Based on the spillover index proposed by Diebold and Yilmaz (2012), this paper studies the impact of China, US and global economic policy uncertainty on the core areas of China's macro finance from June 2006 to October 2018. This paper analyzes the static spillover effects and its dynamic changes of economic policy uncertainty on China's stock market, exchange rate and commodities, and further explores the spatial heterogeneity of spillover effects. The main conclusions of this paper are that: 1) China's economic policy uncertainty has the greatest impact on China's macro financial core areas. Stabilizing the stock market and preventing the malicious short-selling of the Chinese stock market is an effective means to avoid the systemic financial risks brought about by the macro financial effect. 2) The uncertainty of economic policy has a significant time-varying feature for the net spillover of China's macro financial market. And it has a strong correlation with specific events. 3) The spatial heterogeneity of macro financial effects of economic policy uncertainty in different countries or regions is mainly reflected in the two aspects-Intensity and synchronization of spillover.
Employing a weekly sample of Chinese A shares from 2002 to 2017, this paper first computes the sector volatility index for 76 sub-sectors. Based on these indices, a global risk network among sectors is then constructed through a high-dimensional time-varying parameters vector autoregressions (HD-TVP-VAR) model. Afterwards, this paper further examines the systemic importance of financial industries in the risk network from both network topology and empirical analysis perspectives. Results show that, risk spillovers among sectors in Chinese stock market exhibit a complex network structure featured with high connectedness. Specifically, the outbreak of risk events is closely related to the changes in spillover connectedness from financial industries to real economy industries. Second, financial industries play an important role of absorbing risk, with banking being the major risk taker, while the security and future sector being the major risk spreader. Third, the increase in connectedness, transferring from risk-taker to risk-spreader, and the destruction to the stability of network equilibrium are main driving forces underlying systemic financial risk. Forth, financial industries have a greater systematic importance than real economy industries, within which, banking ranks on the top, followed by the security and future sectors. Fifth, real estate sector serves as a risk "bridge" between financial industries and real economy industries which magnifies risk from both sides. Last, high leverage and high liquidity will stimulate risk diffusion, but large scale and heavy investment will help to strengthen risk absorption.
This paper uses the GED-based ARMA-(T)GARCH-VaR model and the test for Granger causality in risk to investigate the downside and upside extreme risk spillover effects of nine sub-markets of China's pan-financial market in five periods, and describes the evolution of extreme risk spillovers between submarkets through directed weighted complex networks. The empirical results show that the extreme risk spillover effects between the sub-markets have time lags. In the risk spillover network, the more extreme the risk is, the stronger its ability to spread is and the higher its efficiency is. The downside and upside extreme risk spillover effects are asymmetric, and the spreading ability of upside risks is higher than that of downside risks over the most of periods. The central nodes of the extreme risk spillover network change continuously over the five periods, but the energy submarket is always the main recipient of upside extreme risk spillovers. After the financial crisis, the ability of commodity and bond submarkets to spread extreme risks spillover in the network is enhanced, and there is a significant extreme risk spillover effect between them.
Based on the new economic geography analysis framework, the theoretical model is established, and this study that constructs the dynamic agglomeration indicator integrating the dynamic process of firms' entry-exit, deeply investigated the impact of dynamic industrial agglomeration on the productivity coordination of cities in China. The study shows that: 1) From 1998 to 2007, there are more firms' inflows in southeast coastal cities of China, and there is large regional difference in the agglomeration index adjusted by the dynamic process of firms, which is featured by "east higher and west lower". 2) The productivity among the cities shows the characteristics of convergence, especially after the year 2003, the effect is more obvious, while dynamic industrial agglomeration reduces the speed of process. 3) Scale efficiency contributes more to productivity, while there is differentiation trend and the dynamic agglomeration improves the divergence level of scale efficiency. 4) In terms of the urban agglomerations, there is greater synergy which shows obvious location difference. The productivity distribution is more balanced, and productivity coordination is relatively more significant within the Yangtze River Delta and Pearl River Delta urban agglomerations, while the dynamic agglomeration enhances the trend. The policy implication shows that the scale advantage of agglomeration should be further enlarged, while the potential to enhance productivity for technological progress should be thorooughly released. The research also suggests that the system structure of urban agglomerations should be optimized, in order to achieve the balance and full development of urban productivity.
