scholarly journals Tsallis Entropy for Cross-Shareholding Network Configurations

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 676
Author(s):  
Roy Cerqueti ◽  
Giulia Rotundo ◽  
Marcel Ausloos
Keyword(s):  
Joint Distribution ◽  
Industrial Structure ◽  
Power Laws ◽  
Tsallis Entropy ◽  
Dependence Structure ◽  
External Shocks ◽  
Stochastic Dependence ◽  
Entropy Parameter ◽  
Crucial Information ◽  
Empirical Experiment

In this work, we develop the Tsallis entropy approach for examining the cross-shareholding network of companies traded on the Italian stock market. In such a network, the nodes represent the companies, and the links represent the ownership. Within this context, we introduce the out-degree of the nodes—which represents the diversification—and the in-degree of them—capturing the integration. Diversification and integration allow a clear description of the industrial structure that were formed by the considered companies. The stochastic dependence of diversification and integration is modeled through copulas. We argue that copulas are well suited for modelling the joint distribution. The analysis of the stochastic dependence between integration and diversification by means of the Tsallis entropy gives a crucial information on the reaction of the market structure to the external shocks—on the basis of some relevant cases of dependence between the considered variables. In this respect, the considered entropy framework provides insights on the relationship between in-degree and out-degree dependence structure and market polarisation or fairness. Moreover, the interpretation of the results in the light of the Tsallis entropy parameter gives relevant suggestions for policymakers who aim at shaping the industrial context for having high polarisation or fair joint distribution of diversification and integration. Furthermore, a discussion of possible parametrisations of the in-degree and out-degree marginal distribution—by means of power laws or exponential functions— is also carried out. An empirical experiment on a large dataset of Italian companies validates the theoretical framework.

ON THE STOCHASTIC DEPENDENCE STRUCTURE OF THE LIMIT LOGNORMAL PROCESS

2011 ◽  
Vol 23 (02) ◽  
pp. 127-154 ◽  
Author(s):  
DMITRY OSTROVSKY
Keyword(s):  
Arbitrary Number ◽  
Joint Distribution ◽  
Mellin Transform ◽  
General Rule ◽  
Dependence Structure ◽  
Selberg Integral ◽  
Stochastic Dependence ◽  
Limit Process ◽  
Kac Equation ◽  
Expansion Coefficients

The distribution of a single increment of the limit lognormal process of Mandelbrot, several representations of its Mellin transform, and an explicit analytic continuation of the Selberg integral are reviewed. The intermittency invariance of the limit lognormal construction is used to establish a functional Feynman–Kac equation that captures the entire stochastic dependence structure of the limit process. This equation is a general rule of intermittency differentiation that quantifies how the joint distribution of an arbitrary number of increments of the limit process evolves as a function of intermittency. The solution is represented by means of a formal intermittency expansion and shown to be an exactly renormalized expansion in the joint centered moments of the limit process. The expansion coefficients are related to a novel extension of the Selberg integral.

A NOTE ON THE CLASS OF GEOMETRIC COUNTING PROCESSES

2013 ◽  
Vol 27 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Ji Hwan Cha ◽  
Maxim Finkelstein
Keyword(s):  
Poisson Process ◽  
Probabilistic Analysis ◽  
Counting Process ◽  
Geometric Distribution ◽  
Survival Models ◽  
Dependence Structure ◽  
Counting Processes ◽  
External Shocks ◽  
New Class ◽  
Independent Increments

In this paper, we suggest a new class of counting processes, called the Class of Geometric Counting Processes (CGCP), where each member of the counting process in the class has increments described by the geometric distribution. Distinct from the Poisson process, they do not possess the property of independent increments, which usually complicates probabilistic analysis. The suggested CGCP is defined and the dependence structure shared by the members of the class is discussed. As examples of useful applications, we consider stochastic survival models under external shocks. We show that the corresponding survival probabilities under reasonable assumptions can be effectively described by the CGCP without specifying the dependence structure.

