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Stefan Kitzler

Publications and source records attributed to Stefan Kitzler.

10 recordsLinked to original sources

Tracing Stablecoin Contagion during the USDC Depeg after the Silicon Valley Bank Collapse

The March 2023 collapse of Silicon Valley Bank (SVB) disrupted the core premise of stablecoins, which are digital tokens designed to maintain a fixed value against the U.S. dollar and serve as on-chain substitutes for dollar liquidity. The event triggered a sharp depeg of USDC, creating a rare exogenous shock to the stablecoin ecosystem. While price deviations during this crisis are well documented, the underlying behavioral reorganization of on-chain activity remains less understood. Here, we analyze high-granularity transaction data to measure the shock's effects on network activities, volumes, and prices, reconstructing the contagion pathway from market-wide synchronization down to account-level reallocation. By extracting phase dynamics, we first show that transaction activity across major stablecoins became strongly synchronized during the crisis window, indicating a collective market-level response. We then uncover a bifurcated contagion pathway. While USDT, WBTC, and WETH reacted primarily as liquidity absorption channels with larger trade volumes, only USDC-related assets exhibited immediate price responses alongside surging transaction counts. This reflects the dominant role of USDC-related assets in this incident and their immediate behavioral connection to user panic, driving a mass reallocation from single-coin to multi-coin portfolios. Finally, governed by persistent intraday time-zone rhythms and balance-size heterogeneity, these findings provide a comprehensive empirical framework for understanding systemic risk and flight-to-quality mechanisms in fractional-reserve digital asset networks.

cs.CE

DeFi Yield Aggregators: Analysing Investment Strategies and Structural Dependencies

Yield aggregators are financial services in Decentralised Finance (DeFi) providing automated investment management and return optimisation for users. In this study, we investigate the operational mechanisms and monetary flows of two major yield aggregators, Yearn Finance and Cian, over the period from May 4, 2024 to May 3, 2025. Our supporting conceptual framework decomposes yield aggregator operations into user investment and strategy management cycles. Using a network approach for 2,459 Yearn and 921 Cian transactions, we trace protocol interactions and capital flows across the ecosystem. Users invested 15.7M USD into Yearn's USDC vault, which generated yield through liquidity provision and dynamic allocation across DeFi protocols. Cian, deployed later, attracted 54.0M USD into its staked-ETH (stETH) vault and implemented sophisticated leverage through flashloan-enabled recursive staking. Yearn's USDC vault achieves an annual yield of 5.41%, while Cian's stETH vault produces 4.22% with higher risk exposure. We use the operational insights from our analysis to extend the existing DeFi Stack Reference Model (DSR) with new financial primitives to highlight structural risk dependencies. Overall, our findings show that strategic complexity in yield aggregation does not necessarily translate into higher returns but materially expands risk exposure.

cs.CE

Modern Portfolio Theory in the Crypto-Wilderness

Modern Portfolio Theory (MPT) prescribes how to maximise the return of an asset portfolio for a given level of risk. The optimal trade-off between return and variance defines the efficient frontier. Whether actual cryptoasset portfolios approximate this prescription and whether proximity to the frontier translates into realised performance remain difficult to test at large scale in traditional markets due to their opaque nature and the inaccessibility of data. As we show, public blockchains make these questions measurable: every token transfer is recorded, thus enabling complete portfolio reconstruction for every account at any point in time. We leverage this transparency to reconstruct cryptoasset portfolios for over 116M Ethereum accounts across the full chain history (2015-2025), measure their distance to the constrained efficient frontier, and quantify how deviations translate into realised performance. Here we show that market entry timing, not allocation choice, is the dominant predictor of realised cryptoasset returns. On-chain wealth is highly concentrated and portfolios are pervasively under-diversified, with single-asset holdings accounting for 83.35% of accounts. Two-asset portfolios sit closest to the efficient frontier defined by their held assets, a proximity that reflects the narrowness of their opportunity set rather than deliberate optimisation. Passive market-capitalisation weighting outperforms every MPT optimisation strategy in median realised return, and entry month alone explains 70-79% of the variance in returns, far exceeding the contribution of allocation choice. Mean-variance optimisation therefore appears neither descriptive of observed behaviour nor prescriptively useful in the cryptoasset domain, even if MPT retains its value as a normative benchmark.