Tenure choice has been a key factor influencing the development of housing rental market. This article aims to analyze households' housing tenure choice under the increasing population aging in China. It theoretically and empirically explores the life-cycle effects of housing tenure choice and the moderating role of housing affordability. First, how household tenure choice influenced by housing preferences and housing affordability is explored theoretically. Second, this study empirically analyzes the effects of household-heads age, house prices and income on the possibility of buying houses by panel logit and probit models, as well as the data from China Family Panel Studies (CFPS). Furthermore, the heterogeneous analysis for households are conducted respectively at income level and regional level. The results suggest that the possibility of purchasing houses for households in China changes with the age of the households' heads by inverted U-shape. House prices and household-heads age respectively have a positive and negative moderating effect on the relationship between household-heads age and possibility of buying houses. Furthermore, the effects are more significant for households with low income and in east areas. This study can provide theoretical supports for the government to guide the development of housing rental market and the promotion of housing reform, effectively addressing the housing issue under the background of fast population aging in China.
This article uses 2007-2017 data on Chinese A-share listed companies to explore whether stock-rich information can affect analyst forecast quality. We obtain the following results: 1) The information content of stock prices can effectively improve the quality of analyst predictions. With the increase of the information content of stock prices, the analyst forecast bias, forecast optimism, and forecast divergence have all significantly reduced, and the number of analysts' tracking has increased significantly; 2) The lower the company's shareholding, the stronger the accounting robustness, and the worse the transparency of information, the greater the information's impact on the quality of analyst forecasts. The research in this paper has certain theoretical and practical significance for comprehensively understanding the information content of stock prices, promoting the role of analyst in the information market of capital markets, and the healthy development of capital markets.
Based on the perspective of information shocks, this paper studies the effect of objective heterogeneous information and subjective information beliefs on the liquidity and efficiency of financial markets by a noise rational expectations model. The results show that: The improvement of the degree of public information has two-sided effects on market liquidity, but positive effects on market efficiency; the improvement of the expectation precision has positive effects on both market liquidity and market efficiency. In addition, when both the degree of public information and the expectation precision are small, the occurring probability and extent of market inefficiency (or excess volatility) will rise dramatically.
Recently, the predictability of intraday return is a hot topic in academic. Our paper explores the intraday return predictability of the Chinese stock market based on intraday jumps and momentum. The main findings are as follows. First, we use the LM jump test to obtain intraday jumps, which is used to predict intraday return, and then find it is useful. In detail, the prediction effect of the first and seventh half-hour to the last half-hour has significantly improved from a statistical view. Second, from an economic view, we find the intraday jumps can gain more economic values and own lower risk. Moreover, we find that jumps can obtain higher predictability during non-crisis, high volatility and middle volume.
Model-free implied variance has the characteristic that is not based on option pricing models. The difference between it and the realized variance measures the variance risk premium. In recent years, it has attracted much attention in the study of the variance risk premium. In this paper, we use soybean meal futures options and sugar futures options data in China's market to compute the corresponding model-free implied variance, and then investigate the variance risk premiums under the framework of a variance swap. The results show that the variance risk premiums of soybean meal futures and sugar futures in China's market are significantly negative and time-varying. Classic risk factors can only account for a small portion of the variance risk premiums for these two assets. A non-linear relationship between the variance risk premium and the returns, and the predictive power of the variance risk premium to the future returns of soybean meal futures and sugar futures are demonstrated. Moreover, we find there is quite a difference between the variance risk premiums of these two assets and of the Shanghai Stock Exchange 50 ETF (SSE 50 ETF). The variance risk premium for SSE 50 ETF is nonsignificant, and it has no predictive power for its future returns.
Considering that the correlation between financial assets has time-varying and long memory, although the MIDAS Copula model incorporating mixed data sampling can characterize time-varying and long memory, its parameter evolution process is relatively simple. Therefore, the generalized autoregressive score (GAS) model is introduced into the MIDAS Copula model as the parameter evolution process, to construct the GAS MIDAS Copula model. The empirical analysis found that the model has improved the ability of the MIDAS Copula model to fit samples; Further select choose three sets of CSI 300 industry indexes with different degrees of correlation, and analyze the model's ability to capture long memory of time-varying correlation coefficients between industries with different degrees of correlation and the risk prediction accuracy of its portfolio. The results showed that: 1) The GAS MIDAS Copula model has the best ability to describe the long memory of the correlation coefficients between highly and moderately related industries; 2) The VaR and ES backtesting results of simple portfolio of three sets of data show that the GAS MIDAS Copula model has the highest prediction accuracy. Finally, various risk prediction results based on different confidence levels, different weight ratios, different rolling window lengths, and different assets confirm the robustness of the GAS MIDAS Copula model.