Copula-Based Bivariate Modelling of Significant Wave Height and Wave Period and the Effects of Climate Change on the Joint Distribution

2016 ◽  
Author(s):  
Erik Vanem
Keyword(s):  
Climate Change ◽  
Wave Height ◽  
Significant Wave Height ◽  
Joint Distribution ◽  
Wave Period ◽  
Dependence Structure ◽  
Zero Crossing ◽  
Conditional Model ◽  
Significant Wave ◽  
Modelling Techniques

The joint distribution of several met-ocean variables is required for risk assessment and load and response calculations in marine engineering. For example, a joint description is needed to construct environmental contours for probabilistic structural reliability analyses. Typically, the joint distribution of significant wave height and wave period is required as a minimum. This paper presents a study on various bivariate modelling techniques for the joint distribution of significant wave height and zero-crossing wave period, i.e. a conditional model, a bi-variate log-normal model and several meta-models based on parametric copulas. Each of the models is fitted to data generated from a numerical wave model for the current climate and for two future climates consistent with the RCP 4.5 and RCP 8.5 scenarios. Thus, the objective of this study is twofold. First, the joint models obtained by the various modelling techniques will be compared. Secondly, the potential effect of climate change on the simultaneous distribution of significant wave height and wave period will be explored. The results indicate that straightforward application of many of the most common families of copulas fails to capture the dependence structure in the data, and that the conditional model performs better than these naive approaches. However, if more advanced copula construction techniques are applied, significant improvements can be achieved. The results also suggest that significant wave height and zero-crossing wave period tend to be more correlated in a future climate, at least in the extremes.

scholarly journals The Effect of Innovative Entrepreneurial Vitality on Economic Resilience Based on a Spatial Perspective: Economic Policy Uncertainty as a Moderating Variable

2021 ◽  
Vol 13 (19) ◽  
pp. 10677
Author(s):  
Weilong Wang ◽  
Jianlong Wang ◽  
Shaersaikai Wulaer ◽  
Bing Chen ◽  
Xiaodong Yang
Keyword(s):  
Economic Policy ◽  
Evaluation System ◽  
Industrial Structure ◽  
Policy Uncertainty ◽  
External Shocks ◽  
Economic Resilience ◽  
Economic Policy Uncertainty ◽  
Economic Agglomeration ◽  
Indicator Evaluation ◽  
The Impact

This study measured the economic resilience of 269 prefecture-level cities in China by constructing an indicator evaluation system for the resilience, adjustment, and responsiveness of the economic system under external shocks. A dynamic spatial Durbin model and a moderating mediation model were employed to analyze empirically the impact of economic policy uncertainty and innovative entrepreneurial vitality on economic resilience using prefecture-level panel data from 2004 to 2018. The statistical results revealed that there were significant “snowball” effects and spatial spillover characteristics of economic resilience. Under the moderating effect of economic policy uncertainty, innovative entrepreneurial vitality was found to have a significant positive effect on economic resilience. Furthermore, innovative entrepreneurial vitality was found to enhance economic resilience significantly by upgrading the industrial structure, alleviating the income gap, and guiding economic agglomeration in the context of economic policy uncertainty. Moreover, the impacts of innovative entrepreneurial vitality and economic policy uncertainty on economic resilience, respectively, showed significant heterogeneities in terms of the cities’ regions and economic sizes. The above-mentioned results were found to be valid even after a series of robustness tests were carried out.

scholarly journals Co-Movement of Forex Rate and Share Price of Pakistan Stock Exchange - An Application of Copula Models

2020 ◽  
Vol V (III) ◽  
pp. 78-87
Author(s):  
Muhammad Nouman Latif ◽  
Nasir Ali ◽  
Anjum Shahzad
Keyword(s):  
Time Series ◽  
Joint Distribution ◽  
Stock Exchange ◽  
Tail Dependence ◽  
Share Price ◽  
Series Data ◽  
Dependence Structure ◽  
Complex Nature ◽  
Copula Models ◽  
The Relationship

This paper examines the relationship between the forex rate and the share price of the Pakistan Stock Exchange. The study provides additional understating of the complex nature of the relationship among bi-variate time series using the Copula model. Copula models are best suited to find the co-movement of time series data integrating the possible latent structure of the relationship through estimation of joint distribution with the help of marginal distribution of each time series variable. Alike from the traditional time series analysis, Copula models are best suited to estimate the complex relationship, specifically the tail dependence structure of joint distribution of the variables. Results of the study highlight a significant two-sided tail dependence structure between the Forex rate and share price of the Pakistan Stock Exchange.

