Jump clustering and volatility asymmetric feedback are important features in stock markets. This paper studies the option pricing issues for a dynamics of jump-diffusion process, which considers the mechanism of time-varying jump arrival rates, diffusion volatility clustering and the asymmetric cross-feedback effect. First, this paper presents the no-arbitrage conditions of equivalent martingale measures for the general jump-diffusion process based on local risk neutral valuation relationship; and then estimates the parameters and jump risk premium of the dynamic jump-diffusion model using the sequential Bayesian learning approach; Finally is the empirical research on the standardized European options of S&P500 Index and Dow Jones Industrial Average, APPLE, IBM and JP Morgan. Our study shows the significant evidence of the jump self-exciting, volatility clustering and asymmetric cross-feedback; these jumps also have a higher persistent influence and show a greater leverage effect to the stock markets; the dynamics of the cross-feedback jump-diffusion model have better performance in option pricing compared with these one-way-feedback jump-diffusion models. Jump risk premium is significantly higher than that of diffusion risk, which plays a dominant role in the process of asset pricing.
Based on considering the execution and inventory risk, the spread process is modelled and the inventory penalty function is also introduced. The market-making strategy is to maximize the expected utility function. The solution of the strategy can be considered as a stochastic optimal control problem. Employing the dynamic programming principle, the stochastic optimal control problem can be written as related variational inequality and solved by finite difference method. The order submissions given by the strategy are in accord with the assumption for execution density, spread and inventory' effect on order submission. The empirical and reliable tests of the strategy show that it has stable profitability under reasonable assumptions.
To study the nonlinear effects of different spatial units, we focus on the estimation and corresponding hypothesis test of the spatial dynamic nonparametric Durbin model with fixed effects. Since the spatial dynamic nonparametric Durbin model is a special kind of spatial dynamic panel model, we propose an iterative approach and corresponding hypothesis test of the spatial dynamic nonparametric Durbin model with fixed effects at first. Then, based on the results of Monte Carlo simulation, we use a three-stage iterative approach to improve the iterative approach. Through simulation experiments and empirical application, it proves that the three-stage iterative approach is stable and effective for the spatial dynamic nonparametric Durbin model with fixed effects, especially when T and N are large.
Recently rising internet public opinions have been bringing profound influences to the capital market, as well as new challenges to regulators and listed firms. Using 1,325 questionnaires collected by regulators, this study empirically tests whether and how listed firms' response to the internet public opinions (hereafter RIPO for short) can affect information efficiency in the capital market. This study document that public opinion crisis results in current-year stock price crash and reproduction from the we-media will magnify the negative effect to the stock price, but firms that emphasize and invest more in the RIPO can decrease the negative effect. Listed firms can spread the firm-specific information to the capital market to lower the stock price synchronicity and the probability or magnitude of future stock price crash by emphasizing and investing in the RIPO. The conclusions we draw from this study show that RIPO can increase the information efficiency in the capital market, RIPO is not a beauty-show project, but play a pragmatic role in the capital market. This study also shows practical significance to perfect the information management system and improve the transparency of the capital markets.
Since equity financing has been an important part in boosting the rapid growth of enterprises, the competition in the product market takes tolls on enterprises' financing decision in the capital market. An equity financing model based on the perspective of market competition is constructed to analyze the effects of market competition on equity financing decision when one of the two retailers is in the process of exploiting a promising market. Core research indicates that the retailer with high growth, high valuation or strong product substitution must consider the "double effect" of market competition on financing, while the retailer with opposite features is seldom influenced by market competition. In addition, market competition restrains the retailer's equity financing and improves the proportion of its original shareholders and their profits, however reduce the development of high growth retailer.
The promotion of "China made 2025" strategy needs to be supported by the new industry internet system-"intelligent production & service network". How to ensure its stability and can be operated orderly? This is the core problem of this research. Therefore, firstly, based on the "logistic mapping model" to discuss the biggest safeguard ability of "intelligent production & service network" network Jieke, and combining with "Jieke theory" to put forward the scheme of constructing network Jieke system. Secondly, the problem of its Jieke opening degree is discussed, and then the "Pan-system theory" is introduced to research the observocontrol problem of Jieke opening degree; system network Jieke opening degree has upper limit, according to the actual situation can increase or decrease, if necessary, can be reduced to 0 in order to protect system security. Finally, the network effect principle and its economic and managerial significance of the system are discussed under certain opening degree. This research theoretically expand the "Jieke theory" and "pan-system theory" applications, from the practical level, perfect "intelligent production & service network" operation mechanism and safeguard function. It is of great significance to push forward the structural reform of supply-side in China.