The rapid development of China's financial market in recent years has brought not only convenience but also challenges to investors. How to effectively allocate assets is one of the problems that investors need to solve. The Black-Litterman model not only solves the traditional mean variance model parameter-sensitive issues, but also allows investors to add investment perspectives to the model. It is a closely watched asset allocation model. However, investors may not be able to give a suitable investment perspective because of their inexperience, they cannot play the application value of the model. This article uses a long-term short-term memory (LSTM) neural network to express quantitative views to solve this problem. As a numerical example, we use ShenWan first-level industry index as an asset pool to build a portfolio, the result of the example shows that the asset allocation model constructed in this paper has a higher Sharpe ratio and annualized rate of return than other reference models.
Prior literature has studied extensively on store brand entry under different power structures between the national-brand manufacturer and the retailer. This paper considers the characteristic and the relationship between two alternative Stackelberg games (manufacturer-led and retailer-led) with store brand entry under an asymmetric demand scenario where the retailer possesses the private demand information. The results show that the impacts of the store brand on the players' profits under each Stackelberg games are different. In addition, when the information is asymmetric, the first-mover advantage for the firms may disappear and they can obtain more profits when acting as the follower. Finally, the players' preference for informational acquisition capability and the information sharing between firms also show some significant differences between different power structures.
Based on the B2C e-retailer's retail and online marketplace platform attributes, this paper builds four supply chain operation modes for new and old products from the perspective of a manufacturer, namely, online reselling mode for new and old products, online hybrid sales mode I (online agency selling mode for new product, online reselling mode for old product), online hybrid sales mode II (online reselling mode for new product, online agency selling mode for old product) and online agency selling mode for new and old products, simultaneously, establishes the demand model of the new and old products considering the heterogeneities of consumer's preferences to the new and old products, analyzes the supply chain members' optimal pricing under four types of sales channel structure, and provides the decision basis for the channel selection of the manufacturer's new and old products. Research results are as follows: When the transaction fee ratio charged by B2C marketplace platform is small, the manufacturer should choose the online agency selling mode to sell new and old products no matter how much the customer acceptance of old product is. Otherwise, the manufacturer should choose the online reselling mode to sell new and old products. In addition, when the transaction fee ratio charged by B2C marketplace platform and the customer acceptance of old products are simultaneously large or small, the online hybrid sales mode I is superior to the online hybrid sales mode II. When the value of the transaction fee ratio charged by B2C marketplace platform is opposite to the customer acceptance of old product, the online hybrid sales mode II is superior to the online hybrid sales mode I.
Under the environment of sustainable development in China, more renewable energy is applied in the power market, but there is still a serious phenomenon of abandoning light and wind at present. How to improve the utilization rate of renewable energy is a problem worth studying. In this paper, renewable energy power suppliers in microgrid and thermal power suppliers in bulk power grid were taken as the research objects. By establishing different game models, the optimal profit of Stackelberg game with traditional mode and cooperative game based blockchain was compared and analyzed. Finally, numerical simulation was carried out with MATLAB. The research showed that the decentralized feature of blockchain can effectively combine the microgrid nodes which scattered in different geographical locations into a whole. Then, the information interaction between microgrid and state grid was conducted through the blockchain information platform, which promoted the cooperation and mutual assistance between power suppliers. The openness, transparency, authenticity and reliability of the data on the blockchain reduced the decision-making and trading cost of each power supplier, which made more reasonable use of renewable energy, reduced carbon emissions and effectively promoted the sustainable development of the power supply side.
In order to improve the monitoring performance of quality variations for processes integrating statistical process control and automatic process control, a process quality monitoring model is established and economic-statistical optimization design scheme of this model is constructed. Using NSGA-III algorithm, non-inferior solution sets of different quality variations for three kinds ARMA noise disturbance model is calculated in a numerical example. Then sensitivity analysis of key parameters of the numerical example is given. Finally, the optimization design method is compared with several existing SPC/APC integrated monitoring schemes. The results show that the economic statistical optimization design method proposed in this paper is significantly superior to the statistical and economic optimization design methods.