Exploring the dependence structure among Chinese firms in the 5G industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Liukai Wang ◽  
Fu Jia ◽  
Lujie Chen ◽  
Qifa Xu ◽  
Xiao Lin
Keyword(s):  
Supply Chain ◽  
Industrial Structure ◽  
Risk Mitigation ◽  
Weak Link ◽  
Dependence Structure ◽  
Chinese Firms ◽  
Content Type ◽  
Industry Chain ◽  
Correlation Parameters ◽  
Different Levels

PurposeThis study aims to explore the dependence structure among Chinese firms across the emerging 5G industry at different stages and to provide some strategic insights for market participants.Design/methodology/approachThis study adopt macroeconomic fundamentals and the log-returns of 45 listed firms in the Chinese 5G industry to construct the weighted adjacency matrix by measuring the correlation parameters and then use the triangulated maximally filtered graph (TMFG) algorithm to construct the dependence network. It analyses the topological structure of the constructed networks to obtain the dependence characteristics for each firm in the whole industrial supply chain at different levels.FindingsThe empirical results provide a comprehensive and concise snapshot of the industrial structure, across the whole 5G industry at different levels, rather than just a “one-to-one” pattern. Specifically, the dependence characteristics of different firms are heterogeneous, and most firms are highly connected with partners in the whole industrial supply chain, whereas a few firms that are weakly connected. Those closely connected firms are usually in the midstream. In addition, compared with firms at different levels, downstream firms usually have closer dependencies and stronger influence capabilities.Practical implicationsRegulators not only should promote stability development for those firms most intensely connected with whole industry chain but also protect those firms with weak link in the whole industry chain. Investors should better understand the embedded ties among different firms to obtain effective market information and can select multiple firms with fewer connections as backup to conduct joint investment for risk mitigation. Mangers should give priority to the central players/firms in the whole industrial supply chain and establish the alliances with closely connected firms.Originality/valueThis study contributes to both the information system and operation management literature by constructing a new network method, Copula-TMFG, to capture the dependence structure among Chinese firms in 5G industry, empirically providing some strategic insights for 5G industry stakeholders, such as regulators, investors and managers.

Estimation of Environmental Contours Using a Block Resampling Method

2020 ◽  
Author(s):  
Ed B. L. Mackay ◽  
Philip Jonathan
Keyword(s):  
Time Series ◽  
Environmental Variables ◽  
Joint Distribution ◽  
Extreme Values ◽  
Bivariate Distribution ◽  
Dependence Structure ◽  
Joint Distributions ◽  
Resampling Method ◽  
Complex Dependence ◽  
Rigorous Method

Abstract A new method for estimating joint distributions of environmental variables is presented. The key difference to previous methods is that the joint distribution of only storm-peak parameters is modelled, rather than fitting a model to all observations. This provides a stronger justification for the use of asymptotic extreme value models, as the data considered are approximately independent. The joint distribution of all data is recovered by resampling and rescaling storm histories, conditional on the peak values. This simplifies the analysis as much of the complex dependence structure is resampled, rather than modelled explicitly. The storm histories are defined by splitting the time series into discrete blocks, with the dividing points defined as the minimum value of a variable between adjacent maxima. Storms are characterised in terms of the peak values of each parameter within each discrete block, which need not coincide in time. The key assumption is that rescaling a measured storm history results in an equally realistic time series, provided that the change in peak values is not large. Two examples of bivariate distribution are considered: the joint distribution of significant wave height (Hs) and zero up-crossing period (Tz) and the joint distribution of Hs and wind speed. It is shown that the storm resampling method gives estimates of environmental contours that agree well with the observations and provides a rigorous method for estimating extreme values.

Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition

2014 ◽  
Vol 61 (5) ◽  
pp. 402-415 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Model Complexity ◽  
Multinomial Model ◽  
Stochastic Dependence ◽  
New Model ◽  
Multinomial Processing Tree ◽  
Cognitive States ◽  
The Family ◽  
Tree Models ◽  
Flexible Framework

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

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