Current mandatory carbon reduction policies and environmental regulation provides too much space for both government and enterprises to seek rent, which also generates a strong intention of enterprises to avoid emission reduction responsibilities through rent-seeking behavior. Based on the SWARM model of carbon emission trading (CET) market, we simulated emissions trading in CET market in different rent-seeking scenarios. This paper studied on the effect of different rent-seeking scenarios in free carbon emission allowances distribution system on the operation efficiency of CET market through market liquidity, volatility and effectiveness. The results prove that rent-seeking affects liquidity and effectiveness of CET market while it has no significant impact on the market price fluctuations. The positive, rather than negative impact of rent-seeking on market efficiency reflects that the initial allocated free carbon allowance cannot realize optimal allocation of resources in free market. Reducing the government's intervention on CET market and changing the distribution system from free emission allowance to fixed-price or auction mechanism will not only strengthen price signals and improve energy utilization efficiency, but also cut off the possibilities of rent-seeking behavior at the source.
For solving the resource constrained project scheduling problem (RCPSP) with stochastic durations, robust resource allocation and time buffer insertion are combined effectively. A two-stage integrated optimization algorithm is proposed to generate robust project schedules against disruptions. In the first stage, a myopic expected penalty cost (MEPC) procedure is designed to construct a stable resource flow network by efficiently allocating resources among project activities. To further improve the schedule stability, an expected penalty cost (EPC) algorithm that relies on the fixed resource flow network is proposed in the second stage to minimize the expected penalty cost through inserting time buffers in front of the activities with higher delay risks. Finally, extensive computational experiments are performed to verify the feasibility and effectiveness of the two-stage algorithm from two aspects of solution robustness and quality robustness. The results indicate that the project schedule generated by the integration of the resource flow network and time buffers not only can achieve shorter completion times, but also can deal with disruptions more effectively during project execution.
In this paper, we established the inventory model considering resales of custom returns for seasonal sales, sales of the last period or the promotional period. We addressed the simultaneous determination of price and order quantity to maximize the profit. We derived the analytic solution for the deterministic problem, and demonstrated the existence condition and algorithms of the optimal solution for the stochastic demand obeying uniform distribution. The results show that online retailers could gain more profits when they develop inventory strategy considering resales of returns, and customers could also buy products at lower price. The advantages of using this strategy are more obvious when the product value is high, the amount of returns is large or return ratio is sensitive to price.
For the high cost of cold chain and highly perishable for fresh food, we integrate food quality in decision-making of food cold chain storage and distribution management. We provided a methodology to describe the food quality degradation and designed a multi-stages multi products fresh food cold chain operation mode, which can be integrated in a mixed-integer linear programming model to optimization distribution center location, route planning and temperature selection in order to ensure food quality and safety while minimize the logistics cost. Finally the numerical simulation and comparative study was given to verify the feasibility of the model and the algorithm.
Retailers' sales efforts not only influences the capital expenditures, but also directly impact on the demand of the supply chain. However, it has not gained enough scholarly attention in supply chain finance research. Therefore, this paper develop an supply chain financial model with retailers' sales efforts, and study the optimal operation strategy and financing strategy of supply chain when the capital constrained retailer obtain bank loan or equity financing, then analyse how the retailer choose the best financing way. The results show that the retailer's capital level has great impact on the decision equilibrium and revenue of supply chain; limited funds are seriously restrict the retailer's sales efforts and reduce the operational efficiency of the supply chain; to some extent, financing services can alleviate the constraints and the affection on the decision equilibrium of the supply chain is different in each financing way. When retailers obtain financing, if the capital level is extremely low, they should choose equity financing, but with capital level rise, they should choose bank loan for financing.
