A Nonlinear Super-Exponential Rational Model of Speculative Financial Bubbles

Keeping a basic tenet of economic theory, rational expectations, we model the nonlinear positive feedback between agents in the stock market as an interplay between nonlinearity and multiplicative noise. The derived hyperbolic stochastic finite-time singularity formula transforms a Gaussian white noise into a rich time series possessing all the stylized facts of empirical prices, as well as accelerated speculative bubbles preceding crashes. We use the formula to invert the two years of price history prior to the recent crash on the Nasdaq (april 2000) and prior to the crash in the Hong Kong market associated with the Asian crisis in early 1994. These complex price dynamics are captured using only one exponent controlling the explosion, the variance and mean of the underlying random walk. This offers a new and powerful detection tool of speculative bubbles and herding behavior.Comment: Latex document of 24 pages including 5 eps figure

What determines China's housing price dynamics? New evidence from a DSGE-VAR

We investigate what determines China's housing price dynamics using a DSGE-VAR estimated with priors allowing for the featured operating of normal and 'shadow' banks in China, with data observed between 2001 and 2014. We find that the housing demand shock, which is the essential factor for housing price 'bubbles' to happen, accounts for near 90% of the housing price fluctuation. We also find that a prosperous housing market could have led to future economic growth, though quantitatively its marginal impact is small. But this also means that, for policy-makers who wish to stabilise the housing market, the cost on output reduction would be rather limited