorithm presented in this study, credit risk assessments can be more accurately predicted in practical applications where most of the clients are categorized as non-default payments. [ABSTRACT FROM AUTHOR]

The paper examines the sensitivity for the solution of linear programming problems using Bayesian techniques, when samples for the coefficients of the objective function are uncertain. When data is available, we estimate the solution of the linear program and provide statistical measures of uncertainty through the posterior distributions of the solution in the light of the data. When data is not available, these techniques examine the sensitivity of the solution to random variation in the coefficients of the linear problem. The new techniques are based on two posteriors emerging from the inequalities of Karush–Kuhn–Tucker conditions. The first posterior is asymptotic and does not require data. The second posterior is finite-sample-based and is used whenever data is available or if random samples can be drawn from the joint distribution of coefficients. A by-product of our framework is a robust solution. We illustrate the new techniques in two empirical applications to the case of uncertain Data Envelopment Analysis efficiency, involving two large samples, of US commercial banks and a sample of European commercial banks regulated by the Single Supervisory Mechanism. We analyse whether some pre-determined criteria, associated with size and new supervisory framework, can adequately affect the solution of linear program. The results provide evidence of substantial improvements in statistical structure with respect to sensitivities and robustification. Our methodology can serve as a consistency check of the statistical inference for the solution of linear programming problems in efficiency under uncertainty in data. [ABSTRACT FROM AUTHOR]

The article titled "Project Management and Scheduling 2021" is a special issue that follows the 17th International Conference on project management and scheduling (PMS) held online in April 2021. The conference brought together over 100 researchers and practitioners in the field. The special issue contains contributions from both conference participants and researchers who did not attend. It features eight accepted papers that cover various aspects of project management and scheduling, providing breakthrough results and enriching the literature in the field. The papers address topics such as project forecasting accuracy, risk response budget allocation, scheduling algorithms, resource-constrained project scheduling, and optimization variants. The article concludes with a note from the publisher, Springer Nature, emphasizing its neutrality and impartiality. [Extracted from the article]

This article discusses recent advances in mathematical methods for finance. It highlights the impact of developments in financial markets, technology, and societal challenges on the field of Mathematical Finance. The article also mentions the emergence of new financial products, regulatory frameworks, and trading infrastructures, as well as the revolutionary changes brought about by artificial intelligence and machine learning techniques in numerical methods for finance. Additionally, it acknowledges the growing importance of new types of risks, such as climate-related and cyber-risks, in the financial industry. The article introduces a special issue on Recent Advances in Mathematical Methods for Finance, which provides a comprehensive overview of the latest developments in the field. The special issue includes 44 papers that underwent a rigorous peer review process, covering both theoretical and applied research in mathematical finance. The authors express their gratitude to the editor and staff for the opportunity to edit the special issue and to the referees and authors for their contributions. They hope that this collection of papers will stimulate further research in the field. [Extracted from the article]

In this paper, an optimal maintenance policy is developed for a k-out-of-n system that works in a random cycle. A k-out-of-n system, which consists of n independent components and functions if at least k of the n components function, is one of the most important systems in reliability engineering. Consider that a component suffers from either minor or catastrophic failure, which is minimally repaired or lying idle, respectively. When n - k + 1 idle components occur, the system completely fails and undergoes corrective replacement. The notations of preventive replacement first and last are adopted; in other words, the system undergoes preventive replacement before complete failure at a planned time or at a random working time, whichever occurs first or last. The present paper aims to develop a two-stage maintenance methodology for a k-out-of-n system, and determine the optimal number of components and preventive replacement time that minimize the mean cost rate functions, respectively. For each model, the mean cost rate function is formulated analytically and the optimization problem is solved theoretically. Finally, a numerical example is given to demonstrate the applicability of the methodology derived from this paper. The proposed models provide a more general framework to analyze the maintenance policy for the k-out-of-n system. [ABSTRACT FROM AUTHOR]

There exist two types of Data Envelopment Analysis (DEA) approaches to the Olympic Games: conventional and fixed-sum outputs (FSO). The approach proposed in this paper belongs to the latter category as it takes into account the total number de medals of each type awarded. Imposing these constraints requires a centralized DEA perspective that projects all the countries simultaneously. In this paper, a multiobjective FSO approach is proposed, and the Weighted Tchebychef solution method is employed. This approach aims to set all output targets as close as possible to their ideal values. In order to choose between the alternative optima, a secondary goal has been considered that minimizes the sum of absolute changes in the number of medals, which also renders the computed targets to be as close to the observed values as possible. These targets represent the output levels that could be expected if all countries performed at their best level. For certain countries, the targets are higher than the actual number of medals won while, for other countries, these targets may be lower. The proposed approach has been applied to the results of the Tokyo 2020 Olympic Games and compared with both FSO and non-FSO DEA methods. [ABSTRACT FROM AUTHOR]