cs.CE

Slaying the Dragon: The Quest for Democracy in Decentralized Autonomous Organizations (DAOs)

This chapter explores how Decentralized Autonomous Organizations (DAOs), a novel institutional form based on blockchain technology, challenge traditional centralized governance structures. DAOs govern projects ranging from finance to science and digital communities. They aim to redistribute decision-making power through programmable, transparent, and participatory mechanisms. This chapter outlines both the opportunities DAOs present, such as incentive alignment, rapid coordination, and censorship resistance, and the challenges they face, including token concentration, low participation, and the risk of de facto centralization. It further discusses the emerging intersection of DAOs and artificial intelligence, highlighting the potential for increased automation alongside the dangers of diminished human oversight and algorithmic opacity. Ultimately, we discuss under what circumstances DAOs can fulfill their democratic promise or risk replicating the very power asymmetries they seek to overcome.

cs.CY

The Economic Impact of DeFi Crime Events on Decentralized Autonomous Organizations (DAOs)

The Decentralized Finance (DeFi) ecosystem has experienced over \$10 billion in direct losses due to crime events. Beyond these immediate losses, such events often trigger broader market reactions, including price declines, trading activity changes, and reductions in market capitalization. Decentralized Autonomous Organizations (DAOs) govern DeFi applications through tradable governance assets that function like corporate shares for voting and decision-making. Leveraging DeFi's granular trading data, we conduct an event study on 22 crime events between 2020 and 2022 to assess their economic impact on governance asset prices, trading volumes, and market capitalization. Using a dynamic difference-in-differences (DiD) framework with counterfactual governance assets, we aim for causal inference of intraday temporal effects. Our results show that 55% of crime events lead to significant negative price impacts, with an average decline of about 14%. Additionally, 68% of crime events lead to increased governance asset trading volume. Based on these impacts, we estimate indirect economic losses of over $1.3 billion in DAO market capitalization, far exceeding direct victim costs and accounting for 74% of total losses. Our study provides valuable insights into how crime events shape market dynamics and affect DAOs. Moreover, our methodological approach is reproducible and applicable beyond DAOs, offering a framework to assess the indirect economic impact on other cryptoassets.

cs.CE

Towards Verifiability of Total Value Locked (TVL) in Decentralized Finance

Total Value Locked (TVL) aims to measure the aggregate value of cryptoassets deposited in Decentralized Finance (DeFi) protocols. Although blockchain data is public, the way TVL is computed is not well understood. In practice, its calculation on major TVL aggregators relies on self-reports from community members and lacks standardization, making it difficult to verify published figures independently. We thus conduct a systematic study on 939 DeFi projects deployed in Ethereum. We study the methodologies used to compute TVL, examine factors hindering verifiability, and ultimately propose standardization attempts in the field. We find that 10.5% of the protocols rely on external servers; 68 methods alternative to standard balance queries exist, although their use decreased over time; and 240 equal balance queries are repeated on multiple protocols. These findings indicate limits to verifiability and transparency. We thus introduce ``verifiable Total Value Locked'' (vTVL), a metric measuring the TVL that can be verified relying solely on on-chain data and standard balance queries. A case study on 400 protocols shows that our estimations align with published figures for 46.5% of protocols. Informed by these findings, we discuss design guidelines that could facilitate a more verifiable, standardized, and explainable TVL computation.

