Energy scheduling in power market under stochastic dependence structure

2015 ◽  
Author(s):  
Mehdi Farhadkhani
Keyword(s):  
Power Market ◽  
Dependence Structure ◽  
Stochastic Dependence

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.

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.

Fast Market Splitting Matching Method for Spot Electric Power Market

2007 ◽  
Vol 127 (4) ◽  
pp. 573-580 ◽  
Author(s):  
Toshiyuki Sawa ◽  
Yuji Nakata ◽  
Mitsuo Tsurugai ◽  
Shigenari Sugiyama
Keyword(s):  
Electric Power ◽  
Power Market ◽  
Electric Power Market ◽  
Matching Method

Nash Commitment for Energy Trading in Liberalized Power Market: a State of Art Review

2016 ◽  
Vol 11 (4) ◽  
pp. 381
Author(s):  
Madan Mohan Tripathi ◽  
Anil Kumar Pandey ◽  
Amit Verma ◽  
Krishan Gopal Upadhyay ◽  
Dinesh Chandra
Keyword(s):  
Power Market ◽  
Energy Trading ◽  
State Of Art
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