The smart supply chain innovation (SSCI) has become the key way for enterprises to enhance their competitiveness. Therefore, it is very important for the current supply chain enterprises to choose a reasonable innovation path. Through the literature review method, this paper summarizes the four main evaluation indicators of path selection for smart supply chain innovation, which are technical indicators, organizational environment indicators, operational efficiency indicators, risk prevention and control indicators. According to the characteristics of evaluation index, the improved Fuzzy Entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is proposed. This method considers the situation that the original data contains both interval value and fixed value data. Firstly, the standardized decision matrix is constructed, and then the optimal index weight is established by Fuzzy Entropy method. Secondly, this paper calculates the weighted decision matrix by using the method of sub item weighting of hierarchical indicators. Finally, the improved TOPSIS method is used to determine the relative closeness of each scheme. According to the weighted decision matrix, the innovation path index of the smart supply chain (SSC) is further calculated. We use the actual case of a company to analyze the evaluation method for three different SSCI paths. This paper provides a reference for the path selection of SSCI from the aspects of theory and practice. [ABSTRACT FROM AUTHOR]

Organisations need to develop long-term strategies to ensure they incorporate innovation for environmental sustainability (IES) to remain competitive in the market. This can be challenging given the high level of uncertainty regarding the future (e.g., following the COVID pandemic). Supplier selection is an important decision that organisations make and can be designed to support IES. While the literature provides various criteria in the field of IES strategies, it does not identify the criteria which can be utilised to assist organisations in their supplier selection decisions. Moreover, the literature in this field does not consider uncertainty related to the occurrence of possible future events which may influence the importance of these criteria. To address this gap, this paper develops a novel criteria decision framework to assist supplier evaluation in organisations, taking into consideration different events that may occur in the future. The framework that combines three decision-making methods: the stratified multi-criteria decision-making method, best worst method, and technique for order of preference by similarity to ideal solution. The framework, proposed in this paper, can also be adopted to enable effective and sustainable decision making under uncertainty in various fields. [ABSTRACT FROM AUTHOR]

The balancing of the disassembly line directly affects the productivity of the disassembly process. The disassembly line balancing (DLB) problem can be determined as assigning the tasks to serial workstations to optimize some performance measures like number of workstations, cycle time, removing hazardous parts earlier, etc. The aim of the paper is to develop an efficient heuristic algorithm to minimize the number of workstations under a pre-known cycle time. In this paper, a genetic algorithm (GA) and a constructive heuristic based on the Dijkstra algorithm is proposed to solve the DLB problem with stochastic task times that is caused by the nature of disassembly operation. The proposed algorithms are tested on benchmark problems and compared with the results of the piecewise-linear model (PLM) and simulated annealing (SA). The average relative percentage deviation is applied to transfer the obtained number of workstations. The results obtained by GA are clearly superior in all tests problem according to average relative percentage deviation. Moreover, the proposed constructive heuristic based on the Dijkstra algorithm is also superior to PLM and SA algorithm with respect to number of workstations and the computational times. The proposed approaches can be a very competitive and promising tool for further research in DLB literature and real cases in industries according to test results. Disassembly lines which need less time or number of workstations for balancing may be simply designed by the proposed techniques. [ABSTRACT FROM AUTHOR]

The aim of this paper is to solve an agricultural technology packages selection problem by considering multiple dimensions which influence a maize producer's preferences. The decision-making process is aided by a new multicriteria method for eliciting scale constants in additive models: flexible and interactive tradeoff (FITradeoff). This method works with partial information, obtained from the decision maker (DM), and thus reduces the time that the DM has to spend on the process for eliciting his/her preferences as he/she may avoid answering difficult questions. The decision-making process makes use of a decision support system (DSS), in which the DM interactively gives preference statements in a structured manner. The DSS gives flexibility to the DM, in such way that he/she gives as much information as he/she is willing to. Graphical visualization is provided at each step in order to help the DM's analyses. Throughout the description of an application, some insights are provided including a discussion of the advantages and features of the FITradeoff method. [ABSTRACT FROM AUTHOR]

This paper explores the optimal joint decision of product information disclosure and ordering in a blockchain-enabled luxury supply chain. Using analytical models, we investigate the optimal joint decision of information disclosure and ordering under three scenarios (i.e., wholesale contracts only, revenue-sharing (RS) contracts only, and a hybrid of these two types of contracts). Furthermore, we extend our study to examine the impacts of the number of competing retailers and the retailers' fairness concerns on supply chain members' optimal decisions. Lastly, the theoretical results are checked and illustrated by numerical examples with sensitivity analysis. The main findings are as follows: (1) As the proportion of information-sensitive consumers in the market increases, the level of product information disclosure of supply chain members increases in varying degrees, while supply chain members' order quantities and profits first decrease and then increase in varying degrees. (2) When a RS contract is acceptable for all supply chain members, all members benefit from the cooperation between the manufacturer and retailers. (3) Although all supply chain members may benefit from an increase in the number of retailers, when the number of retailers is greater than a certain threshold, retailers would be caught in a "prisoner's dilemma" of product information disclosure due to consumer information overload. Moreover, to maximize business profits, manufacturers should sometimes strictly limit and control the number of their reseller partners, rather than blindly expand their markets. (4) Retailers may benefit from their own fairness concerns if and only if the level of fairness concerns is sufficiently low, otherwise such concerns would be harmful to all supply chain members. [ABSTRACT FROM AUTHOR]

