The codes underlying my publications are available via my GitHub profile, https://github.com/SimonTrimborn , 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.
Trimborn, S., H. Peng, and Y. Chen (2024) Influencer detection meets network autoregression - Influential regions in the bitcoin blockchain, Journal of Empirical Finance, 78, 101529, https://doi.org/10.1016/j.jempfin.2024.101529
Kim, A., S. Trimborn, W.K. Härdle (2021) VCRIX – a volatility index for crypto-currencies, International Review of Financial Analysis, 78, 101915, https://doi.org/10.1016/j.irfa.2021.101915
Okhrin, O., S. Trimborn, M. Waltz (2021) gofCopula: Goodness-of-Fit tests for copulae, The R-Journal, 13:1, 467-498, https://doi.org/10.32614/RJ-2021-060
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, https://doi.org/10.1080/14697688.2021.1880023
Chen, Y., P. Guidici, B. Hadji Misheva, S. Trimborn (2020) Detecting Lead Behavior in Crypto Networks Risks, 8(1), 4, https://doi.org/10.3390/risks8010004
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, doi.org/10.1093/jjfinec/nbz016
Trimborn, S. and W.K. Härdle (2018) CRIX an Index for cryptocurrencies Journal of Empirical Finance, 49, 107-122, https://doi.org/10.1016/j.jempfin.2018.08.004
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, https://doi.org/10.1016/B978-0-12-810441-5.00007-5
Zhang, K. and S. Trimborn (2024) Influential assets in Large-Scale Vector AutoRegressive Models, https://dx.doi.org/10.2139/ssrn.4619531
Trimborn, S. and I. Zwetsloot (2024) Reddit users unleashed-understanding user behaviour and their impact on meme stocks, https://ssrn.com/abstract=4886074
Wang, Z., S. Hao, I. Zwetsloot and S. Trimborn (2024) Social Network Datasets on Reddit Financial Discussion, https://arxiv.org/abs/2410.05002
Hui, Y., I. Zwetsloot, S. Trimborn, and S. Rudinac (2024) Domain-Informed Negative Sampling Strategies for Dynamic Graph Embedding in Meme Stock-Related Social Networks, https://arxiv.org/abs/2411.00606
Trimborn, S., Y. Chen, R.-B. Chen (2020) TriSNAR: A Three-Layer Sparse Estimator for Large-Scale Network AutoRegressive Models, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3573336
Trimborn, S. and L. Yu (2022) Blockchain meets network analytics: a tale of heuristics, location and fraud detection, https://dx.doi.org/10.2139/ssrn.4096814
Trimborn, S. , Y. Li (2021) Informative Effects of Expert Sentiment on the Return Predictability of Cryptocurrency, http://dx.doi.org/10.2139/ssrn.3834279