Since the end of the 1960s, price limits has been employed by many futures markets and stock markets around the world. In the literature, the effectiveness of price limits is still under debate. The main purpose of price limit is to reduce price volatility. The rationale for supporting price...

The stock market crash in 1929 has raised many discussions about the causes and the ways to prevent the financial markets from large fluctuations. The role of the margin loan has usually been regarded as the source of instability in financial markets. The view that low margin infused excessive...

The last decade has witnessed an explosive growth in the use of the internet. Not only the number of users has dramatically increased, but also the amount and quality of the information displayed online has improved. The information available to web users is limited mostly by technological...

Smith and Sørenson (2002) depart from Bikhchandani, Hirshleifer and Welsh’s (1999) herding framework in three ways; non-discrete signals, noise and multiple rational agent types. They refer to the standard model of a single type with no noise as a “herding†model, and differentiate...

Many models of monetary policy predict a trade-off between inflation and output variance despite compelling evidence that the Federal Reserve has become more aggressive in fighting inflation and there has been a resulting decline in both inflation and output variance. We address this apparent...

This paper studies the impact of monetary policy transparency on economic stability, when economic agents are boundedly rational. I first consider a simple class of microfunded general equilibrium models with nominal rigidities and learning. Under a transparent monetary regime, market...

This paper examines the effects of uncertainty through dynamic learning about the firm's project value in the real options framework. We extend the real options framework with incomplete information by allowing an unobserved state variable that drives profits to follow a stochastic process with...

We develop a learning rule that generalises the well known fading memory learning in the sense that the weights attached to the available time series data are not constant and are updated in light of the prediction error(s). The underlying idea is that confidence in the available data will be...