Home // FDEF // News // PhD Defense - Essays in Market Microstructure and Financial Markets Stability - Vladimir Levin

PhD Defense - Essays in Market Microstructure and Financial Markets Stability - Vladimir Levin

twitter linkedin facebook email this page
Add to calendar
Event date: Wednesday, 06 July 2022 03:30 pm - 05:30 pm
Place: Campus Kirchberg, Room A02
6, rue Richard Coudenhove-Kalergi
L-1359 Luxembourg

Supervisor: Prof. Dr Roman KRÄUSSL

Title: Essays in Market Microstructure and Financial Markets Stability

The defense will be organised in presential mode on Campus Kirchberg, room B22

Vladimir's thesis abstract 

Chapter 1: Dark Trading and Financial Markets Stability
This paper examines how the implementation of a new dark order - Midpoint Extended Life Order (M-ELO) on Nasdaq - impactsfinancial markets stability in terms of occurrences of mini-flash crashes in individual securities. We use high-frequency order book data and apply panel regression analysis to estimate the effect of dark order tradingactivity on market stability and liquidity provision. The results suggest a predominance of a speed bump effect of M-ELO rather than a darkness effect. We find that the introduction of M-ELO increases market stability by reducing the average number of mini-flash crashes, but its impact on market quality is mixed.

Chapter 2: Dark Pools and Price Discovery in Limit Order Markets
This paper examines how the introduction of a dark pool impacts price discovery, market quality, and aggregatewelfare of traders. I use a four-period model where rational and risk-neutral agents choose the order type and the venue and obtain the equilibrium numerically. The comparative statics on the order submission probability suggests a U-shaped order migration to the dark pool. The overall effect of dark trading on market quality and aggregate welfare was found to be positive but limited insize and depended on market conditions. I find mixed results for the process of price discovery.Depending on the immediacy need of traders, price discovery may change due to the presence of the dark venue.

Chapter 3: Machine Learning and Market Microstructure Predictability and it is another.
This paper illustrates the application of machine learning to market microstructure research. I outline the most insightful microstructure measures, that possess the highest predictive power and are useful for the out-of-sample predictions of such features of the market as liquidity volatility and general market stability. By comparing the models' performance during the normal time versus the crisis time, I come to the conclusion that financial markets remain efficient during both periods.Additionally, I find that high-frequency traders activity is not able to forecast accurately neither of the market features.