Econophysics: Financial White Noise Switch. Thought of the Day 115.0


What is the cause of large market fluctuation? Some economists blame irrationality behind the fat-tail distribution. Some economists observed that social psychology might create market fad and panic, which can be modeled by collective behavior in statistical mechanics. For example, the bi-modular distribution was discovered from empirical data in option prices. One possible mechanism of polarized behavior is collective action studied in physics and social psychology. Sudden regime switch or phase transition may occur between uni-modular and bi-modular distribution when field parameter changes across some threshold. The Ising model in equilibrium statistical mechanics was borrowed to study social psychology. Its phase transition from uni-modular to bi-modular distribution describes statistical features when a stable society turns into a divided society. The problem of the Ising model is that its key parameter, the social temperature, has no operational definition in social system. A better alternative parameter is the intensity of social interaction in collective action.

A difficult issue in business cycle theory is how to explain the recurrent feature of business cycles that is widely observed from macro and financial indexes. The problem is: business cycles are not strictly periodic and not truly random. Their correlations are not short like random walk and have multiple frequencies that changing over time. Therefore, all kinds of math models are tried in business cycle theory, including deterministic, stochastic, linear and nonlinear models. We outline economic models in terms of their base function, including white noise with short correlations, persistent cycles with long correlations, and color chaos model with erratic amplitude and narrow frequency band like biological clock.



The steady state of probability distribution function in the Ising Model of Collective Behavior with h = 0 (without central propaganda field). a. Uni-modular distribution with low social stress (k = 0). Moderate stable behavior with weak interaction and high social temperature. b. Marginal distribution at the phase transition with medium social stress (k = 2). Behavioral phase transition occurs between stable and unstable society induced by collective behavior. c. Bi-modular distribution with high social stress (k = 2.5). The society splits into two opposing groups under low social temperature and strong social interactions in unstable society. 

Deterministic models are used by Keynesian economists for endogenous mechanism of business cycles, such as the case of the accelerator-multiplier model. The stochastic models are used by the Frisch model of noise-driven cycles that attributes external shocks as the driving force of business fluctuations. Since 1980s, the discovery of economic chaos and the application of statistical mechanics provide more advanced models for describing business cycles. Graphically,


The steady state of probability distribution function in socio-psychological model of collective choice. Here, “a” is the independent parameter; “b” is the interaction parameter. a Centered distribution with b < a (denoted by short dashed curve). It happens when independent decision rooted in individualistic orientation overcomes social pressure through mutual communication. b Horizontal flat distribution with b = a (denoted by long dashed line). Marginal case when individualistic orientation balances the social pressure. c Polarized distribution with b > a (denoted by solid line). It occurs when social pressure through mutual communication is stronger than independent judgment. 


Numerical 1 autocorrelations from time series generated by random noise and harmonic wave. The solid line is white noise. The broken line is a sine wave with period P = 1. 

Linear harmonic cycles with unique frequency are introduced in business cycle theory. The auto-correlations from harmonic cycle and white noise are shown in the above figure. Auto-correlation function from harmonic cycles is a cosine wave. The amplitude of cosine wave is slightly decayed because of limited data points in numerical experiment. Auto-correlations from a random series are an erratic series with rapid decade from one to residual fluctuations in numerical calculation. The auto-regressive (AR) model in discrete time is a combination of white noise term for simulating short-term auto-correlations from empirical data.

The deterministic model of chaos can be classified into white chaos and color chaos. White chaos is generated by nonlinear difference equation in discrete-time, such as one-dimensional logistic map and two-dimensional Henon map. Its autocorrelations and power spectra look like white noise. Its correlation dimension can be less than one. White noise model is simple in mathematical analysis but rarely used in empirical analysis, since it needs intrinsic time unit.

Color chaos is generated by nonlinear differential equations in continuous-time, such as three-dimensional Lorenz model and one-dimensional model with delay-differential model in biology and economics. Its autocorrelations looks like a decayed cosine wave, and its power spectra seem a combination of harmonic cycles and white noise. The correlation dimension is between one and two for 3D differential equations, and varying for delay-differential equation.


