Narrow Results

Green bonds are one of the most fascinating financial innovations that offer a solution to climate change and sustainable development from the financial point of view. In this study, we employ methods from statistical physics to examine the multifractal features of cross-correlations between green bonds and emerging stock markets. Utilizing multifractal detrended cross-correlation analysis (MFDCCA) on return and volatility series, our findings reveal significant cross-correlations between green bonds and emerging stock markets. The MFDCCA results uncover multifractal features and long-range cross-correlations between the two assets. Notably, volatility cross-correlations exhibit persistent behavior, while return cross-correlations vary across small and large fluctuation periods. These findings hold practical implications for policymakers and investors involved in green bonds and emerging stock markets.

We present new results in a model of technological evolution which displays different macroscopic behaviors based on very simple microscopic rules of local interaction. The main features are criticality and self-organization. We give information about new scaling relation and study the roughness of the spatial technological profile. We verify that the performance is optimized in the critical region independently of the dynamical rules.

Statistical dynamics of financial systems is investigated, based on a model of a randomly coupled equation system driven by a stochastic Langevin force. It is found that in a stable regime the noise power spectrum of the system is 1/f-like: ∝ ω^{- 3/2} (where ω is the frequency), that the autocorrelation function of the increments of the variables (returns of prices) is negative and follows the power law: ∝ - τ^{- 3/2} (where τ is the delay), and that the stochastic drift of the variables (prices, exchange rates) is subdiffusive: ∝ t^H (where t is the time, H ≈ 1/4 is the Hurst, or self-similarity, exponent). These dependencies correspond to those calculated from historical $/EURO exchange rates.

Recent works by econo-physicists [5,8,15,19] have shown that the probability function of the share returns and the volatility satisfies a power law with an exponent close to 4. On the other hand, we investigated quantitatively the return and the volatility of the daily data of the Nikkei 225 index from 1990 to 2003, and we found that the distributions of the returns and the volatility can be accurately described by the exponential distributions [11]. We then propose a stochastic model of stock markets that can reproduce these empirical laws. In our model the fluctuations of stock prices are caused by interactions among traders. We indicate that the model can reproduce the empirical facts mentioned above. In particular, we show that the interaction strengths among traders are a key variable that can distinguish the emergence of the exponential distribution or the power-law distribution.

We develop a simple Keynesian type business cycle model in which heterogeneous agents are either optimistic or pessimistic. If the majority of the agents are optimistic, then consumption expenditures are high and the economy booms, otherwise consumption expenditures are low and the economy is in a recession. Within our model, the sentiment of the agents is affected by their social interactions. For instance, people regularly meet each other and thus their mood may change. Overall, our model suggests that swings in consumer confidence may generate irregular fluctuations in economic activity.

This paper discusses the debate between those advocating a computational and those advocating a dynamic definition of complexity, and how this relates to issues in econophysics. It then reviews the criticisms that have been raised about ways in which econophysics has been done, noting that many of these are now being dealt with. Finally, it argues that while an obvious way to resolve many of these matters is to have economists and physicists work together, the physicists should be sure to work with economists who understand the complexity critique of conventional economic theory and are thus not led astray into building models that have some of the problems of standard economics models that most econophysicists have striven to overcome.

Correlations of stocks in time have been widely studied. Both the random matrix theory approach and the graphical visualization of so-called minimum spanning trees show the clustering of stocks according to industrial sectors. Studying the correlation between stocks traded in markets of different countries, we show that the random matrix theory approach is able to separate stocks according to their geographical location, provided that they are not strongly correlated. These results are compared with the results from random time series created using the market model, where the main factor is the mean of returns of the stocks of each sector.

