With a bang, not a whimper: Pricking Germany's "stock market bubble" in 1927 and the slide into depression

In May 1927, the German central bank intervenedindirectly to reduce lending to equity investors.The crash that followed ended the only stockmarket boom during Germany s relative stabilization 1924-28. This paper examines thefactors that lead to the intervention as well asits consequences. We argue that genuine concernabout the exuberant level of the stock market,in addition to worries about an inflow offoreign funds, tipped the scales in favour ofintervention. The evidence strongly suggeststhat the German central bank under HjalmarSchacht was wrong to be concerned aboutstockprices-there was no bubble. Also, theReichsbank was mistaken in its belief thata fall in the market would reduce theimportance of short-term foreign borrowing,and help to ease conditions in the money market.The misguided intervention had important realeffects. Investment suffered, helping to tipGermany into depression.

The evolution of conventions under incomplete information

We formulate an evolutionary learning process in the spirit ofYoung (1993a) for games of incomplete information. The process involves trembles. For many games, if the amount of trembling is small, play will be in accordance with the games' (semi-strict) Bayesian equilibria most of the time. This supports the notion of Bayesian equilibrium. Further, often play will most of the time be in accordance with exactly one Bayesian equilibrium. This gives a selection among the Bayesian equilibria. For two specific games of economic interest wecharacterize this selection. The first is an extension to incomplete information of the prototype strategic conflict known as Chicken. The second is an incomplete information bilateral monopoly, which is also an extension to incompleteinformation of Nash's demand game, or a simple version ofthe so-called sealed bid double auction. For both gamesselection by evolutionary learning is in favor of Bayesianequilibria where some types of players fail to coordinate, such that the outcome is inefficient.

Local volatility changes in the black-scholes model

In this paper we address a problem arising in risk management; namely the study of price variations of different contingent claims in the Black-Scholes model due to anticipating future events. The method we propose to use is an extension of the classical Vega index, i.e. the price derivative with respect to the constant volatility, in thesense that we perturb the volatility in different directions. Thisdirectional derivative, which we denote the local Vega index, will serve as the main object in the paper and one of the purposes is to relate it to the classical Vega index. We show that for all contingent claims studied in this paper the local Vega index can be expressed as a weighted average of the perturbation in volatility. In the particular case where the interest rate and the volatility are constant and the perturbation is deterministic, the local Vega index is an average of this perturbation multiplied by the classical Vega index. We also study the well-known goal problem of maximizing the probability of a perfect hedge and show that the speed of convergence is in fact dependent of the local Vega index.