Further analysis of the speed of response to large trades in interest rate futures

Abstract

This study examines the adjustment process in the interest rate futures market following large block trades, by analyzing changes in the levels of quoted prices, bid-ask spreads, and trading activity. Most of the adjustment in prices and spreads is complete within 12 quote revisions (approximately 70 seconds). Results suggest that block trades stimulate subsequent trading activity, as traders rush to express differences of opinion about the price implication of the block. The market response to block trades exhibits several features in common with the two-phase

Testing Stylized Facts of Bitcoin Limit Order Books

“…We fit an exponential decay functions(τ) ∼ exp(−τ/T) to estimate the decay time constant as T ≈ 8.3 s (corresponding to a half-life of T 1/2 ≈ 5.8 s). Surprisingly, this is well in line with the finding of Cummings and Frino (2010) for large block trades of interest rate futures at the Sydney Futures Exchange: Considering only the largest block trades, they find that excess bid-ask spreads after the largest block purchases recover to a normal level on a time scale of approximately 7 s. They find that recovery is faster for the largest trades. Similarly, Large (2007) estimates a half-life time of about 20 s for a stock at the London Stock Exchange.…”

“…Another class of studies covers the concept of market resiliency, which we define in the sense of Foucault et al (2005) and Gomber et al (2015) as the recovery of market liquidity following liquidity shocks, and which is considered to be one of the main characteristics of liquid markets (Black 1971;Kyle 1985). We follow several previous studies (compare, for example, Degryse et al (2005), Large (2007), Cummings and Frino (2010) and Gomber et al (2015)) and analyze the evolution of market liquidity around large trades in a conditional event study: We condition the analysis onto a set of selected events E C , which is given by the set of trades with exceptionally large volume, and transform the timestamp t of a LOB state L(t) to the event time scale τ of event e, i.e., calculate τ = t − t e . We then consider the average avg of a measure f over all LOB states that are a time difference τ away from a selected trade e ∈ E C :…”

Liquidity Dynamics in an Electronic Open Limit Order Book: an Event Study Approach

“…(), De Winne and d'Hondt () and Wuyts () analyse the impact of large trades on depth and the quoted spread. Cummings and Frino () perform a similar analysis for futures markets. Large () assesses resiliency using a continuous time impulse response function framework while Kempf et al .…”

Deep reinforcement learning for the optimal placement of cryptocurrency limit orders

“…5 A rule of thumb is that permanent price impact is insignificant as long as the daily participation rate is below 10 percent (Almgren and Chriss, 2001, p. 24). Limit order markets are typically found to possess a relatively high resilience of liquidity, i.e., liquidity levels revert to their average within a short period of time (Degryse et al, 2005;Cummings and Frino, 2010;Gomber et al, 2015). For cryptocurrency limit order markets, Schnaubelt et al (2019) similarly find that liquidity close to the bid-ask spread recovers within a few seconds after trades in the top percentile of trading volume.…”