In comparison to the US market, we use several methods to study the volatility risk and the volatility risk premium in China's market. We find that there exist anomalies in China's market. First, unlike the US market, the volatility risk in China's market is not a systematic risk on the whole. It is time-varying, and shows significantly negative during crash period while significantly positive during non-crash period. Second, the option-implied volatility risk premium in China is negative although the volatility risk is not a systematic risk, which differs from the US market and does not match the principles of finance. That might mean the options should be continuously overpriced in China. It is proved by the simulation of the Buy-Write strategy.
Convertible bond becomes an important way of refinancing for Chinese listed companies recently, and the pricing problem of convertible bond has attracted wide attention. In this paper, we propose a two-factor willow tree method to price Chinese convertible bonds with the consideration of stock price, interest rate, default rate and the trigger conditions of callable and putable clauses embedded in Chinese convertible bonds. By comparing with the existing models, our model matches the market prices better. In empirical research, we estimate the volatility of the stock price process with both historical volatility and implied volatility which is got by calibrating the valuation model with the market price of convertible bond. We find that the accuracy of the valuation model has been greatly improved.
Based on the natural experiment of the "central management enterprise salary system reform" (pay ceiling order), which was implemented on January 1, 2015, this paper empirically examines the impact of policy on the performance of listed state-owned enterprises. By using the difference-in-differences (DID) model and propensity score matching (PSM) method, we find that "pay ceiling order" has a negative effect on the operating performance of listed companies. Further studies on the dynamic effect of "pay ceiling order" show that the second year is more significant than the first year after implementation. It is also found that the policy has the heterogeneity influence on the performance of different types of enterprises. Results show that performance of central government-owned, high compensation and large-scale enterprises declined more comparing to the local government-owned, low pay and small-scale enterprises. Our findings suggest that executive compensation system of state-owned enterprises should follow the enterprise features and market-oriented reform should be accelerated to maximize the enterprise value.
As a major source of information, news media has significant impacts on the stock market investors, and further influence the stock market dynamics. In order to study the effects of online news reports on the Chinese stock market, this paper uses data collected from the Internet, and analyzes through indicators of media attention and media sentiment. After enriching the sentiment corpus, covering positive sentiment, negative sentiment and neutral sentiment, indicators of media attention and media sentiment are constructed. Based on the empirical studies of Shanghai Composite Index during the period from January 1st, 2012 to April 30th, 2016, we find that the media attention and sentiment both affect the stock market. While the sentiment index is a better indicator to capture media sentiments. Although there are various kinds of sources of online news, 52.8% of these sources provide 99.3% of all the related news reports. The results could improve the analysis and prediction of the Chinese stock market, and provide suggestions for investors.
This paper built a game theory model to discuss how liquidity affects Chinese firm innovation from the perspective of information asymmetry and goal incompatibility. The study found liquidity affects Chinese firm innovation in two ways:Threat of takeover and different type of shareholders. In terms of threat of takeover, liquidity would harm innovation activities. In terms of type of investors, liquidity's effect differs from short-term investors and shareholders. The existence of short-term investors will make liquidity impede innovation. When firm's main investors are shareholders, influence on innovation from liquidity is depended on whether improvements of firm's valuation by rising innovation input is more than the reduction through decreasing short-term return. Taking characteristics of state-owned and non-state-owned enterprises into account, we found that the stock liquidity of private company impedes its innovation and the stock liquidity of state-owned company boosts its innovation. Then we take Chinese stock market as a "natural lab" to test the conclusion of theoretical analysis with fixed effect (FE) model. The result of empirical test is similar with conclusion of theoretical analysis and reinforces the result of game theory model.
In order to optimize the investment portfolio of the international financial market, this paper selects the important stock index of the seven major stock markets in the world. First, we use the more flexible APARCH model to describe the "stylized facts" of the stock index return sequence. In the portfolio optimization model, the R-vine Copula, which is selected by the maximum spanning tree (MST) algorithm is used to describe the interdependent structure of the seven stock markets, and then measure portfolio risk under R-vine Copula dependent structure CVaR. Finally, the Mean-CVaR portfolio model was established under the R-vine Copula dependent structure condition. And compare Mean-VaR, Mean-CVaR and Mean-CVaR model based on R-vine Copula dependency structure. The empirical results shows that the interdependence between assets can be used to optimize the portfolio effect, reducing the risk of portfolio risk while increasing the rate of return. The Mean-CVaR model based on the R-vine Copula dependent structure is better than the Mean-CVaR model, while the Mean-VaR model has a relatively poor performance.
