Methods of Non-Extensive Statistical Physics in Analysis of Price Returns on Polish Stock Market

Bil, Łukasz; Grech, Dariusz; Podhajska, Ewa

doi:10.12693/aphyspola.129.986

Cited by 7 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, calculations have demonstrated the superiority of the NCEE method over such traditional estimation techniques as Shannon entropy, the non-linear least squares, the generalized methods of moments, and the maximum likelihood approaches [ 57 ]. Non-extensive Tsallis entropy also finds a number of applications on financial markets, which includes studies concerning the distribution of return fluctuations for the Polish stock market index WIG20 [ 58 ], the origin of multifractality in the time series [ 59 ], the exchange rate return fluctuations [ 60 ], relationship between the stock market returns and corresponding trading volumes [ 61 ], the memory effect involved in returns of companies from WIG 30 index on the Warsaw Stock Exchange [ 62 ] and the asymmetry of price returns on stock and money markets [ 63 ]. Other types of entropy have also been applied to financial markets [ 64 ].…”

Section: Non-extensive Cross-entropy Econometricsmentioning

confidence: 99%

The Role of Entropy in the Development of Economics

Jakimowicz

2020

Entropy

View full text Add to dashboard Cite

The aim of this paper is to examine the role of thermodynamics, and in particular, entropy, for the development of economics within the last 150 years. The use of entropy has not only led to a significant increase in economic knowledge, but also to the emergence of such scientific disciplines as econophysics, complexity economics and quantum economics. Nowadays, an interesting phenomenon can be observed; namely, that rapid progress in economics is being made outside the mainstream. The first significant achievement was the emergence of entropy economics in the early 1970s, which introduced the second law of thermodynamics to considerations regarding production processes. In this way, not only was ecological economics born but also an entropy-based econometric approach developed. This paper shows that non-extensive cross-entropy econometrics is a valuable complement to traditional econometrics as it explains phenomena based on power-law probability distribution and enables econometric model estimation for non-ergodic ill-behaved (troublesome) inverse problems. Furthermore, the entropy economics has accelerated the emergence of modern econophysics and complexity economics. These new directions of research have led to many interesting discoveries that usually contradict the claims of conventional economics. Econophysics has questioned the efficient market hypothesis, while complexity economics has shown that markets and economies function best near the edge of chaos. Quantum economics has already appeared on the horizon, which recognizes money as a fundamental measurement device in the economy. The development of these sciences may indicate the need to reformulate all mainstream economics from its foundations.

show abstract

Section: Non-extensive Cross-entropy Econometricsmentioning

confidence: 99%

The Role of Entropy in the Development of Economics

Jakimowicz

2020

Entropy

View full text Add to dashboard Cite

show abstract

“…However, Boltzmann–Gibbs statistical mechanics and standard thermodynamics present serious difficulties or anomalies for non-equilibrium, open, non-ergodic, non-mixing, systems, and for those that exhibit memory retention. Within a long list, we might mention systems that involve long-range interactions (see e.g., [ 57 , 58 ]), non-Markovian stochastic processes, like financial markets (see e.g., [ 59 , 60 , 61 , 62 , 63 , 64 ]), dissipative systems in a phase space that has some underlying looking (multi)fractal-like structure (see e.g., [ 65 ]), like many open social systems, all hardly having an additive property (see e.g., [ 66 ]).…”

Section: Methodsmentioning

confidence: 99%

“…[59,5]), non-Markovian stochastic processes, like financial markets (see e.g. [60,61,62,8,68,40]), dissipative systems in a phase space which has some underlying looking (multi)fractal-like structure (see e.g. [58]), like many open social systems, all hardly having an additive property (see e.g.…”

Section: Reasoning Behind the Tsallis Entropymentioning

confidence: 99%

Tsallis Entropy for Cross-Shareholding Network Configurations

Cerqueti

Rotundo

Ausloos

2020

Entropy

View full text Add to dashboard Cite

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.

show abstract

“…Genay and Gradojevic compared the 1987 and 2008 financial crisis using entropic risk management measures and suggested the entropic methodology as a market sentiment indicator (Genay & Gradojevic, 2017). Bill et al used non-extensive statistical framework to study the behavior of volatility in Polish stock market (Bil, Grech, & Podhajska, 2016). Borland have used Tsallis entropy framework to create an option pricing model (Borland, 2002) (Vellekoop & Nieuwenhuis, 2007) (Borland & Bouchaud, 2004).…”

Section: Introductionmentioning

confidence: 99%

Non-Extensive Value-at-Risk Estimation During Times of Crisis

Hajihasani,

Namaki,

Asadi

et al. 2020

Preprint

View full text Add to dashboard Cite

Value-at-risk is one of the important subjects that extensively used by researchers and practitioners for measuring and managing uncertainty in financial markets. Although valueat-risk is a common risk control instrument, but there are criticisms about its performance. One of these cases, which has been studied in this research, is the value-at-risk underestimation during times of crisis. In these periods, the non-Gaussian behavior of markets intensifies and the estimated value-at-risks by normal models are lower than the real values. In fact, during times of crisis, the probability density of extreme values in financial return series increases and this heavy-tailed behavior of return series reduces the accuracy of the normal value-at-risk estimation models. A potential approach that can be used to describe non-Gaussian behavior of return series, is Tsallis entropy framework and non-extensive statistical methods.In this paper, we have used non-extensive value at risk model for analyzing the behavior of financial markets during times of crisis. By applying q-Gaussian probability density function, we can see a better value-at-risk estimation in comparison with the normal models, especially during times of crisis. We showed that q-Gaussian model estimates value-at-risk better than normal model. Also we saw in the mature markets, it is obvious that the difference of value-at-risk between normal condition and non-extensive approach increase more than one standard deviation during times of crisis, but in the emerging markets we cannot see a specific pattern.

show abstract

Methods of Non-Extensive Statistical Physics in Analysis of Price Returns on Polish Stock Market

Cited by 7 publications

References 27 publications

The Role of Entropy in the Development of Economics

The Role of Entropy in the Development of Economics

Tsallis Entropy for Cross-Shareholding Network Configurations

Non-Extensive Value-at-Risk Estimation During Times of Crisis

Contact Info

Product

Resources

About