Stefan Voigt

PhD Candidate in Finance

Vienna Graduate School of Finance

I am currently a doctoral candidate at the Vienna Graduate School of Finance and will join the Department of Economics at the University in Copenhagen as a tenure-track assistant professor in finance from August 2020.

I have a deep interest in the economic implications and evolution of blockchain-based settlement in financial markets. I pursue research questions related to market fragmentation, high frequency trading and big data in financial applications. My research is thus anchored in the intersection of market microstructure, asset pricing and financial econometrics.

You can find my current research on SSRN and arXiv.


  • Blockchain Technology
  • Big Data in Finance
  • Financial Econometrics


  • PhD in Finance, exp. 2020

    Vienna University of Economics and Business

  • MSc in Mathematical Finance, 2015

    University of Konstanz


Liquidity and Price Informativeness in Blockchain-Based Markets

Blockchain-based markets impose substantial costs on cross-market trading due to the decentralized and time-consuming settlement process. I quantify the impact of the time-consuming settlement process in the market for Bitcoin on arbitrageurs activity. The estimation rests on a novel threshold error correction model that exploits the notion that arbitrageurs suspend trading activity when arbitrage costs exceed price differences. I estimate substantial arbitrage costs that explain 63% of the observed price differences, where more than 75% of these costs can be attributed to the settlement process. I also find that a 10 bp decrease in latency-related arbitrage costs simultaneously results in a 3 bp increase of the quoted bid-ask spreads. I reconcile this finding in a theoretical model in which liquidity providers set larger spreads to cope with high adverse selection risks imposed by increased arbitrage activity. Consequently, efforts to reduce the latency of blockchain-based settlement might have unintended consequences for liquidity provision. In markets with substantial adverse selection risk, faster settlement may even harm price informativeness.

Building Trust Takes Time: Limits to Arbitrage in Blockchain-Based Markets

Distributed ledger technologies replace trusted clearing counterparties and security depositories with time-consuming consensus protocols to record the transfer of ownership. This settlement latency exposes cross-market arbitrageurs to price risk. We theoretically derive arbitrage bounds that increase with expected latency, latency uncertainty, volatility and risk aversion. Using Bitcoin orderbook and network data, we estimate arbitrage bounds of on average 121 basis points, explaining 91% of the observed cross-market price differences. Consistent with our theory, periods of high latency-implied price risk exhibit large price differences, while asset flows chase arbitrage opportunities. Blockchain-based settlement thus introduces a non-trivial friction that impedes arbitrage activity.

Large Scale Portfolio Optimization under Transaction Costs and Model Uncertainty

We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized versions of efficient allocations. The regulatory effect of transaction costs is studied in an econometric setting incorporating parameter uncertainty and optimally combining predictive distributions resulting from high-frequency and low-frequency data. In an extensive empirical study, we illustrate that turnover penalization is more effective than commonly employed shrinkage methods and is crucial in order to construct empirically well-performing portfolios.


Some of my lecture notes for students:


Connect Four - Deep Reinforcement Learning

Bellman Equation outside of Finance. A short report on my first Kaggle Competition

LobsteR - Analysing a Decade of High-Frequency Trading

A short series of posts on handling high-frequency data from Lobster and R

LobsteR - NASDAQ under a "tidy" Microscope

A short series of posts on handling high-frequency data from Lobster and R