The vast majority of recent studies in market impact assess each product individually, and the interactions between their order flows are disregarded. This strong approximation may lead to an underestimation of trading costs and possible contagion effects. Transactions mediate a significant part...

We analyze a proprietary dataset of trades by a single asset manager, comparing their price impact with that of the trades of the rest of the market. In the context of a linear propagator model we find no significant difference between the two, suggesting that both the magnitude and time...

Using a proprietary dataset of meta-orders and prediction signals, and assuming a quasi-linear impact model, we deconvolve market impact from past correlated trades and a predictable return component to elicit the temporal dependence of the market impact of a single daily meta-order, over a ten...

Latent order book models have allowed for significant progress in our understanding of price formation in financial markets. In particular they are able to reproduce a number of stylized facts, such as the square-root impact law. An important question that is raised -- if one is to bring such...

Financial markets display a host of universal “stylized facts” begging for a scientific explanation: Excess volatility, fat tails, and clustered activity are well known and have been studied for many years. More microstructural stylized facts have recently emerged, for example the long...

The aim of our work is to propose a natural framework to account for all the empirically known properties of the multivariate distribution of stock returns. We define and study a "nested factor model", where the linear factors part is standard, but where the log-volatility of the linear factors...