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

Regulating financial conglomerates

We investigate the optimal regulation of financial conglomerates which combine a bank and a non-bank financial institution. The conglomerate’s risk-taking incentives depend upon the level of market discipline it faces, which in turn is determined by the conglomerate’s liability structure. We examine optimal capital requirements for stand-alone institutions, for integrated financial conglomerates, and for financial conglomerates that are structured as holding companies. For a given risk profile, integrated conglomerates have a lower probability of failure than either their stand-alone or decentralized equivalent. However, when risk profiles are endogenously selected, conglomeration may extend the reach of the deposit insurance safety net and hence provide incentives for increased risk-taking. As a result, integrated conglomerates may optimally attract higher capital requirements. In contrast, decentralised conglomerates are able to hold assets in the socially most efficient place. Their optimal capital requirements encourage this. Hence, the practice of “regulatory arbitrage”, or of transferring assets from one balance sheet to another, is welfare-increasing. We discuss the policy implications of our finding in the context not only of the present debate on the regulation of financial conglomerates but also in the light of existing US bank holding company regulation

Critical Market Crashes

This review is a partial synthesis of the book ``Why stock market crash'' (Princeton University Press, January 2003), which presents a general theory of financial crashes and of stock market instabilities that his co-workers and the author have developed over the past seven years. The study of the frequency distribution of drawdowns, or runs of successive losses shows that large financial crashes are ``outliers'': they form a class of their own as can be seen from their statistical signatures. If large financial crashes are ``outliers'', they are special and thus require a special explanation, a specific model, a theory of their own. In addition, their special properties may perhaps be used for their prediction. The main mechanisms leading to positive feedbacks, i.e., self-reinforcement, such as imitative behavior and herding between investors are reviewed with many references provided to the relevant literature outside the confine of Physics. Positive feedbacks provide the fuel for the development of speculative bubbles, preparing the instability for a major crash. We demonstrate several detailed mathematical models of speculative bubbles and crashes. The most important message is the discovery of robust and universal signatures of the approach to crashes. These precursory patterns have been documented for essentially all crashes on developed as well as emergent stock markets, on currency markets, on company stocks, and so on. The concept of an ``anti-bubble'' is also summarized, with two forward predictions on the Japanese stock market starting in 1999 and on the USA stock market still running. We conclude by presenting our view of the organization of financial markets.Comment: Latex 89 pages and 38 figures, in press in Physics Report

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