A link in a supplier–customer network is usually a creditor–debtor relationship. If a firm goes into a state of financial insolvency or bankruptcy, then firms on its upstream are affected secondarily along the links. By using 10-year data from recent data on bankruptcy in Japan, we show that this "link effect" is by no means negligible in a nationwide economy. While the total debt of bankruptcy typically amounts to as much as a few percent of GDP in Japan, nearly 20% of all bankruptcies are due to the link effect. Interestingly, we find that such a link effect becomes comparable with other causes of bankruptcy, including poor performance in business (namely solo failure) with respect to the number of events, as the bankruptcy is larger. This means that the link effect grows for larger bankruptcies. Because the supplier–customer network has a heavy tail in its degree distribution, the ripple effect due to the bankruptcy chain is considerable. Since every firm is embedded in a giant network of production, this study would suggest the importance of understanding the heterogeneity in the large-scale network of suppliers and customers on a nationwide scale.

In this paper, starting from Jaynes' MaxEnt methodology [10, 11], we follow the original idea of Aoki [1] to implement a canonical MaxEnt inference model for the replication of industrial firms' dynamics over a space of economic states. We develop an aggregate model to infer the distributions of agents at meso level using representative states. In particular, we estimate the access probability for agents in different states consistently with macroscopic economic constraints. The model is calibrated on the basis of a sample of firms, drawn from the AMADEUS database, within the manufacturing industry made up of nine sectors of economic activity from 1995 to 2004, and results come to experimental proof at aggregate macroscopic level.

Minimum spanning trees and planar maximally filtered graphs are generated from correlations between the 300 most-capitalized NYSE stocks' daily returns, computed dynamically over moving windows of sizes between 1 and 12 months, in the period from 2001 to 2003. We study how different economic sectors differently populate the various regions of these graphs. We find that the financial sector is always at the center whereas the periphery is shared among different sectors. Four extremes are observed: stocks well-connected and central; stocks well-connected but at the same time peripheral; stocks poorly-connected but central; stocks poorly-connected and peripheral. Two principal components of centrality measures are individuated. The economic meaning of this hierarchical disposition is discussed.

The cross-correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different time–scales and then explore the eigenvalue spectrum over sliding time-windows. The dynamics of the eigenvalue spectrum at different times and scales provides insight into the interactions between the numerous constituents involved.

Eigenvalue dynamics are examined for both medium, and high-frequency equity returns, with the associated correlation structure shown to be dependent on both time and scale. Additionally, the Epps effect is established using this multivariate method and analyzed at longer scales than previously studied. A partition of the eigenvalue time-series demonstrates, at very short scales, the emergence of negative returns when the largest eigenvalue is greatest. Finally, a portfolio optimization shows the importance of time–scale information in the context of risk management.

Recent developments in quantum computation and quantum information theory allow one to extend the scope of game theory for the quantum world. The authors have recently proposed a quantum description of financial market in terms of quantum game theory. This paper contains an analysis of such markets that shows that there are advantages to be gained from using quantum computers and quantum strategies.

The formalism of geometrothermodynamics has been developed to describe the properties of thermodynamic systems in terms of concepts of differential geometry. On the other hand, in econophysics, it is argued that under certain conditions the behavior of economic systems can be described by using the laws of classical thermodynamics. These two results are used in this work to propose a geometric description of economic systems. We obtain as a result that most systems can be described by two different geometries corresponding to the Boltzmann–Gibbs and Pareto distributions, which represent two different population groups that are usually present in most economic systems. The geometrothermodynamic analysis shows that no phase transitions are present in the Boltzmann–Gibbs sector, whereas the Pareto sector is characterized by a strong thermodynamic interaction that leads to the appearance of a rich phase transition structure. We argue that those phase transitions could be interpreted as financial crises.

How can we detect real estate bubbles? In this paper, we propose making use of information on the cross-sectional dispersion of real estate prices. During bubble periods, prices tend to go up considerably for some properties, but less so for others, so that price inequality across properties increases. In other words, a key characteristic of real estate bubbles is not the rapid price hike itself but a rise in price dispersion. Given this, the purpose of this paper is to examine whether developments in the dispersion in real estate prices can be used to detect bubbles in property markets as they arise, using data from Japan and the U.S. First, we show that the land price distribution in Tokyo had a power-law tail during the bubble period in the late 1980s, while it was very close to a lognormal before and after the bubble period. Second, in the U.S. data we find that the tail of the house price distribution tends to be heavier in those states which experienced a housing bubble. We also provide evidence suggesting that the power-law tail observed during bubble periods arises due to the lack of price arbitrage across regions.

