Conference Agenda

Please note that all times are shown in the time zone of the conference. The current conference time is: 1st Nov 2024, 12:14:46am CET

 
 
Session Overview
Session
MM 06: Man or Machine?
Time:
Saturday, 19/Aug/2023:
11:30am - 1:00pm

Session Chair: Andreas Park, University of Toronto
Location: 2A-24 (floor 2)


Show help for 'Increase or decrease the abstract text size'
Presentations
ID: 2112

HFTs and Dealer Banks: Liquidity and Price Discovery in FX Trading

Wenqian Huang1, Peter O'Neill2, Angelo Ranaldo3, Shihao Yu4

1Bank for International Settlements; 2University of New South Wales; 3University of St. Gallen; 4Vrije Universiteit Amsterdam

Discussant: Chen Yao (The Chinese University of Hong Kong)

In this paper, we characterise the liquidity provision and price discovery roles of dealers and HFTs in the FX spot market during the sample period between 2012 and 2015. We find that they have different responses to adverse market conditions: HFT liquidity provision is less sensitive to spikes in market-wide volatility, while dealer bank liquidity is more robust ahead of scheduled macroeconomic news announcements when adverse selection risk is high. In periods of extreme levels of volatility, such as the ‘Swiss De-peg’ event in our sample, HFTs appear to withdraw almost all liquidity while dealers remain. In normal times, we also find that HFTs contribute to market liquidity by passively trading against the pricing errors created by dealers’ aggressive trade flows. On price discovery, HFTs contribute the dominant share, mostly through their high-frequency quote updates which incorporate public information. In contrast, dealers contribute to price discovery more through trades that impound private information.

EFA2023_2112_MM 06_1_HFTs and Dealer Banks.pdf


ID: 1982

Algorithmic Pricing and Liquidity in Securities Markets

Jean-Edouard Colliard, Thierry Foucault, Stefano Lovo

HEC Paris, France

Discussant: Yajun Wang (Baruch College)

We let “Algorithmic Market-Makers” (AMs), using Q-learning algorithms, choose prices for a risky asset when their clients are privately informed about the asset payoff. We find that AMs learn to cope with adverse selection and to update their prices after observing trades, as predicted by economic theory. However, in contrast to theory, AMs charge a mark-up over the competitive price, which declines with the number of AMs. Interestingly, markups tend to decrease with AMs’ exposure to adverse selection. Accordingly, the sensitivity of quotes to trades is stronger than that predicted by theory and AMs’ quotes become less competitive over time as asymmetric information declines.

EFA2023_1982_MM 06_2_Algorithmic Pricing and Liquidity in Securities Markets.pdf


ID: 717

Relationship Discounts in Corporate Bond Trading

Simon Jurkatis2, Andreas Schrimpf1, Karamfil Todorov1, Nick Vause2

1Bank for International Settlements, Switzerland; 2Bank of England

Discussant: Marco Rossi (Texas A&M Unversity)

We find that clients with stronger past trading relationships with a dealer receive consistently better prices in corporate bond trading. The top 1% of relationship clients face a sizeable 67% drop in transaction costs relative to the median client - an effect which is particularly strong during the COVID-19 turmoil. We find clients' liquidity provision to be a key driver of relationship discounts: clients to whom balance-sheet constrained dealers can turn to as a source of liquidity, are rewarded with relationship discounts. Another important motive for dealers to quote better prices to relationship clients is because these clients generate the bulk of dealers' profits. Finally, we find no evidence that extraction of information from clients' order flow is related to relationship discounts.

EFA2023_717_MM 06_3_Relationship Discounts in Corporate Bond Trading.pdf


 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: EFA 2023
Conference Software: ConfTool Pro 2.6.151+TC
© 2001–2024 by Dr. H. Weinreich, Hamburg, Germany