Research

I model the effect of exogenous trading costs for arbitrageurs on liquidity provision and price informativeness in fragmented markets. I use the model to derive a threshold error correction model to identify latent trading costs. The theoretical framework predicts that trading costs hamper arbitrage activity and thus reduce informational efficiency but also adverse selection risk for liquidity providers. I use network activity as a proxy for trading costs and confirm these predictions by analyzing bitcoin orderbook data. The estimated trading costs related to network activity explain 63% of observed price differences. In line with the theoretical predictions, I find that spreads decrease by 2% if network activity increases by 1% after controlling for volume and volatility.
Job Market Paper, 2019.

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.
Journal of Econometrics, Vol 212, Issue 1, 221-240, 2019.

Distributed ledger technologies replace trusted intermediaries with time-consuming consensus protocols to record the transfer of ownership. This settlement latency imposes limits to arbitrage and hinders price discovery. 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 90 basis points, explaining 81% of the observed cross-market price differences. Consistent with our theory, periods of high latency risk exhibit large price differences, while asset flows chase arbitrage opportunities. Decentralized settlement without centralized clearing thus introduces a non-trivial friction that affects market efficiency.
Working Paper, 2018.

Teaching

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