This paper analyzes the weighted Shapley value for cooperative games in which partial cooperation is based on a set system, which is the set of feasible coalitions that can be formed by players in a game, and the structure of feasible coalitions is completely free. In this context, we define the weighted Shapley value which distributes the Harsanyi dividends proportional to the weights of players. We provide two axiomatic characterizations of the weighted Shapley value:one by means of component efficiency and proportional fairness, and the other with efficiency and weighted balanced contributions. Moreover, the stability of feasible coalitions is analyzed, and some properties of the weighted Shapley value are obtained. Finally, we give two examples about assignment games showing that our method is feasible and effective.
Random turnover of R&D staff influences new product R&D project portfolio scheduling. Using discrete Markov chain to describe staff's turnover processes with multi-skilled R&D staff as the scheduling object, we proposed a stochastic multi-objective constraint optimization model for the new product R&D project portfolio scheduling. Specifically, three objectives are strategic gains for talent cultivation, R&D cycle and R&D costs. The proposed model is solved by an adaptive Pareto sampling algorithm which utilizes the sampling method of Markov chain Monte Carlo, we calculate objective values for the deterministic model by serial schedule generation scheme, and obtain the Pareto set by the nondominated sorting genetic algorithmⅡ for the multi-objective expected value model. Both model and algorithm were tested by a real-world case of staff scheduling for a new electric energy-saving product R&D project portfolio in a Chinese company. Since the algorithm converged well and obtained the Pareto set effectively, results indicated that the stochastic model is more suitable to reflect company's reality than the deterministic model. Practically, enterprises can use our model and algorithm to make an effective decision on multi-skilled staff scheduling scheme for a new product R&D project portfolio under the stochastic turnover scenario.
Pilots' transfer and promotion planning has many typical characteristics such as complex routes and long cycle, thus, making effective and economic human resource allocation is the critical problem the civil aviation is faced with. The paper makes a quantitative study on the problem. With the constraints of the demand of pilots, the paper builds a nonlinear integer programming model to minimize the generalized cost, and then converts it into a corresponding network flow model in order to solve the problem conveniently and designs algorithms. At last, a practical example is illustrated to verify the efficiency of the method.
Improvements of fitting precision and tendency similarity are of vital importance for forecasting analysis. To promote data characteristic adaptation of grey prediction model, this paper analyzes the relationship of grey differential equation and whitenization equation, and studies the restoring process of response function, then proposes a novel characteristic adaptive GM(1,1) model, namely CAGM(1,1) model. This model uses the novel background formula with quantile variable to construct grey differential equation, and employs the transformed model to derive the process of parameters evaluation. Further, we construct the time response formula based on background series; to improve forecasting performance, we propose a new fitness function according to grey incidence method and utilize the particle swarm algorithm to search optimal values of the variables in restoring process. The new model is used to analyze traffic pollution emission in China, and we construct GM(1,1) and CAGM(1,1) for comparison. The results confirm that the model proposed in this paper outperforms traditional GM(1,1) model and could be useful and effective in practice.
Medium and long term load forecasting is an important prerequisite for the power sector's development planning and stable operation. According to the multiple factors of influencing the medium and long term power load forecasting accuracy, this paper uses the stepwise regression method to identify the key influencing factors from a number of factors associated load forecasting, and proposes a probability density forecasting method based on the Box-Cox transformation quantile regression combined with kernel density estimation. The probability density forecasting results of load under the different quantiles at any year in the next few years are evaluated. The proposed method is likely to realize the accurate range prediction of future annual electricity consumption. The historical load and socio-economic data of Anhui province are adopted as simulation experiment. The results show that the proposed method not only realizes the medium and long term load forecasting, but also well improves the precision of medium and long-term power load probability density forecasting by means of introducing strong relation factors, and effectively solves medium and long term power load probability density forecasting problem considering multiple factors.
Enterprise architecture has become increasingly emphasized as a crucial role of requirement elicitation, decision making and budget planning in recent years. Still, many problems seem far from being solved because enterprise architecture is a discipline with abstract and macroscopic. In order to deal with the problems, this paper analyzes 186 articles of enterprise architecture in three years based on other persons' research, obtaining the results concluding lists of conferences and journals, co-authorship network, research universities and research topics. Then we predict the future enterprise architecture research after comparing with other persons' results.