The burgeoning digital economy has expanded the information gap in supply chains. Supply chain information transparency has thus attracted increasing attention from both industry and academia. Meanwhile, as consumers pay more attention to corporate social responsibility (CSR), firms have made efforts to audit their suppliers to avoid CSR violations. This paper explores the strategic role of CSR auditing in supply chain information transparency. We examine how CSR auditing interacts with supply chain information transparency by considering a scenario where a firm strategically sells either a single product or a product line through an online retail platform. Our results show that when the product line strategy is uncertain, the firm's audit commitment strengthens the platform's incentive to voluntarily disclose information. Our results also show that when the length of the product line is unchangeable, the platform prefers to share the information even if the firm does not make audit commitments. Additionally, when the platform's information sharing decision and the firm's product line design are made simultaneously, the platform may choose to privately withhold the information if the product line extension cost is in the moderate range and the demand loss from CSR-concerned consumers is relatively minor. [ABSTRACT FROM AUTHOR]

Vaccination is a well-known method to protect the public against an epidemic outbreak, e.g., COVID-19. To this end, the government of a country or region would strive to achieve its target of vaccination coverage. Limited by the total vaccine capacity of public hospitals, the government may need to cooperate with private hospitals or clinics for more vaccination. Exploring in this paper government coordination of public and private resources for vaccination, we model a vaccine system consisting of a public hospital, a profit-maximizing private clinic, and self-interested individuals, under three scenarios: (1) without information sharing (concerning vaccine inventory and vaccine price), (2) with information sharing and subsidy, and (3) with information sharing and allocation. We find that, under scenario (1), the vaccine demand is fully satisfied by the public hospital and the private clinic cannot make any profit. Under scenario (2), the private clinic is willing to enter the vaccine market with a positive profit-maximizing vaccination coverage. Under scenario (3), the socially optimal vaccination coverage may be lower than that under scenario (1). Moreover, we conduct a sensitivity analysis to generate practical implications of the research findings for vaccination policy-making. Our results provide both theoretical and managerial insights on vaccine supply decision, government intervention, and vaccination coverage. [ABSTRACT FROM AUTHOR]

The increased environmental awareness of consumers has led supply chains (SC) to green their operations. To extract a higher portion from the expanded demand due to greening activities, SC parties may hide key information regarding their green activities. This paper investigates the channel coordination problem in a green SC consisting of a manufacturer who sells a green product through a retailer. Both parties may involve in greening operations to expand an environmental-aware market; however, the retailer is privy to the information about his green sales effort. The analysis of the first-best outcome characterizes the conditions for (i) hold-up problem under which the retailer benefits from free ride on the manufacturer's greening operations effort, (ii) commitment strategy from the retailer to cover for the market expansion due to the manufacturer's underinvestment in greening operations, and (iii) synergy in greening efforts. We then solve for the optimal incentive contracts under asymmetric information. Our analysis suggests that the manufacturer can include her greening effort in the contract to work as an incentive-fee; the higher level of greening effort by the manufacturer incentivizes the retailer to increase his green sales effort. We also show that the wholesale price term works as a screening tool to avoid the low efficient retailer from mimicking the high efficient one. Finally, we show that information asymmetry reduces the social welfare in a green market; it leads to a higher market price and a lower greening effort level. [ABSTRACT FROM AUTHOR]

In e-commerce, sellers can disclose product information (such as quality, size information, function, and so on) to make consumers understand the products. However, in the process of information disclosure, consumers often fall into information distortion or information loss. Because of its immutability and traceability, blockchain can help e-commerce sellers improve information disclosure and ensure the efficiency of information transmission. We study a duopoly competitive e-commerce market in which two e-commerce sellers compete in information disclosure. According to whether to apply blockchain, we divide the sellers' decision-making into four research scenarios (NN, BN, NB, BB). Based on the above four scenarios, we get the market demand of different products depending on the consumer utility, and further establish the game model in the competitive environment. This paper explores the impact of blockchain on information disclosure and consumer surplus, and achieves the Nash equilibrium of blockchain application for both sides. In the expansion model, we study e-commerce sellers' risk aversion and capital constraints, and further explore their impact on blockchain in practice. Finally, combining with blockchain's characteristics, we also analyze the impact of the application of blockchain at other aspects on the supply chain. We find that when consumers' trust in information is low or the cost of blockchain applications is low, all e-commerce sellers in competition will adopt blockchain. In addition, when consumers have low trust in information, it will be difficult to achieve complete equilibrium in the application of blockchain as their risk aversion increases. For capital constrained sellers, when the cost of blockchain application is low, it will be difficult to achieve full equilibrium for blockchain applicants as the bank financing rate increases. [ABSTRACT FROM AUTHOR]