In order to introduce the long memory property of financial markets into the study of European option pricing under uncertain environment, the fractional Brownian motion is used to describe the dynamics of the underlying stock price. On the basis of fractional Black-Scholes model, considering that the financial market is uncertain with randomness and fuzziness, using stochastic analysis, fractal theory and fuzzy set theory to construct European option pricing model based on the long-term memory property of the financial market in a uncertain environment. Then the influence of Hurst index H, a measure of long-term memory in financial market, on European option pricing is analyzed. Finally, numerical experiment demonstrates the proposed pricing model are reasonable and acceptable. The results show that the pricing model of European options with long-term memory property is more suitable for financial market under uncertain environment.
For a multilateral distribution platform with consideration of cross-network externalities, this paper investigated the optimal value-added service (VAS) investing and pricing strategies. Firstly, an optimization model that maximizes the platform profit was formulated. Subsequently, the optimal VAS investing and pricing strategies were obtained by solving the optimization model. Finally, numerical analyses were conducted to investigate the influence of relevant parameters (VAS investment coefficient, marginal VAS investment cost, fundamental investment and cross-network external strengths) on the optimal strategies and the maximum platform profit. Main results of the numerical analyses are as follows:1) if the consumer utility brought by unit VAS investment increases, the platform should increase VAS investment, transaction fee, advertising fee and delivery fee, simultaneously; 2) if the platform's fundamental investment increases, VAS investment should be reduced; 3) if the delivery staff's utility brought by unit consumer's participation increases, the platform should increase advertising fee and VAS investment, and reduce the transaction fee and delivery fee, and even subsidize the consumer; 4) if the advertiser's utility brought by unit consumer's participation increases, the platform should increase VAS investment, advertising fee and delivery fee, and reduce transaction fee; 5) if the consumer's negative utility brought by unit advertiser's participation increases, the platform should greatly increase the advertising fee and the transaction fee, and reduce the delivery fee and VAS investment. The results of this paper provide important policy suggestions for the operations of multilateral distribution platforms.
Mutually exclusive products (such as liquids, hazardous chemicals, etc.) cannot be mixed into the same container. Logistics companies usually use multi-compartment trucks to deliver mutually exclusive products to customers. The loading and vehicle routing strategies are the key issues to determine the distribution efficiency and distribution costs. Considering the constraints of loading and unloading sequence and transportation time of mutually exclusive products, a joint optimization model of loading and distribution is constructed to minimize the distribution cost. This paper designs an improved genetic algorithm for solving the model, using the queen evolution and the edge reconstruction crossover operations based on probability to lift the ability of finding the optimal solution. Then we construct the testing examples based on the vehicle routing problem benchmark provided by Augerat to verify the running time and solving efficient of genetic algorithm. The simulation results show that, the solutions obtained by improved genetic algorithm are better than those of classical genetic algorithm, for small-scale examples, the improved genetic algorithm can obtains global optimal solution; for the medium-sized or large size examples with no more than 101 customers, the approximate optimal solution can be obtained in 130 seconds using genetic algorithm. The innovation of this paper lies in the establishment of a mathematical model for a new expended vehicle routing problem and the design of a fast and effective algorithm for solving the model. The mathematical model and algorithm of this paper provide a theoretical basis and algorithmic support for logistics companies to draw up distribution schedule of mutually exclusive products.
