Production, Capital Stock and Price Dynamics in Simple Model of Closed Economy

The purpose of this paper is to study a price level dynamics in a simple four-equation model. A basis of this model is developed from dynamical Kaldorian model which could be noticed very frequently in works of non-linear economic dynamics. Our approach is traditional. The difference is observed in a choice of an investment function. The investment function depending on the difference of logarithm of production and logarithm of capital (logarithm of the productivity of capital) is in a form of the logistic function. These two equations create relatively closed sub-model generating both production and capital stock trajectories. Two other equations describe the price level dynamics as a consequence of money market disequilibrium and continuously adaptive expectation of inflation. Our investigation is firstly aimed to core model dynamics, i.e., a dynamics of the production and capital stock. Secondly is to analyze dynamics of the model as a whole, i.e., to the first part is superadded the price dynamics and expected inflation dynamics depending on both an adaptation parameter of the commodity market and a parameter of the expectation. Thirdly we compute Lyapunov exponents for a simple model of closed economy showing it’s a chaotic behavior. Simulation studies are performedProduction, Capital Stock, Price Dynamics, Expected Inflation

Can a stochastic cusp catastrophe model explain stock market crashes?

This paper is the first attempt to fit a stochastic cusp catastrophe model to stock market data. We show that the cusp catastrophe model explains the crash of stock exchanges much better than other models. Using the data of U.S. stock markets we demonstrate that the crash of October 19, 1987, may be better explained by cusp catastrophe theory, which is not true for the crash of September 11, 2001. With the help of sentiment measures, such as the index put/call options ratio and trading volume (the former models the chartists, the latter the fundamentalists), we have found that the 1987 returns are bimodal, and the cusp catastrophe model fits these data better than alternative models. Therefore we may say that the crash has been led by internal forces. However, the causes for the crash of 2001 are external, which is also evident in much weaker presence of bifurcations in the data. In this case, alternative models explain the crash of stock exchanges better than the cusp catastrophe model.Stochastic cusp catastrophe Bifurcations Singularity Nonlinear dynamics Stock market crash

Smart predictors in the heterogeneous agent model

Heterogeneous agent model, Market structure, Smart traders, Hurst exponent, C15, D84, G14,