Network Models & Complex Systems
In this project network models are developed for the structure identification within complex systems/networks. The goal is an accurate identification of the important network nodes. The models are suited to uncover the lead-lag relationship between the flow of funds, company networks, asset return relationship, etc. In this project the TriSNAR and SGNAR models were developed and the papers study the geographical lead-lag relationship between blockchain transactions as well as Bitcoin pricing series on various exchanges all over the globe.
Trimborn, S., Y. Chen, and J. Zhang (2021) Discover Regional and Size Effects in Bitcoin Blockchain via Sparse-Group Network AutoRegressive Modeling, https://dx.doi.org/10.2139/ssrn.3245031
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., 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
Textual Analysis in Digital Markets
In this project text data are analysed for their sentiment and the corresponding effect on asset returns. Topic Modeling is performed to uncover the shared topics market participants discuss and their dynamic relation to the market state. At the moment the project focuses upon Twitter data and the effect of experts on market returns and market states.
Trimborn, S. , Y. Li (2021) Informative Effects of Expert Sentiment on the Return Predictability of Cryptocurrency, http://dx.doi.org/10.2139/ssrn.3834279
Investing with Cryptocurrencies
In several papers investment methods for the cryptocurrency market are developed and/or tested, as well as the relationship of cryptocurrencies with established assets is investigated. Cryptocurrencies have little correlation with establisted assets, which makes them an attractive asset for portfolio diversification. Though the high volatility and frequent jumps also adds considerable risk to a portfolio when investing with cryptocurrencies. Besides the market risk, cryptocurrencies are often also subject to low liquidity, which adds another layer of risk. An investment methodology which incorporates market risk (volatility/quantiles) and liquidity risk is developed, called LIBRO.
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
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 Code: GitHub
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 Code: GitHub
CRIX - a CRyptocurrency IndeX
The market of cryptocurrencies changes its structure frequently. New coins arise on a daily level, while many coins are no longer used after some time. This destiny is shared by small as well as previously large coins. The construction of indices is challenged by the frequently changing market structure. In this project a methodology for a cryptocurrency index, CRIX, is developed as well as for a Volatility CRIX, called VCRIX. Both indices are updates every 5 minutes on the website: https://thecrix.de/ . The underlying engine for the website is the R-package IndexConstruction.
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
Kim, A., S. Trimborn, W.K. Härdle (2019) VCRIX – a volatility index for crypto-currencies , http://dx.doi.org/10.2139/ssrn.3480348