Models based on random fluctuations

The interests of the neoliberal economic theory has moved from the cycle theory, which some consider deterministic towards the study of random economic fluctuations, while still using the expression cycle, as has always distinguished the cyclicality of those strictly determined, as the seasons.
Defenders of the theory of rational expectations argue that there can be no “cycle” as they lead to opportunities that arbitration would be used by rational economic agents and thus end the cycle dynamics. The theory of business cycles shows instead of acting as such agents ends up reinforcing the causes of cyclical fluctuations.
However, the assumption of classical liberal or that the economy is close to balance, it seems hardly compatible with the existence of cycles. For this reason, alternative formulations have remained interested and have faced the cycle theory since the nineteenth century.
So certain stochastic moving average models lead to time-series displayed in graphs show fluctuations similar to those seen in historical series of actual values of inflation, employment or investment. According to these approaches, the stochastic processes generated graphics with the greatest similarity to the real time series of cycles that any theory based on deterministic formulations.
In 1900 the mathematician Louis Bachelier (Theorie of speculation) suggested that fluctuations in prices followed a certain random walk, without being totally random and cyclical component. As a pioneering work, the Bachelier model failed to explain the large fluctuations as the Great Depression.
Beno t Mandelbrot (1963) proposed an improved model based on the “Levy flight” (a more general case of “random walk”) that applies to the price of cotton. Using the distribution of Levy seeks to explain the large fluctuations in the markets.
In 1995, two physicists, Rosario Mantegna and Eugene H. Stanley, about a million records of the stock market the previous five years and proposed the model of truncated Levy flight, the distribution was found halfway between a walk and a Gaussian random Levy flight. We found similar distributions independent of the temporal scale, this property is associated with functions autosimilaridad fractals. A stochastic model is still needed to be discovered.

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