Delivery by UAV in the logistics field is becoming a fast and efficient dispatch method and application hotspot. For forward and reverse logistics data, drone dispatch is an important means for large-scale logistics enterprises at home and abroad to implement efficient logistics delivery. A path dynamic programming model (KMG) that integrates the scalable K-Means++ algorithm and genetic algorithm to implement a UAV scheduling strategy including reverse logistics. The KMG model integrates the reverse logistics path into the forward logistics path, using weighted clustering. The algorithm determines the minimum number of dispatched drones required for different attribute packages. For each connected graph of coordinate data, the genetic algorithm is used to solve the TSP problem and the feasible solution is coded, and finally the minimum Euler loop is solved. The simulation results show that the cost of the KMG model is 20.08% lower than that of the independent reverse logistics. The time of using the scalable K-Means++ clustering calculation is 298.85% shorter than the traditional K-Means algorithm.
Based on a continuous multinomial logit model, this paper develops a stochastic urban system equilibrium to investigate residents' choices of residential location and work place in a linear polycentric city, which takes into account individual taste heterogeneity in terms of location choice. In the urban system, people commute by private car, bus or rail transit between their home and working places. The model is then used to explore the impacts of urban rail transit investment on residential distribution, housing price, and attractiveness of CBDs for employees through comparison of the urban system equilibrium performances before and after the introduction of rail transit. The result reveals that the introduction of urban rail transit makes employment more concentrated in the relatively developed CBD and further widens the gap of attractiveness between the two CBDs. It also finds that improvements of traffic conditions encourage residents to migrate towards the suburbs, and thus leads to a more uniform distribution of residential density and housing price across the city, weakening the advantages of central areas.
The steelmaking-continuous casting process is a complex process. The molten steel needs to select the ladle as a storage device that meets the technological requirements and carry out according to the production plan. When the ladle is matched, the properties of the ladle are numerous, and it is difficult for the matching targets to meet each other at the same time. The steel with many properties lead that the ladle selection constraints are complicate. The optimization of the matching is a multi-conflict target optimization problem. First of all, using the first order inductive learner method to extract the key attributes as the performance indicators, including the highest temperature of the ladle, the highest number of ladle used, the lowest grade of the ladle and the number of ladle outlet, the ladle temperature, the number of ladle, ladle material, the number of skateboarding and frames to be constraints, and establish the ladle matching model. Then, the least general generalization method is used to extract the rules of ladle selection, and the simulation experiments are carried out for many scheduling order strategies and matching priority combinations. Finally, a heuristic ladle matching method based on rule priority was given, and carried out industrial applications. The results show that the accuracy of ladle selection is improved, the number of using ladle and the number of maintenance ladle are reduced. The production efficiency of heat is improved.
The counter-terrorism decision-making of the members of the international anti-terrorism coalition is faced with externally complicated random factors such as political competition, religious beliefs and national structure, which brings about high uncertainty for the international counter-terrorism situation. This paper constructs a stochastic evolution model based on the Moran process for the international anti-terrorism situation in a highly uncertain environment. Then, the equilibrium results of the counter-terrorism strategies under different random disturbance intensities are calculated under the three scenarios of stochastic factor dominance, expected payoff dominance and super-expected payoff dominance. Studies have shown that the governments adopt "preemptive strike" or "passive response" strategy, which mainly depend on three factors:1) Irrational external random factors; 2) The scale of the anti-terrorist coalition; 3) Cost-benefit of different counter-terrorism strategies. If the irrational random factors dominate the decision-making, the cost-benefit of the two strategies and the numbers determine the strategic choice of the member states. If the expected payoff dominates the decision-making, the "preemptive strike" strategy will become the general consensus of all member states only when the number of members of the anti-terrorist coalition is greater than a numerical value. Otherwise, the "passive response" anti-terrorist free-riding behavior will become stable strategy.
The medical dispute has been the important contradiction restricting the development of harmonious society, while government regulation will play an important role in strategy choice of both sides when resolving disputes. This article analyzes the influence of the degrees of governmental regulation to doctor-patient strategies by building the doctor-patient evolution game model. The study results indicate that different degrees of regulation result in different strategy profile, different regulation efforts to doctor and patient will determine the operation status and behavior choice separately. The government's improvement of the regulation standard to hospitals and patients will gradually raise the probability of hospitals to operate specifically, regulate patients' routes and ways to safeguard legal rights, and finally normalized the behaviors of both sides. The behaviors of "medical disturbance" are somehow a way to expose the operation status of hospitals; different self-positioning of the government in medical disputes determines different strategies, which resulted in a relatively big difference in the results of the medical disputes. Finally, this article discusses the evolution results under the circumstances of insufficient governmental regulation and improper regulation strategies combing with the Putian Medical Group and Medical Dispute in Nanping, Fujian.
