Banks use trading as a vehicle to take risk. Using high-frequency regulatory data, we estimate the sensitivity of weekly bank trading profits to aggregate equity, fixed-income, credit, currency, and commodity risk factors. Our estimates imply that U.S. banks had large trading exposures to equity market risk before the Volcker Rule, which they curtailed afterwards. Credit and currency risk exposures were smaller. The results hold up in a quasi-natural experiment that exploits the phased-in introduction of reporting requirements for identification. Total trading-book risk was also curtailed afterwards with material financial stability implications and no evidence of migration to other bank activities.

We study dealers’ bidding behavior in the Bank of England’s quantitative easing (QE) reverse auctions. Using a granular dataset on both accepted and rejected offers together with an equilibrium model of bidding behavior, we estimate dealers’ valuations of securities offered to the Bank of England. We also recover the rents accruing to dealers from participating in the auctions as opposed to liquidating gilts in the secondary market, thereby possibly causing prices to change. These rents or so-called ”liquidity benefits” are largest in the early phases of QE implemented during the Global Financial Crisis, suggesting that QE may be particularly effective in restoring smooth market functioning when market participants are facing large liquidity shocks. Finally, we document that dealers’ valuations vary significantly with the amount of interest rate risk acquired in the secondary gilt market before the auction and with dealers’ regulatory capital.

We present evidence that several popular low-frequency measures of effective spread suffer from a volatility-induced bias and that volatility is the primary driver of the variation of these liquidity proxies. Using data for U.S. equities and major foreign exchange rates, we show that the bias arises when the effective spread is small relative to volatility. We document that the bias has become more acute over time and show that volatility-biased measures fail to replicate some well-known results in empirical finance. We conclude by providing guidance on the choice of low-frequency measures in empirical applications.

This paper estimates the intraday value of money implicit in the UK unsecured overnight money market. Using transactions data on overnight loans advanced through the UK large value payments system CHAPS in 2003–2009, we find a positive and economically significant intraday interest rate. While the implicit intraday interest rate is quite small pre-crisis, it increases more than tenfold during the financial crisis of 2007–2009. The key interpretation is that an increase in implicit intraday interest rate reflects the increased opportunity cost of pledging collateral intraday and can be used as an indicator to gauge the stress of the payment system. We obtain qualitatively similar estimates of the intraday interest rate by using quoted intraday bid and offer rates and confirm that our results are not driven by the intraday variation in the bid-ask spread.

We study the evolution of the price discovery process in the euro-dollar and dollar-yen currency pairs over a ten-year period on the EBS platform, a global trading venue used by both manual and automated traders. We find that the importance of market orders decreases sharply over that period, owing mainly to a decline in the information share from manual trading, while the information share of market orders from algorithmic and high-frequency traders remains fairly constant. At the same time, there is a substantial, but gradual, increase in the information share of limit orders. Price discovery also becomes faster, suggesting improvements in market efficiency. The results are consistent with theoretical predictions that with a lower information advantage, informed traders tend to use more limit orders.

This paper studies the dynamics of volatility transmission between Central European (CE) currencies and the EUR/USD foreign exchange using model-free estimates of daily exchange rate volatility based on intraday data. We formulate a flexible yet parsimonious parametric model in which the daily realized volatility of a given exchange rate depends both on its own lags as well as on the lagged realized volatilities of the other exchange rates. We find evidence of statistically significant intra-regional volatility spillovers among the CE foreign exchange markets. With the exception of the Czech and, prior to the recent turbulent economic events, Polish currencies, we find no significant spillovers running from the EUR/USD to the CE foreign exchange markets. To measure the overall magnitude and evolution of volatility transmission over time, we construct a dynamic version of the Diebold–Yilmaz volatility spillover index and show that volatility spillovers tend to increase in periods characterized by market uncertainty.

Using unique transactions data for individual high-frequency trading (HFT) firms in the U.K. equity market, we examine the extent to which the trading activity of individual HFT firms is correlated with each other and the impact on price efficiency. We find that HFT order flow, net positions, and total volume exhibit significantly higher commonality than those of a comparison group of investment banks. However, intraday HFT order flow commonality is associated with a permanent price impact, suggesting that commonality in HFT activity is information based and so does not generally contribute to undue price pressure and price dislocations.