We employ the static and dynamic copula models to investigate whether technical indicators provide information on volatility in the next trading day, where the volatility is measured by daily realized volatility. Our empirical results, based on long samples of 8 well-known stock indexes, suggest that a significant and asymmetric tail dependence between the technical indicators based on moving average and the next day volatility. The level of dependence change over time in a persistent manner. And the dependence structure presents some distinct differences between emerging market indexes and developed market indexes. These results indicate that the technical indicators can provide information on the next day volatility at extremes, and are less informative at normal market.
Our article discusses a class of Jump-diffusion stochastic differential system under Markovian switching (JD-SDS-MS). This model is generated by introducing Poisson process and Markovian switching based on a normal stochastic differential equation. Our work dedicates to analytical properties of solutions to this model. First, we give some properties of the solution, including existence, uniqueness, non-negative and global nature. Next, boundedness of first moment of the solution to this model is considered. Third, properties about coefficients of JD-SDS-MS is proved by using a right continuous markov chain. Last, we study the convergence of Euler-Maruyama numerical solutions and apply it to pricing bonds.
Reduction of carbon dioxide (CO2) emissions is one of the biggest challenges for global sustainable development, in which economic growth characterized by industrialization plays a formidable role. We innovatively adopted the input and output (I-O) table of 41 countries released by World I-O Database to determine the industrial structure change and analyze its impact on CO2 emission evolution by developing a cross-country panel model. The empirical results show that industrial structure change has a significantly negative effect on CO2 emissions; to be specific, 0.1 unit increase in the linkage of manufacturing sector and service sector will lead to a decrease of 0.94 metric tons per capita CO2 emissions, indicating that upgrading industrial structure contributes to carbon mitigation and sustainable development. Further, urbanization, technology and trade openness have significantly negative impact on CO2 emissions, while economy growth and energy use take positive impacts. In particular, a 1% increase in per capita income will contribute to an increase of 8.6 metric tons per capita CO2 emissions. However, the effect of industrial structure on environment degradation is moderated by technology level. These findings fill the gaps of previous literature and provide valuable references for effective policies to mitigate CO2 emissions and achieve sustainable development.
In this paper, we propose and analyze a cooperation model with harvesting and state-dependent delay, which is assumed to be an increasing function of the population density with lower and upper bound. The main purpose of this article is to obtain the dynamics of our model analytically by controlling the harvesting. We present results on positivity and boundedness of all populations. Criteria for the existence of all equilibria and uniqueness of a positive equilibrium are given by controlling the harvesting. Finally, the global exponentially asymptotical stability criteria of model is obtained by the improved Hanalay inequality.
According to the philosophy of self-cultivation that “one should refine his personal virtue when in poverty, and help save the world when in success”, a new type of evolutionary strategy, Poor-Competition-Rich-Cooperation (PCRC), is proposed. To discuss its superiority and inferiority, based on a multi-player iterated Prisoner’s Dilemma game, PCRC and other six kinds of strategies are played by using the roulette method in three different populations (a uniformly distributed population, a cooperation-preference population, a defection-preference population). The payoff characteristics for each strategy under different temptation coefficients and noise values are also analyzed. Simulation results indicate that PCRC has a sufficient robustness and its payoff presents a basically monotonic increasing tendency with the increment of noise. The superiority of PCRC becomes more obvious when the temptation coefficient becomes larger. Furthermore, a higher population preference for defection yields a more obvious advantage for PCRC.
This paper seeks to position the third-party transaction (TPT) in the macrostructure of Chinese market in terms of Concentration Ratio and Herfindahl-Hirschman Index. By extending both Cournot oligopolistic and Stackelberg oligopolistic competition models, a new oligopolistic competition model is established for China’s TPT market. Based on multidimensional game, a pricing game model is theorized accordingly to elucidate the TPT platform. The influence of various factors on the price of the TPT platform is verified with numerical simulation. The formation of monopoly is an inevitable consequence of the “one superpower and multi powers” market structure given the unregulated development of TPT enterprises as the prerequisite. In addition, through the studies on telecom payment market, such unregulated development can also cause the “big-three” domination for the TPT enterprises in the “multi powers” market structure. Based on our modelizations, this paper serves to provide prospect recommendations for policy-making of state supervision authorities.
Persona is a common human-computer interaction technique for increasing stakeholders’ understanding of audiences, customers, or users. Applied in many domains, such as e-commerce, health, marketing, software development, and system design, personas have remained relatively unchanged for several decades. However, with the increasing popularity of digital user data and data science algorithms, there are new opportunities to progressively shift personas from general representations of user segments to precise interactive tools for decision-making. In this vision, the persona profile functions as an interface to a fully functional analytics system. With this research, we conceptually investigate how data-driven personas can be leveraged as analytics tools for understanding users. We present a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes. We apply this framework for an analysis of digital marketing use cases to demonstrate how data-driven personas can be leveraged in practical situations. We then present a functional overview of an actual data-driven persona system that relies on the concept of data aggregation in which the fundamental question defines the unit of analysis for decision-making. The system provides several functionalities for stakeholders within organizations to address this question.
With the rapid growth of the smartphone and tablet market, mobile application (App) industry that provides a variety of functional devices is also growing at a striking speed. Product life cycle (PLC) theory, which has a long history, has been applied to a great number of industries and products and is widely used in the management domain. In this study, we apply classical PLC theory to mobile Apps on Apple smartphone and tablet devices (Apple App Store). Instead of trying to utilize often-unavailable sales or download volume data, we use open-access App daily download rankings as an indicator to characterize the normalized dynamic market popularity of an App. We also use this ranking information to generate an App life cycle model. By using this model, we compare paid and free Apps from 20 different categories. Our results show that Apps across various categories have different kinds of life cycles and exhibit various unique and unpredictable characteristics. Furthermore, as large-scale heterogeneous data (e.g., user App ratings, App hardware/software requirements, or App version updates) become available and are attached to each target App, an important contribution of this paper is that we perform in-depth studies to explore how such data correlate and affect the App life cycle. Using different regression techniques (i.e., logistic, ordinary least squares, and partial least squares), we built different models to investigate these relationships. The results indicate that some explicit and latent independent variables are more important than others for the characterization of App life cycle. In addition, we find that life cycle analysis for different App categories requires different tailored regression models, confirming that inner-category App life cycles are more predictable and comparable than App life cycles across different categories.