Drug-proportion regulation is on implementing following the price regulation and the elimination of markups on pharmaceuticals to curb the over-treatment, which is an important issue for Chinese healthcare reform. Based on that, we propose an evolutionary game model of doctor-patient behavior under drug-proportion regulation. Theoretically, it founds that there exists behavioral evolutionary law and stable strategies between physicians and patients according to replicator dynamics equation. Then treatment strategies are analyzed by considering normal treatment costs, the ratio of over-treatment to normal treatment costs, physician performance coefficient, and the severity of illness. The results show that drug-proportion regulation does not always inhibit over-treatment, which depends on a transition point. Physicians prefer to choose overtreatment while the drug-proportion is lower than the transition point. It is worth noting that the severity of illness affects over-treatment under the drug-proportion regulation. Physicians are prefer to over-treatment when patients are less ill. These conclusions are beneficial for drug-proportion setting, light-illness monitoring, hierarchical diagnosis, and information disclosure mechanism. Finally, related healthcare policy suggestions are provided.
Performance allocation is a hotspot and difficult point in performance management research. This paper fully considers the impact of individual differences on performance allocation satisfaction. To this end, the score conversion function is used to convert the initial scores of the basic workload of the assessment objects, and then the K-means clustering method is used to classify the assessment objects; Secondly, it discusses the optimal solution of the most favorable and unfavorable basic workload for the assessment objects, and obtains the most favorable and unfavorable performance allocation ratio for the assessment objects. The satisfaction functions of the assessment objects are constructed. On this basis, we establish the multi-objective optimization model of optimal performance allocation and obtain the Pareto optimization solution. Finally, the numerical experiment of the performance allocation of a university teacher is carried out by using the model, the results show that the multi-objective performance allocation model constructed in this paper has a good effect on improving the satisfaction of performance allocation.
The topological structure of geostationary earth orbit (GEO) communication satellite constellation network is usually very complex and it is often difficult to define system state accurately. In this paper, we first analyze the topological relationships of a GEO constellation system containing three working satellites and one warm backup satellite, and then define and aggregate the system operating states by means of stochastic process. Based on Kolmogorov theorem, differential equations of aggregated state residence probabilities are built. In addition, with Laplace transform and its inverse transform, reliability indexes of the constellation network are deduced. Furthermore, probability sensitivity of the system's visiting various state sets containing safe state subset, deteriorating state subset, alert state subset and failed state subset is studied with failure rate change of the backup satellite, which can provide quantitative support for improving the constellation network reliability and adopting a backup strategy. In the case study part, we study a specific GEO constellation system of a commercial company and compare the effect of different varied failure rates of the backup satellite, which supports the theoretical conclusion in the previous part.
Considering the complexity and heterogeneity of weapon nodes in the weapon system of systems, and the diversity of the relationship between equipments, this paper first proposes a modeling method based on heterogeneous networks. Next, by referring to the concept of observe-orient-decide-act (OODA) combat cycle theory and combining the definition of meta-path in heterogeneous networks, the number of attack link is used to evaluate the structural robustness of the weapon system of systems. Then, we do the experiments on structural robustness of weapon system-of-systems under random attack and selective attack strategy using the traditional natural connectivity index and the number of attack link. It is found that the number of attack link with actual semantic information is more effective to evaluate the structural robustness of weapon system of systems. Finally, the invulnerability of the weapon system of systems under certain background is analyzed, which provides assistant decision-making for the attacker's primary attack target and the defender's primary protection object.
In this paper, a safety analysis method based on system theoretic process analysis (STPA) is proposed for the submarine torpedo launch control system. One typical action of torpedo launching process, releasing the weapon brake is taken as an example for analysis with XSTAMPP safety engineering platform. Traditional STPA causal scenario analysis model is improved; refined system safety requirements are generated and the descriptions are standardized by linear temporal logic (LTL), the limitations of natural language descriptions used by traditional STPA analysis have been avoided, which provides theoretical support for further safety model verification and decision-making assistant for system operators.