q-fin.GN

Mapping the DeFi crime landscape: an evidence-based picture

Decentralized finance (DeFi) has been the target of numerous profit-driven crimes, but the prevalence and cumulative impact of these crimes have not yet been assessed. This study provides a comprehensive assessment of profit-driven crimes targeting the DeFi sector. We collected data on 1141 crime events from 2017 to 2022. Of these, 1036 were related to DeFi (the main focus of this study) and 105 to centralized finance (CeFi). The findings show that the entire cryptoasset industry has suffered a minimum loss of US$30B, with two-thirds related to CeFi and one-third to DeFi. Focusing on DeFi, a taxonomy was developed to clarify the similarities and differences among these crimes. All events were mapped onto the DeFi stack to assess the impacted technical layers, and the financial damages were quantified to gauge their scale. The results highlight that during an attack, a DeFi actor (an entity developing a DeFi technology) can serve as a direct target (due to technical vulnerabilities or exploitation of human risks), as a perpetrator (through malicious uses of contracts or market manipulations), or as an intermediary (by being imitated through, for example, phishing scams). The findings also show that DeFi actors are the first victims of crimes targeting the DeFi industry: 52% of events targeted them, primarily due to technical vulnerabilities at the protocol layer, and these events accounted for 83% of all financial damages. Alternatively, in 41% of events, DeFi actors were themselves malicious perpetrators, predominantly misusing contracts at the cryptoasset layer (e.g. rug pull scams). However, these events accounted for only 17% of all financial damages. The study offers a preliminary assessment of the size and scope of crime events within the DeFi sector and highlights the vulnerable position of DeFi actors in the ecosystem.

cs.CR

Investigating Similarities Across Decentralized Financial (DeFi) Services

We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571); we discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.

q-fin.ST

The Governance of Decentralized Autonomous Organizations: A Study of Contributors' Influence, Networks, and Shifts in Voting Power

We present a study analyzing the voting behavior of contributors, or vested users, in Decentralized Autonomous Organizations (DAOs). We evaluate their involvement in decision-making processes, discovering that in at least 7.54% of all DAOs, contributors, on average, held the necessary majority to control governance decisions. Furthermore, contributors have singularly decided at least one proposal in 20.41% of DAOs. Notably, contributors tend to be centrally positioned within the DAO governance ecosystem, suggesting the presence of inner power circles. Additionally, we observed a tendency for shifts in governance token ownership shortly before governance polls take place in 1202 (14.81%) of 8116 evaluated proposals. Our findings highlight the central role of contributors across a spectrum of DAOs, including Decentralized Finance protocols. Our research also offers important empirical insights pertinent to ongoing regulatory activities aimed at increasing transparency to DAO governance frameworks.

cs.SI

Disentangling Decentralized Finance (DeFi) Compositions

We present a measurement study on compositions of Decentralized Finance protocols, which aim to disrupt traditional finance and offer services on top of distributed ledgers, such as Ethereum. DeFi compositions may impact the development of ecosystem interoperability, are increasingly integrated with web technologies, and may introduce risks through complexity. Starting from a dataset of 23 labeled DeFi protocols and 10,663,881 associated Ethereum accounts, we study the interactions of protocols and associated smart contracts. From a network perspective, we find that decentralized exchanges and lending protocols have high degree and centrality values, that interactions among protocol nodes primarily occur in a strongly connected component, and that known community detection methods cannot disentangle DeFi protocols. Therefore, we propose an algorithm to decompose a protocol call into a nested set of building blocks that may be part of other DeFi protocols. With a ground truth dataset we have collected, we can demonstrate the algorithm's capability by finding that swaps are the most frequently used building blocks. As building blocks can be nested, i.e., contained in each other, we provide visualizations of composition trees for deeper inspections. We also present a broad picture of DeFi compositions by extracting and flattening the entire nested building block structure across multiple DeFi protocols. Finally, to demonstrate the practicality of our approach, we present a case study that is inspired by the recent collapse of the UST stablecoin in the Terra ecosystem. Under the hypothetical assumption that the stablecoin USD Tether would experience a similar fate, we study which building blocks and, thereby, DeFi protocols would be affected. Overall, our results and methods contribute to a better understanding of a new family of financial products.

cs.CR