| Daniel Traian PELE |  |
Daniel Traian Pele graduated Mathematics (2000) and got his Master in Stochastic Processes and Theoretical Statistics at the University of Bucharest (2002). He got his Ph.D. in Statistics (2007) and habilitation in Statistics (2019) at the Bucharest University of Economic Studies. He currently serves as a Professor at the Department of Statistics and Econometrics, the Bucharest University of Economic Studies, Romania, teaching Statistics of Financial Markets and Time Series. He is an author of more than 20 scientific publications in internationally refereed journals. The corresponding research profile is that of a data scientist, focused on statistical modelling of financial markets.
He was a postdoctoral researcher at ICMA Centre, Reading University, United Kingdom (2011) and a Guest Researcher at Research Data Center from Department of Statistics, Humboldt University from Berlin (2014) and International Research Training Group 1792 “High Dimensional Nonstationary Time Series”, Humboldt University from Berlin (2018, 2019). He is the co-founder of SAS Centre of Excellence in the Bucharest University of Economic Studies (2009), aiming to use SAS as a platform for analytics with applications in economy and finance. He is also a World Bank and European Investment Bank consultant in Romania and he taught courses of Statistics, Econometrics and SAS for the employees of the Romanian National Bank (BNR), National Statistical Institute (INS) and other financial institutions.
Publication list can be found at: https://scholar.google.ro/citations?user=tN32HYcAAAAJ&hl=en or https://www.researchgate.net/profile/Daniel_Traian_Pele .
Code and documentation can be found on GitHub.
Presentation abstract: Cryptocurrencies became popular in the society the last decade and there are still substantive, unanswered questions on their positioning with respect to other types of assets in the economy.
In this paper we provide insights for the separation of cryptocurrencies from other type of assets, using various statistical methods. Using dimensionality reduction techniques, we show that most of the variation among cryptocurrencies, stocks, exchange rates and commodities can be explained by only a few factors. By applying various classification models, we are able to classify cryptocurrencies as a new asset class with unique features in the tails of their log-returns distribution. The main result is the complete separation of cryptocurrencies from the other asset types, using the Maximum Variance Components Split method. Additionally, we observe a synchronic evolution of cryptocurrencies, compared to the classical assets.
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