When examining information flow into prices, empirical literature usually focusses on direct conduits such as order submissions. Meanwhile, theory suggests that market conditions should have substantial additional effects. Empirical analyses of such effects are methodologically challenging and...

The COVID-19 pandemic has caused some of the largest - and fastest - market dislocations in modern history. Contemporaneous with the significant fall in equity market values is the evaporation of market liquidity. We show that transactions costs increase sharply in a coordinated fashion across...

Errors in variables in linear regression continue to be a major empirical issue in financial econometrics. We propose a method using the characteristic function (CF) to obtain estimates for linear models with errors in the variables. By assuming that the explanatory variable follows a flexible...

We use a novel machine learning approach to tackle the problem of limit order management. Applying our framework to data, we show that the most important variable for a trader to consider is the price level of their order, followed by the queue sizes of the order book, volatility and finally...

In this paper, we show that vector auto-regression (VAR) models, which are commonly used to estimate permanent price impact, are misspecified and can produce conflicting and incorrect inferences when the price impact function is nonlinear. We propose an alternative method to estimate permanent...