Determining the optimal market structure using near-zero intelligence traders

We evaluate an agent-based model featuring near-zero-intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices, which orders are executed as well as a range of parameters regarding market intervention by specialists and the presence of informed traders. We optimize these trading rules using a population-based incremental learning algorithm seeking to maximize the trading volume. Our results suggest markets should choose a large tick size and ensure only a small fraction of traders are informed about the order book. The effect of trading rules regarding the determination of prices, priority rules, and specialist intervention, we find to have an ambiguous effect on the outcome.     

Monetary policy implementation and overnight rate persistence

Overnight money market rates are the predominant operational target of monetary policy. As a consequence, central banks have redesigned the implementation of monetary policy to keep the deviations of the overnight rate from the key policy rate small and short-lived. This paper uses fractional integration techniques to explore how the operational framework of four major central banks affects the persistence of overnight rates. Our results suggest that a well-communicated and transparent interest rate target of the central bank is a particularly important condition for a low degree of overnight rate persistence.