Research

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 (forthcoming), 2018.

We analyze how stochastic latency in the transaction settlement process, as introduced by distributed ledger systems, affects cross-market arbitrage. The time-consuming settlement process exposes arbitrageurs to price risk and imposes limits to arbitrage. We derive arbitrage bounds imposed by stochastic latency and show that larger price differences are consistent with higher expected latency, higher uncertainty in latency, or higher volatility. We parametrize stochastic latency in the Bitcoin network and estimate boundaries for high-frequency orderbook data from several exchanges. Stochastic latency explains about 94 % of observed price differences adjusted for transaction costs.
Working Paper (coming soon), 2018.

Teaching

I am/was teaching instructor for the following courses:

  • Supervision of Bachelor Thesis
  • Teaching Introduction to R for Financial Econometrics
  • Gutman Private Wealth Management Seminar: Portfolio Choice
  • Teaching Revision Course in Asset Management: Portfolio Choice and CAPM
  • Instructor at Perm Sumer School on Blockchain & Cryptomarkets on Blockchain Market Microstructure [Slides]

Notes for Bachelor students (Crypto CAPM - An application to … ):

  • Data for your Thesis. The zipped file contains three elements

    1. crix_returns.csv. Contains a time series of daily returns of the CRIX index ranging from January 2015 until February 2018.
    2. crypto_returns.csv. A file with time series of daily returns (based on closing prices) of 30 crypocurrencies. The data was downloaded from Coinmarketcap.com. Note: Your Thesis only requires you to work with one of the time series, you do not have to analyze all 30!
    3. ff3_returns.csv. A file with daily returns of the market portfolio (minus the risk free rate), the small-minus-big factor, the high-minus-low factor returns and the risk free rate. The data was downloaded from Kenneth Frenchs data library.
  • Sample Code to run the analysis. The script contains (documented) R-Code which extracs the data and performs some simple analysis for the Bitcoin. You can use and adjust this file on your computer to replicate (and extend) the analysis based on your assigned asset.

  • Slides from the Kick-off Meeting.

  • Style guide (template) for Bachelor Thesis. Latex template for your Bachelor Thesis created by my colleague Christoph Scheuch and me.

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