Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 13th May 2024, 04:57:48pm EDT
A Model to Quantify the Risk of Cross-Product Manipulation: Evidence from the European Government Bond Futures Market
Alexis Stenfors1, Kaveesha Dilshani2, Andy Guo2, Peter Mere3
1University of Portsmouth, United Kingdom; 2University of Technology Sydney (UTS), Australia; 3Macquarie University, Australia
Discussant: Douglas Cumming (Florida Atlantic University)
Cross-product manipulation involves manipulating one financial product to profit from the subsequent reaction in a different but related product. In this paper, we develop a simple model that researchers and regulators can use to scan for the susceptibility of two markets to such misconduct. We also test the model empirically on a set of government bond futures contracts using a complete EUREX ultra-high-frequency dataset. Our findings show that cross-product manipulation is feasible across bond futures with different underlying maturities, issuers and contract expiry dates. The results suggest that cross-product manipulation might be widespread despite an increasing crackdown by regulators and prosecutors.
Does High Frequency Market Manipulation Harm Market Quality?
Jonathan Brogaard1, Dan Li2, Jeffrey Yang1
1University of Utah, United States of America; 2Chinese University of Hong Kong, Shenzhen
Discussant: Jeff Harris (American University)
Manipulation of financial markets has long been a concern. With the automation of financial markets, the potential for high frequency market manipulation has arisen. Yet, such behavior is hidden within vast sums of order book data, making it difficult to define and to detect. We develop a tangible definition of one type of manipulation, spoofing. Using proprietary user level identified order book data, we show the determinants of spoofing. Exploiting lagged spoofing profitability and SEC Litigation Releases as instruments, we show causal evidence that spoofing increases volatility and adverse selection, and decreases price efficiency. The findings indicate that spoofing harms market quality.
