Algorithmic trading has sharply increased over the past decade. Equity market liquidity has improved as well. Are the two trends related? For a recent five-year panel of New York Stock Exchange (NYSE) stocks, we use a normalized measure of electronic message traffic (order submissions,...

We investigate the effects of introducing a central clearing counterparty (CCP) on securities prices by adopting as an experimental construct the 2009 CCP reform in three Nordic markets. We find that, relative to other European economies, these countries experience market-adjusted equity returns...

We develop a new likelihood-based approach to signing trades in the absence of quotes. This approach is equally efficient as the existing Markov-chain Monte Carlo methods, but more than ten times faster. It can address the occurrence of multiple trades at the same time and allows for analysis of...

Speeding up the exchange does not necessarily improve liquidity. The price quotes of high-frequency market makers are more likely to meet speculative high-frequency "bandits", thus less likely to meet liquidity traders. The bid-ask spread is raised in response. The recursive dynamic model...

We study price pressures in stock prices-price deviations from fundamental value due to a risk-averse intermediary supplying liquidity to asynchronously arriving investors. Empirically, twelve years of daily New York Stock Exchange intermediary data reveal economically large price pressures. A...

Signed customer order flow correlates with permanent price changes in equity and nonequity markets. We exploit macro news events in the 30Y treasury futures market to identify causality from customer flow to riskfree rates. We remove the positive feedback trading part and establish that, in the...

We develop a new likelihood-based approach to sign trades in the absence of quotes. It is equally efficient as existing MCMC methods, but more than 10 times faster. It can deal with the occurrence of multiple trades at the same time, and noisily observed trade times. We apply this method to a...

In recent years the Value at Risk (VaR) concept for measuringdownside risk has been widelystudied. VaR basically is a summary statistic that quantifies theexposure of an asset or portfolio tomarket risk, or the risk that a position declines in value withadverse market price changes. Threeparties...