Aiming at the maintenance decision-making issues on "when to repair" and "when to replace" for civil aircraft composite structure in the practice of maintenance, a repair tolerance determination is proposed using Monte Carlo simulation method, in which a few factors of the damage forming sequence, occurrence time, size, detection probability, overload distribution, maintenance policy, and strength degradation and recovering are taken into account and failure probability is regarded as safety constraint condition and maintenance cost is treated as economy optimization objective. A case result shows that the repair tolerance and inspection interval due to the approach can make decisions of the maintenance activities more economically and control the safety and reliability level of aircraft composite structure actively. It should provide a practical significance technique about composite structure safety and economy analysis and damage maintenance threshold decision-making in the life cycles for airline operators.
Aiming at the problem of low accuracy of outlier recognition for spatio-temporal data, a framework was constructed according to fusion thought of time dimension and space dimension. Based on the framework, the difference of attribute data between different positions was derived by Archimedean copulas function under the condition of unknown distribution. A method for converting spatial data was established with high value as a core to build rank series. Then, the expectation and variance were determined for hypothesis test. Finally, with the model parameters of window size and scope radius, an approach of outlier recognition for spatio-temporal data was given based-on M-K test. The calculation example and application analysis show that this approach can improve the accuracy of outlier recognition for spatio-temporal data, and has more recognition capability.
Considering the rational decision makers usually make the best choice to obtain high cost-performance results, further analysis is conducted based on the Pareto non-inferior solutions that are derived by solving a tri-objective optimization problem. Based on the concept of cost-performance ratio, the basic rule of ordering the Pareto front points is established, the concept of the adjacent points is defined, and the method for the selection of adjacent points is clarified. Based on the distribution feature of the Pareto front points and their adjacent points, the Pareto front change rate is calculated. The concept of sensitivity ratio is defined, and the bias degree of each Pareto non-inferior solution corresponding to each objective is calculated. The innovations are as follows:(i) a new dominance relationship that is formed by the Pareto front sensitivity ratio of the tri-objective optimization problem is used to obtain a subset that has a smaller range than the Pareto non-inferior solution set; (ii) the bias degree that corresponds to each Pareto non-inferior solution for each objective is quantified for the tri-objective optimization problems for the first time; (iii) the unbalance degree, which corresponds to each Pareto non-inferior solution for each objective, is also derived. Finally, the above calculation process is demonstrated by calculating numerical examples, and the results are compared with that obtained by other common methods. The results illustrate that the proposed method in this paper is feasible and valid. This research is a significant theoretical advancement for understanding the important features of Pareto non-inferior solutions and for solving tri-objective optimization problems.
Based on the criterion of the static maximum grey incidence degree, the classic time-delay analysis method strongly depends on time series samples. This affects the representativeness of the delay values, and even causes contradictory results. To solve these problems, this study develops a new dynamic time-delay analysis method. First, a new generalized grey incidence model is constructed by introducing a novel new information priority weighting operator. Then, to overcome the disadvantages of the widely used classic grey incidence-based time delay analysis method, a new concept of dynamic grey incidence window is proposed, which helps to extract all possible time delays between sequences. Based on this new model and the dynamic grey incidence window concept, the time-delay incidence matrix and related time-delay incidence vectors are obtained. A novel grey stack matrix is designed to realize the effective extraction of the representative value from all potential time delays between sequences. Finally, a case study of the time delays between some important macroeconomic indicator sequences is carried out. Compared with the classic method, result shows that the proposed time delay analysis method can provide more reliable representative time delay values.
This paper proposes to process the original data in a reverse-accumulation manner, and generalizes it to the fractional domain on an integer-order basis, based on fractional-order inverse-accumulation-generating operators and fractional-order inverse-reduction-generating operators. A fractional-order inverse cumulative Verhulst model was established, and the application examples were compared with fractional order inverse cumulative GM(1,1) model to test the model simulation error. The correlation results showed that compared with the traditional Verhulst model and fractional order inverse cumulative GM(1,1) model, the accuracy of the data fitting of the fractional-order inverse cumulative Verhulst model is high.