History shows the remarkable resilience of a market that experienced a series of wars and crises. The related issue is why the economy can recover from severe damage and out of equilibrium? Mathematically speaking, we may exam the regime stability under parameter change. One major weakness of the linear oscillator model is that the regime of periodic cycle is fragile or marginally stable under changing parameter. Only nonlinear oscillator model is capable of generating resilient cycles within a finite area under changing parameters. The typical example of linear models is the Samuelson model of multiplier-accelerator. Linear stochastic models have similar problem like linear deterministic models. For example, the so-called unit root solution occurs only at the borderline of the unit root. If a small parameter change leads to cross the unit circle, the stochastic solution will fall into damped (inside the unit circle) or explosive (outside the unit circle) solution.


Conjuncted: Ergodicity. Thought of the Day 51.1


When we scientifically investigate a system, we cannot normally observe all possible histories in Ω, or directly access the conditional probability structure {PrE}E⊆Ω. Instead, we can only observe specific events. Conducting many “runs” of the same experiment is an attempt to observe as many histories of a system as possible, but even the best experimental design rarely allows us to observe all histories or to read off the full conditional probability structure. Furthermore, this strategy works only for smaller systems that we can isolate in laboratory conditions. When the system is the economy, the global ecosystem, or the universe in its entirety, we are stuck in a single history. We cannot step outside that history and look at alternative histories. Nonetheless, we would like to infer something about the laws of the system in general, and especially about the true probability distribution over histories.

Can we discern the system’s laws and true probabilities from observations of specific events? And what kinds of regularities must the system display in order to make this possible? In other words, are there certain “metaphysical prerequisites” that must be in place for scientific inference to work?

To answer these questions, we first consider a very simple example. Here T = {1,2,3,…}, and the system’s state at any time is the outcome of an independent coin toss. So the state space is X = {Heads, Tails}, and each possible history in Ω is one possible Heads/Tails sequence.

Suppose the true conditional probability structure on Ω is induced by the single parameter p, the probability of Heads. In this example, the Law of Large Numbers guarantees that, with probability 1, the limiting frequency of Heads in a given history (as time goes to infinity) will match p. This means that the subset of Ω consisting of “well-behaved” histories has probability 1, where a history is well-behaved if (i) there exists a limiting frequency of Heads for it (i.e., the proportion of Heads converges to a well-defined limit as time goes to infinity) and (ii) that limiting frequency is p. For this reason, we will almost certainly (with probability 1) arrive at the true conditional probability structure on Ω on the basis of observing just a single history and counting the number of Heads and Tails in it.

Does this result generalize? The short answer is “yes”, provided the system’s symmetries are of the right kind. Without suitable symmetries, generalizing from local observations to global laws is not possible. In a slogan, for scientific inference to work, there must be sufficient regularities in the system. In our toy system of the coin tosses, there are. Wigner (1967) recognized this point, taking symmetries to be “a prerequisite for the very possibility of discovering the laws of nature”.

Generally, symmetries allow us to infer general laws from specific observations. For example, let T = {1,2,3,…}, and let Y and Z be two subsets of the state space X. Suppose we have made the observation O: “whenever the state is in the set Y at time 5, there is a 50% probability that it will be in Z at time 6”. Suppose we know, or are justified in hypothesizing, that the system has the set of time symmetries {ψr : r = 1,2,3,….}, with ψr(t) = t + r, as defined as in the previous section. Then, from observation O, we can deduce the following general law: “for any t in T, if the state of the system is in the set Y at time t, there is a 50% probability that it will be in Z at time t + 1”.

However, this example still has a problem. It only shows that if we could make observation O, then our generalization would be warranted, provided the system has the relevant symmetries. But the “if” is a big “if”. Recall what observation O says: “whenever the system’s state is in the set Y at time 5, there is a 50% probability that it will be in the set Z at time 6”. Clearly, this statement is only empirically well supported – and thus a real observation rather than a mere hypothesis – if we can make many observations of possible histories at times 5 and 6. We can do this if the system is an experimental apparatus in a lab or a virtual system in a computer, which we are manipulating and observing “from the outside”, and on which we can perform many “runs” of an experiment. But, as noted above, if we are participants in the system, as in the case of the economy, an ecosystem, or the universe at large, we only get to experience times 5 and 6 once, and we only get to experience one possible history. How, then, can we ever assemble a body of evidence that allows us to make statements such as O?

