By Rosario N. Mantegna

ISBN-10: 0521620082

ISBN-13: 9780521620086

Statistical physics thoughts akin to stochastic dynamics, brief- and long-range correlations, self-similarity and scaling, enable an realizing of the worldwide habit of monetary structures with out first having to determine an in depth microscopic description of the process. This pioneering textual content explores using those suggestions within the description of monetary platforms, the dynamic new strong point of econophysics. The authors illustrate the scaling recommendations utilized in chance thought, severe phenomena, and fully-developed turbulent fluids and observe them to monetary time sequence. additionally they current a brand new stochastic version that screens numerous of the statistical homes saw in empirical facts. Physicists will locate the appliance of statistical physics options to monetary platforms attention-grabbing. Economists and different monetary execs will enjoy the book's empirical research equipment and well-formulated theoretical instruments that may let them describe structures composed of an important variety of interacting subsystems.

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**Extra info for An Introduction to Econophysics: Correlations and Complexity in Finance**

**Example text**

Speciﬁcally, the banker asks for his expected loss – it is an inﬁnite number of coins. The player disagrees because he assumes he will not win an inﬁnite number of coins with probability one (two coins or fewer with probability 3/4, four coins or fewer with probability 7/8, and so on). The two parties cannot come to an agreement. Why? The ‘modern’ answer is that they are trying to determine a characteristic scale for a problem that has no characteristic scale. 2 Power laws in ﬁnite systems Today, power-law distributions are used in the description of open systems.

20) [α = 1] where 0 < α ≤ 2, γ is a positive scale factor, µ is any real number, and β is an asymmetry parameter ranging from −1 to 1. The analytical form of the L´evy stable distribution is known only for a few values of α and β: • α = 1/2, β = 1 (L´evy–Smirnov) • α = 1, β = 0 (Lorentzian) • α = 2 (Gaussian) Henceforth we consider here only the symmetric stable distribution (β = 0) with a zero mean (µ = 0). Under these assumptions, the characteristic function assumes the form of Eq. 19). 11), PL (x) ≡ 1 π ∞ 0 e−γ|q| cos(qx)dq.

32) Then P˜ (S˜n ) approaches a stable non-Gaussian distribution PL (x) of index α and asymmetry parameter β, and P (Sn ) belongs to the attraction basin of PL (x). Since α is a continuous parameter over the range 0 < α ≤ 2, an inﬁnite number of attractors is present in the functional space of pdfs. They comprise the set of all the stable distributions. 1 shows schematically several such attractors, and also the convergence of a certain number of stochastic processes to the asymptotic attracting pdf.

### An Introduction to Econophysics: Correlations and Complexity in Finance by Rosario N. Mantegna

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