Saggese, Pietro (2021) Arbitrage in the Bitcoin ecosystem: an investigation of the Mt. Gox exchange platform. Advisor: Facchini, Dr. Angelo. Coadvisor: Dimitri, Prof. Nicola . pp. 230. [IMT PhD Thesis]
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Text (Doctoral thesis)
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Abstract
The purpose of the thesis is to identify and describe the arbitrage activity conducted in the Bitcoin ecosystem at its early stages. This work is the first attempt in the literature to investigate empirically the individual behavior of the arbitrageurs, and provides evidence that they are few and sophisticated. I exploit a dataset containing the history of trades executed within the exchange platform Mt. Gox between 2011 and 2013. I follow and improve upon the established methods to preprocess the data by proposing a new approach whose validity is documented extensively. Crucially, trades are labelled with user specific identifiers, allowing to reconstruct the individual sequences of actions and thus to identify arbitrageurs, and explicit transaction costs are accounted for. The core of the work is thus the implementation of two novel methodologies that aim at identifying the triangular arbitrage activity within the Mt. Gox platform and the two-point arbitrage across Mt. Gox and two counterpart exchanges, Bitstamp and BTC-e. In the former I focus on the mispricings of the bitcoin price denominated in different fiat currencies; in the latter, I compare differences in price - across Mt. Gox and the counterpart exchanges - denominated in the same fiat currency. I classify as arbitrageurs respectively 23 and 49 users, for a total of 72. A comparison of aggregate statistics between arbitrageurs and non arbitrageurs is given and discussed. This work represents the first empirical contribution on arbitrage at the micro level that goes beyond anecdotal evidence: the findings challenge the textbook definition of arbitrage and demonstrate that arbitrage is conducted by a limited number of sophisticated and specialized investors.
| Item Type: | IMT PhD Thesis |
|---|---|
| Subjects: | H Social Sciences > HB Economic Theory |
| PhD Course: | Economics management and data science |
| Identification Number: | https://doi.org/10.13118/imtlucca/e-theses/329 |
| NBN Number: | urn:nbn:it:imtlucca-27453 |
| Date Deposited: | 21 Jul 2021 08:27 |
| URI: | http://e-theses.imtlucca.it/id/eprint/329 |
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