In this work, we use Monte-Carlo simulations to study the control of the fluctuations for tax evasion in the economics model proposed by [G. Zaklan, F. Westerhoff and D. Stauffer, J. Econ. Interact. Coordination. 4 (2009) 1; G. Zaklam, F.W.S. Lima and F. Westerhofd, Physica A387 (2008) 5857.] via a nonequilibrium model with two states ($-1,+1$) and a noise $q$ proposed for [M. J. Oliveira, J. Stat. Phys.66 (1992) 273] and known as Majority-Vote model (MVM) and Sánchez–López-Rodríguez model on communities of agents or persons on some topologies as directed and undirected Barabási–Albert networks and Erdös–Rényi random graphs, Apollonian networks, directed small-world networks and Stauffer–Hohnisch–Pittnauer networks. The MVM is applied around the noise critical $q_c$ to evolve the Zaklan model.

China’s stock market is the largest emerging market in the world. It is widely accepted that the Chinese stock market is far from efficiency and it possesses possible linear and nonlinear dependencies. We study the predictability of returns in the Chinese stock market by employing the wild bootstrap automatic variance ratio test and the generalized spectral test. We find that the return predictability vary over time and a significant return predictability is observed around market turmoils. Our findings are consistent with the Adaptive Markets Hypothesis (AMH) and have practical implications for market participants and policy makers. A predictability index can be constructed for each asset, which might help warn a crisis is in store, ease the development of the ongoing bubble, and stabilize the market.

In this work, we apply the electro-magnetism geometrical model of the Möbius Strip in the context of Corporate Social Responsibility (CSR) in order to test the relationship between CSR and organizational performance. We exploit a unique dataset that includes 4135 workers in a matched sample of 320 Italian social enterprises. Results show that CSR is the strongest determinant of firm performance, although there is an indirect effect of cooperation and worker alienation in terms of higher job satisfaction.

In this work, we review various dynamical models in socio-econo-physics, and discuss their roles in understanding socio-economic biases. We deal with fairness in nations, wealth distributions in nations, elections’ dynamics and tax evasion models. We claim that these models can better explain biases in societies than traditional models of economics from 1700s that are based on various assumptions such as the “invisible hand concept” that justifies interventions from social beneficiaries.

The correlation structure of a stock market contains important financial contents, which may change remarkably due to the occurrence of financial crisis. We perform a comparative analysis of the Chinese stock market around the occurrence of the 2008 crisis based on the random matrix analysis of high-frequency stock returns of 1228 Chinese stocks. Both raw correlation matrix and partial correlation matrix with respect to the market index in two time periods of one year are investigated. We find that the Chinese stocks have stronger average correlation and partial correlation in 2008 than in 2007 and the average partial correlation is significantly weaker than the average correlation in each period. Accordingly, the largest eigenvalue of the correlation matrix is remarkably greater than that of the partial correlation matrix in each period. Moreover, each largest eigenvalue and its eigenvector reflect an evident market effect, while other deviating eigenvalues do not. We find no evidence that deviating eigenvalues contain industrial sectorial information. Surprisingly, the eigenvectors of the second largest eigenvalues in 2007 and of the third largest eigenvalues in 2008 are able to distinguish the stocks from the two exchanges. We also find that the component magnitudes of the some largest eigenvectors are proportional to the stocks’ capitalizations.

Personal income distribution in the USA has a well-defined two-class structure. The majority of population (97–99 %) belongs to the lower class characterized by the exponential Boltzmann-Gibbs ("thermal") distribution, whereas the upper class (1–3 % of population) has a Pareto power-law ("superthermal") distribution. By analyzing income data for 1983–2001, we show that the "thermal" part is stationary in time, save for a gradual increase of the effective temperature, whereas the "superthermal" tail swells and shrinks following the stock market. We discuss the concept of equilibrium inequality in a society, based on the principle of maximal entropy, and quantitatively show that it applies to the majority of population.