The solution to this problem lies in the property of ergodicity. This is a property that a system may or may not have and that, if present, serves as the desired metaphysical prerequisite for scientific inference. To explain this property, let us give an example. Suppose T = {1,2,3,…}, and the system has all the time symmetries in the set Ψ = {ψr : r = 1,2,3,….}. Heuristically, the symmetries in Ψ can be interpreted as describing the evolution of the system over time. Suppose each time-step corresponds to a day. Then the history h = (a,b,c,d,e,….) describes a situation where today’s state is a, tomorrow’s is b, the next day’s is c, and so on. The transformed history ψ1(h) = (b,c,d,e,f,….) describes a situation where today’s state is b, tomorrow’s is c, the following day’s is d, and so on. Thus, ψ1(h) describes the same “world” as h, but as seen from the perspective of tomorrow. Likewise, ψ2(h) = (c,d,e,f,g,….) describes the same “world” as h, but as seen from the perspective of the day after tomorrow, and so on.

Given the set Ψ of symmetries, an event E (a subset of Ω) is Ψ-invariant if the inverse image of E under ψ is E itself, for all ψ in Ψ. This implies that if a history h is in E, then ψ(h) will also be in E, for all ψ. In effect, if the world is in the set E today, it will remain in E tomorrow, and the day after tomorrow, and so on. Thus, E is a “persistent” event: an event one cannot escape from by moving forward in time. In a coin-tossing system, where Ψ is still the set of time translations, examples of Ψ- invariant events are “all Heads”, where E contains only the history (Heads, Heads, Heads, …), and “all Tails”, where E contains only the history (Tails, Tails, Tails, …).

The system is ergodic (with respect to Ψ) if, for any Ψ-invariant event E, the unconditional probability of E, i.e., PrΩ(E), is either 0 or 1. In other words, the only persistent events are those which occur in almost no history (i.e., PrΩ(E) = 0) and those which occur in almost every history (i.e., PrΩ(E) = 1). Our coin-tossing system is ergodic, as exemplified by the fact that the Ψ-invariant events “all Heads” and “all Tails” occur with probability 0.

In an ergodic system, it is possible to estimate the probability of any event “empirically”, by simply counting the frequency with which that event occurs. Frequencies are thus evidence for probabilities. The formal statement of this is the following important result from the theory of dynamical systems and stochastic processes.

Ergodic Theorem: Suppose the system is ergodic. Let E be any event and let h be any history. For all times t in T, let Nt be the number of elements r in the set {1, 2, …, t} such that ψr(h) is in E. Then, with probability 1, the ratio Nt/t will converge to PrΩ(E) as t increases towards infinity.

Intuitively, Nt is the number of times the event E has “occurred” in history h from time 1 up to time t. The ratio Nt/t is therefore the frequency of occurrence of event E (up to time t) in history h. This frequency might be measured, for example, by performing a sequence of experiments or observations at times 1, 2, …, t. The Ergodic Theorem says that, almost certainly (i.e., with probability 1), the empirical frequency will converge to the true probability of E, PrΩ(E), as the number of observations becomes large. The estimation of the true conditional probability structure from the frequencies of Heads and Tails in our illustrative coin-tossing system is possible precisely because the system is ergodic.

To understand the significance of this result, let Y and Z be two subsets of X, and suppose E is the event “h(1) is in Y”, while D is the event “h(2) is in Z”. Then the intersection E ∩ D is the event “h(1) is in Y, and h(2) is in Z”. The Ergodic Theorem says that, by performing a sequence of observations over time, we can empirically estimate PrΩ(E) and PrΩ(E ∩ D) with arbitrarily high precision. Thus, we can compute the ratio PrΩ(E ∩ D)/PrΩ(E). But this ratio is simply the conditional probability PrΕ(D). And so, we are able to estimate the conditional probability that the state at time 2 will be in Z, given that at time 1 it was in Y. This illustrates that, by allowing us to estimate unconditional probabilities empirically, the Ergodic Theorem also allows us to estimate conditional probabilities, and in this way to learn the properties of the conditional probability structure {PrE}E⊆Ω.

We may thus conclude that ergodicity is what allows us to generalize from local observations to global laws. In effect, when we engage in scientific inference about some system, or even about the world at large, we rely on the hypothesis that this system, or the world, is ergodic. If our system, or the world, were “dappled”, then presumably we would not be able to presuppose ergodicity, and hence our ability to make scientific generalizations would be compromised.