The codes underlying my publications are available via my GitHub profile, , or on request. Please do not hesitate to reach out! For selected projects I program R-packages out of the codes/methods/algorithms developed in the papers. For a short description of the R-packages, see the Software page.


  1. Kim, A., S. Trimborn, W.K. Härdle (2021) VCRIX – a volatility index for crypto-currencies, International Review of Financial Analysis, 78, 101915,

  2. Okhrin, O., S. Trimborn, M. Waltz (2021) gofCopula: Goodness-of-Fit tests for copulae, The R-Journal, 13:1, 467-498,

  3. Petukhina, A., S. Trimborn, W.K. Härdle, and H. Elendner (2021) Investing with Cryptocurrencies – Evaluating their Potential for Portfolio Allocation Strategies, Quantitative Finance, 1-29,

  4. Chen, Y., P. Guidici, B. Hadji Misheva, S. Trimborn (2020) Detecting Lead Behavior in Crypto Networks Risks, 8(1), 4,

  5. Trimborn, S., M. Li and W.K. Härdle (2019) Investing with Cryptocurrencies – A Liquidity Constraint Investment Approach Journal of Financial Econometrics, 18 (2), 280-306,

  6. Trimborn, S. and W.K. Härdle (2018) CRIX an Index for cryptocurrencies Journal of Empirical Finance, 49, 107-122,

  7. Elendner, H., S. Trimborn, B. Ong and T.M. Lee (2017) The Cross-Section of crypto-currencies as financial assets Handbook of Digital Finance and Financial Inclusion: Cryptocurrency, FinTech, InsurTech, and Regulation. Ed. by D. Lee Kuo Chuen and R. Deng. Vol. 1. Elsevier,

Discussion Papers

  1. Trimborn, S., Y. Chen, R.-B. Chen (2020) TriSNAR: A Three-Layer Sparse Estimator for Large-Scale Network AutoRegressive Models,

  2. Trimborn, S. , Y. Chen, and J. Zhang (2021) Discover Regional and Size Effects in Bitcoin Blockchain via Sparse-Group Network AutoRegressive Modeling,

  3. Trimborn, S. , Y. Li (2021) Informative Effects of Expert Sentiment on the Return Predictability of Cryptocurrency,