Copulas and time series with long-ranged dependences

We review ideas on temporal dependences and recurrences in discrete time series from several areas of natural and social sciences. We revisit existing studies and redefine the relevant observables in the language of copulas (joint laws of the ranks). We propose that copulas provide an appropriate mathematical framework to study non-linear time dependences and related concepts - like aftershocks, Omori law, recurrences, waiting times. We also critically argue using this global approach that previous phenomenological attempts involving only a long-ranged autocorrelation function lacked complexity in that they were essentially mono-scale.Comment: 11 pages, 8 figure

The travelling salesman problem on randomly diluted lattices: results for small-size systems

If one places N cities randomly on a lattice of size L, we find that the normalized optimal travel distances per city in the Euclidean and Manhattan metrics vary monotonically with the city concentration p. We have studied such optimal tours for visiting all the cities using a branch and bound algorithm, giving exact optimized tours for small system sizes (N<100). Extrapolating the results for N tending to infinity, we find that the normalized optimal travel distances per city in the Euclidean and Manhattan metrics both equal unity for p=1, and they reduce to about 0.74 and 0.94, respectively, as p tends to zero. Although the problem is trivial for p=1, it certainly reduces to the standard TSP on continuum (NP-hard problem) for p tending to zero. We did not observe any irregular behaviour at any intermediate point. The crossover from the triviality to the NP-hard problem seems to occur at p=1.Comment: 7 pages, 4 figures. Revised version with changes in text and figures (to be published in Euro. Phys. Jour. B

Statistical mechanics of money: How saving propensity affects its distribution

We consider a simple model of a closed economic system where the total money is conserved and the number of economic agents is fixed. In analogy to statistical systems in equilibrium, money and the average money per economic agent are equivalent to energy and temperature, respectively. We investigate the effect of the saving propensity of the agents on the stationary or equilibrium money distribution.The equilibrium probablity distribution of money becomes the usual Gibb's distribution, characteristic of non-interacting agents, when the agents do not save. However with saving, even for local or individual self-interest, the dynamics become cooperative and the resulting asymmetric Gaussian-like stationary distribution acquires global ordering properties. Intriguing singularities are observed in the stationary money distribution in the market, as function of the ``marginal saving propensity'' of the agents.Comment: 9 pages, 5 figures. Revised version with major changes in the text and figures (to appear in Euro. Phys. Jour. B

Market application of the percolation model: Relative price distribution

We study a variant of the Cont-Bouchaud model which utilizes the perco lation approach of multi-agent simulations of the stock market fluctuations. Here, instead of considering the relative price change as the difference of the total demand and total supply, we consider the relative price change to be proportiona l to the ``relative'' difference of demand and supply (the ratio of the difference in total demand and total supply to the sum of the total demand and total supply). We then study the probability distribution of the price changes.Comment: Int. J. Mod. Phys. C 13, Jan 200

The Euclidean travelling salesman problem: Frequency distribution of neighbours for small-size systems

We have studied numerically the frequency distribution $\rho (n)$ of the n-th neighbour along the optimal tour in the Euclidean travelling salesman problem for N cities, in dimensions d=2 and d=3. We find there is no significant dependence of $\rho (n)$ on either the number of cities N or the dimension d.Comment: 6 pages, 3 figures. To be published in Int. J. Mod. Phys.

The near-extreme density of intraday log-returns

The extreme event statistics plays a very important role in the theory and practice of time series analysis. The reassembly of classical theoretical results is often undermined by non-stationarity and dependence between increments. Furthermore, the convergence to the limit distributions can be slow, requiring a huge amount of records to obtain significant statistics, and thus limiting its practical applications. Focussing, instead, on the closely related density of "near-extremes" -- the distance between a record and the maximal value -- can render the statistical methods to be more suitable in the practical applications and/or validations of models. We apply this recently proposed method in the empirical validation of an adapted financial market model of the intraday market fluctuations