Francis Breedon, Louisa Chen, Angelo Ranaldo and Nicholas Vause
Most academic studies find that algorithmic trading improves the quality of financial markets in normal times by boosting market liquidity (so larger trades can be executed more quickly at lower cost) and enhancing price efficiency (so market prices better reflect all value-relevant information). But what about in times of market stress? In a recent paper looking at the removal of the Swiss franc cap, we find that algorithmic trading provided less liquidity than usual, at worse prices, and that its contribution to efficient pricing dropped to near zero. Market quality benefits from a diversity of participants pursuing different trading strategies, but it seems this was undermined in this episode by commonalities in the way algorithms responded.
Continue reading “Algos all go?”
Olga Cielinska, Andreas Joseph, Ujwal Shreyas, John Tanner and Michalis Vasios
The Bank of England has now access to transaction-level data in over-the-counter derivatives (OTCD) markets which have been identified to lie at the centre of the Global Financial Crisis (GFC) 2007-2009. With tens of millions of daily transactions, these data catapult central banks and regulators into the realm of big data. In our recent Financial Stability Paper, we investigate the impact of the de-pegging in the euro-Swiss franc (EURCHF) market by the Swiss National Bank (SNB) in the morning of 15 January 2015. We reconstruct detailed trading and exposure networks between counterparties and show how these can be used to understand unprecedented intraday price movements, changing liquidity conditions and increased levels of market fragmentation over a longer period.
Continue reading “Big Data jigsaws for Central Banks – the impact of the Swiss franc de-pegging”