Laurs Røndbøll Leth forsvarer sin ph.d.-afhandling ved Økonomisk Institut
Kandidat: Laurs Randbøll Leth
Titel: "Essays on High-Frequency Market Microstructure: Herding and Volume-Synchronized Probability of Informed Trading". Det vil være muligt før forsvaret at rekvirere en kopi af afhandlingen ved henvendelse til Receptionen (26.0.20), Økonomisk Institut.
Tid og sted
23. september 2019 kl. 14:30, Københavns Universitet, CSS, Øster Farimagsgade 5, 1353 København K, bygning 4, lokale 4.2.26. Af hensyn til kandidaten lukkes dørene præcis.
Professor Anders Rahbek, Økonomisk Institut, Københavns Universitet, Danmark (formand)
Professor Antonio Guarino, Økonomisk Institut, University College London, UK
Professor David Easley, Cornell University, USA
This thesis consists of three self-contained chapters within financial market microstructure. The focal point is the modeling and implications of information-based trading in financial markets affected by high-frequency traders.
Chapter 1 (“Rational Herding During a Stock Crash“) examines the extent of herd behavior in a stock market from 2005 to 2008. I consider an asymmetric information model with event uncertainty. This setting enables rational statistical herding to occur with positive probability. The model is fitted to the financial tick data of a NYSE traded stock, and findings reveal that the proportion of herd sellers increased during the Great Recession. This suggests that herd behavior may provide a part of the explanation of financial crises.
Chapter 2 (“Delta Hedging and the VPIN“) investigates how portfolio hedging can take advantage of a microstructure measure (VPIN) of toxic order flow. Suppose a portfolio manager with a short position in a European call option attempts to collect its volatility risk premium by engaging in dynamic delta hedging of the underlying asset. The results show that VPIN signals large intraday price movements leading to losses of the hedging portfolio. Conditional on high VPIN readings, the portfolio manager is advised to either expand her portfolio with VIX futures and/or adjust her daily risk exposure.
Finally, Chapter 3 (“Maximum Likelihood Estimation of VPIN: Toxic Order Flow and Warning Signals“) investigates if maximum likelihood estimation of the VPIN will improve its predictive power for short-term return volatility. This assessment is based on the classification of true and false positive events after high VPIN values were detected. Compared to the method of moment estimation, I show that maximum likelihood estimation is superior in terms of a lower false discovery rate.“