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Assessing VASP Solvency For Cryptoassets: Appendix A. Supplemental materialby@solvency
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Assessing VASP Solvency For Cryptoassets: Appendix A. Supplemental material

by SolvencyJune 20th, 2024
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Most VASPs (N = 14) provide services on cryptoassets of three or more different DLTs. N = 11 VASP provide only one service, and N = 7 offer three different services. The address clustering we implement relies on the multi- input technique discussed in Androulaki et al. (2013); Ron & Shamir (2013)
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Authors:

(1) Pietro Saggese, Complexity Science Hub Vienna (CSH);

(2) Esther Segalla, Oesterreichische Nationalbank (OeNB);

(3) Michael Sigmund, Oesterreichische Nationalbank (OeNB);

(4) Burkhard Raunig, Oesterreichische Nationalbank (OeNB);

(5) Felix Zangerl, Austrian Financial Market Authority (FMA);

(6) Bernhard Haslhofer, Complexity Science Hub Vienna (CSH).

Abstract and 1. Introduction

  1. Background and related literature
  2. VASPs: A Closer Examination
  3. Measuring VASPs Cryptoasset Holdings
  4. Closing The Data Gap
  5. Conclusions, Declaration of Competing Interest, and References

Appendix A. Supplemental material

Appendix A. Supplemental material

In this appendix we report additional information about how many VASPs offer several services or support multiple coins (Figure A.1). Most VASPs (N = 14) provide services on cryptoassets of three or more different DLTs; N = 11 VASPs provide only one service, and N = 7 offer three different services.


Furthermore, we provide further insights on the address clustering technique utilized and on the data gathering procedure. The address clustering we implement relies on the multi-input technique discussed in Androulaki et al. (2013); Ron & Shamir (2013); Meiklejohn et al. (2016). It assumes that the addresses utilized as input in a Bitcoin transaction must be controlled by the same entity. If addresses are reused across transactions, then multiple input addresses can be associated as belonging to the same entity. We note that in this study we computed the VASP balances by analyzing the flows at the level of clusters. We also repeated the analysis at the address level, and found minor inconsistencies most likely due to rounding errors.


Next, we report additional information on the addresses we utilized to identify VASPs. The full list of addresses used is reported in Tables A.1 and A.2. As explained in Section 4.1, we first exploited a collection of tagpacks that associate addresses to the entities controlling them.


Furthermore, to increase our dataset sample, we conducted manual transactions against the VASPs that were already in our sample. This led to the identification of 9 new addresses and their corresponding clusters, that we highlight in light gray in Tables A.1 and A.2.


We further expanded the dataset by conducting transaction pattern analyses of the new collected addresses. We identified 7 additional addresses that follow specific patterns indicating the redirection of funds from temporary addresses to collector wallets. These are highlighted in Tables A.1 and A.2 in darker gray.


Finally, Table A.3 reports the VASP categorization by their service offering according to their online documentation.


Figure A.1: The Austrian VASP landscape. (a) histogram showing how many services VASPs offer; (b) histogram showing how many DLTs VASPs utilize.


Table A.1: List of Bitcoin cluster-defining addresses.


Table A.2: List of Ethereum addresses.


Table A.3: VASPs categorization by their service offering — VASP-specific observations.


This paper is available on arxiv under CC BY 4.0 DEED license.