The log-periodic-AR(1)-GARCH(1,1) model for financial crashes

This paper intends to meet recent claims for the attainment of more rigorous statistical methodology within the econophysics literature. To this end, we consider an econometric approach to investigate the outcomes of the log-periodic model of price movements, which has been largely used to forecast financial crashes. In order to accomplish reliable statistical inference for unknown parameters, we incorporate an autoregressive dynamic and a conditional heteroskedasticity structure in the error term of the original model, yielding the log-periodic-AR(1)-GARCH(1,1) model. Both the original and the extended models are fitted to financial indices of U. S. market, namely S&P500 and NASDAQ. Our analysis reveal two main points: (i) the log-periodic-AR(1)-GARCH(1,1) model has residuals with better statistical properties and (ii) the estimation of the parameter concerning the time of the financial crash has been improved.Comment: 17 pages, 4 figures, 12 tables, to appear in Europen Physical Journal

Worrying trends in econophysics

Econophysics has already made a number of important empirical contributions to our understanding of the social and economic world. These fall mainly into the areas of finance and industrial economics, where in each case there is a large amount of reasonably well-defined data. More recently, Econophysics has also begun to tackle other areas of economics where data is much more sparse and much less reliable. In addition, econophysicists have attempted to apply the theoretical approach of statistical physics to try to understand empirical findings. Our concerns are fourfold. First, a lack of awareness of work that has been done within economics itself. Second, resistance to more rigorous and robust statistical methodology. Third, the belief that universal empirical regularities can be found in many areas of economic activity. Fourth, the theoretical models which are being used to explain empirical phenomena. The latter point is of particular concern. Essentially, the models are based upon models of statistical physics in which energy is conserved in exchange processes. There are examples in economics where the principle of conservation may be a reasonable approximation to reality, such as primitive hunter–gatherer societies. But in the industrialised capitalist economies, income is most definitely not conserved. The process of production and not exchange is responsible for this. Models which focus purely on exchange and not on production cannot by definition offer a realistic description of the generation of income in the capitalist, industrialised economies

Learning to Trade and Mediate

In this paper we study the behavior of boundedly rational agents in a two good economy where trading is costly with respect to time. All individuals have a fixed time budget and may spend time for the production of good one, the production of good two and trading. They update their strategies, which determine their time allocation, according to a simple imitation type learning rule with noise. In a setup with two different type of agents with different production technologies we show by the means of simulations that both direct trade and trade via mediators who specialize in trading can emerge. We can also observe the transition from a pure production economy via direct trade to an economy with mediated trade. JEL Classification: D83, F10 Keywords: bounded rationality, learning, trade, mediation 1 Introduction Mediated trade is a phenomenon which can be observed in many different markets in the real world economy. Examples reach from retail stores to real estate agents an..