To improve the work efficiency of underwater detection by AUV, a global path planning method is proposed for AUV in variable currents. Firstly, the terrain and static obstacles are identified, and the multi-objective function is established, which is constrained by the shortest and maximum smoothness of the path in the 3D space. The improved QPSO algorithm is used to solve the problem and generate the initial path. Secondly, considering the existence of uncertain obstacles in underwater environment and disturbance of time-varying currents, the dynamic obstacle information is updated on the known map, and the velocity vector of ocean current is estimated by using Gaussian noise, so that ensure that the AUV adapts to the change of the ocean current to output the appropriate speed. Finally, the observation and penalty function are established to adjust the initial path in real-time and get a more scientific and reasonable path. The simulation results show that the path planning method can make the AUV stealthy more stable and autonomous. Comparing with the conventional algorithm, the proposed algorithm is better than other algorithm in the accuracy and quality of the solution.
The air traffic control (ATC) operational characteristics under convective weather were analyzed. The features of the impacted terminal control area were quantified from the perspectives of airspace, traffic and meteorology. The information-gain based feature selection was carried out, and the terminal hourly take-off and landing capacity prediction model based on random forest was established. Taking the Guangzhou terminal area as an example, the 10-fold cross-validation and bootstrapping model evaluation methods were used to measure the model performance as well as mean square error (MSE) and determination coefficient (R2) metrics. Compared with the capacity prediction method based on the MAXFLOW-MINCUT analytical model, the mean absolute error (MAE) of the hourly prediction was reduced by 83% and the predicted variance was reduced by 80%.
Materialism holds that objective reality does not shift by human consciousness. Therefore, for the same practical problem, different evaluation methods should be compatible and complementary, and their evaluation results tend to be basically consistent. Extension sets and variable sets are taken as examples to study the theoretical compatibility between the two from three aspects, i.e. problem context, research methods and philosophical implication. Then, on the basis of analyzing the numerical characteristics and relationship of extension correlation function and variable opposite difference function, a coupling evaluation model based on extension matter element theory and opposite difference function was constructed. A comparative study on the application of water resources carrying capacity in 13 cities in Heilongjiang Province shows that the evaluation results of the model established in this paper are basically consistent with those of the extenics method, and are consistent with the actual situation, thus verifying the compatibility of variable sets and extension sets at the application level. The model introduces the opposite difference function into the extension evaluation method, which can not only determine the level of water resources carrying capacity, but also reflect its strength relative to adjacent levels, which provides support for further revealing the characteristics of the studied system and enriching its expression forms.
A bipolar capacities multi-attribute decision making VIKOR method is proposed to solve the problem of multiple attribute decision making with interaction attributes. Firstly, the relative distance difference matrix and prospect decision matrix of alternatives relative to positive and negative ideal solution are constructed according to prospect theory. Secondly, based on bipolar capacities Choquet operator, the comprehensive evaluation value of alternatives can be calculated, and the ranking results of alternatives can also be obtained according to the original VIKOR judgment rules. Finally, the feasibility and validity of the proposed method are verified by a case study. The results show that the proposed method can not only effectively distinguish the differences among different alternatives considering the decision maker's psychology, but also can describe more attribute interaction scenarios. Therefore, the proposed method can provide a new approach for decision-making problems with interaction attributes.
An expert integrated discussion method based on expert group classification consensus information integration is proposed to solve multi-attribute group decision problem under hesitant fuzzy linguistic information. The integration process of expert decision information is divided into two stages: In the first stage, the expert linguistic information is transformed into hesitant fuzzy linguistic terminology through text free grammar and transformation function. The expert group is classified based on the decision information matrix, and then the consensus model is used to make the consensus within the class reach consensus. In-class expert decision information is transformed into probabilistic language combination to realize expert decision information integration within the class. In the second stage, the expert class weights are comprehensively calculated through the expert group size and the degree of deviation of decision information between classes, and the decision information integration results in the class are further weighted and integrated to realize the integration of decision information classes. According to the result of decision information integration, the decision objects are ranked by superiority and inferiority, and the decision-making department can select the optimal decision-making scheme by referring to the sorting result. Finally, the effectiveness of the proposed method is verified by an example.