Aiming at the problem that the semantic description of primary FIPA-ACL in the equipment support agent communication is not enough, the communication efficiency is low and the semantic recognition is not accurate, design and implementation of agent communication language based on extended FIPA-ACL is proposed. Firstly, the communication performatives of FIPA-ACL are extended, and the formal semantics of the primitive is described. Secondly, the communication ontology is defined. Thirdly, the content grammer is defined and the communication content is described based on FIPA-SL. Finally, the agent communication language is implemented. The feasibility and effectiveness of the proposed language is verified by a study case of military equipment support simulation demonstration and effectiveness evolution system.
Continuous observation on moving targets at sea has important implications to maintain the safety of the navigation barrier. Existing methods either rely on satellite observations, or only rely on UAV observation, did not solve the problem of continuous observation of the maritime moving target. On the basis of analysis of the current methods, we constructed the aerospace collaborative continuous observation model for moving target at sea, and put forward aerospace collaborative continuous observation strategy (ACCOS) to reduce the complexity of solving the model. ACCOS extracted five sub problems to solve the problem:Satellite planning, UAV flight plan, UAV observation sequence, target potential area and distribution probability density prediction, UAV path planning. In order to realize the optimization goal of the model, the five sub problems are modeled and solved in turn. Finally, the simulation results show that the proposed method can greatly reduce the average observation period of the target, and effectively solve the problem of the continuous observation of the moving target in the sea.
Under the condition of modern combat, operational effectiveness evaluation of anti-tank missile weapon system is the key to this kind of weapon equipment. The traditional methods of evaluating operational effectiveness are mainly focused on one subsystem of the whole equipment, which cannot fully reflect the combat capability of weapon system of authenticity and integrity. So that, an evaluation method for anti-tank missile weapon system based on combination weighting is proposed in this paper. The first step of evaluation is to establish the index system of anti-tank missile weapon operational efficiency, including index set and evaluation set, and optimize the index system according to the different expert opinions and suggestions. Then combining with the idea of combination weighting, the entropy algorithm and hierarchical weighted algorithm are weighted combination to calculate the final weights of each index in index set and attribute to each index, thus, this method can reduce the evaluation error due to subjective factors. Finally, the proposed evaluation method is verified in anti-tank missile hardware in the loop simulation platform. The experimental results show that the feasibility of the algorithm.
The parameters of m and r for approximate entropy (ApEn) and sample entropy (SampEn) have important influences on complexities. Effective approaches for selecting the common optimal parameters of m and r for ApEn and SampEn are still absent. The consistency requirement of comparisons between ApEn and SampEn and the characteristics of intersection between the two curves of ApEn and SampEn were considered. A new intersection method for determining the common optimal parameters of m and r was presented in terms of coefficients of correlation. The time series of runoff at the Zhangjiashan station and the precipitation above the station in the Jinghe watershed during 1956-2010 were taken as the object of investigation. Results show that:(I) The common optimal parameters were determined as the values of m=2 and r=0.11 times the standard deviation of time series for both ApEn and SampEn. (Ⅱ) The complexities of the precipitation and runoff present increasing significant trends. (Ⅲ) There are significant decreasing relationships between both runoff and precipitation and their complexities. The decreasing trend of precipitation impacts greatly the increase of runoff complexity. The ApEn is better than the SampEn for describing the general changing trends of time series; while the SampEn has a better performance than the ApEn for identifying the changes of peak and valley of time series. There is a good applicability for the new intersection method presented in this study for selecting the common optimal parameters of m and r for both ApEn and SapmEn.
In order to carry out more comprehensive accident analysis and prevention work via accident causation model 24Model, the authors analyze the relationship between hazards and accidents. Then the system characteristics of 24Model is expounded. The study shows that the hazards are the sources of the accident which equates to the accident causations. 24Model is a systematic accident causation model which involves features of integrity, relevance, hierarchy and dynamic. The characteristics of integrity, relevance, hierarchy and dynamic of 24Model are illustrated with an example which is the capsized accident of the "Eastern Star" ferry analyzed via 24Model.