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Register a new userCeFi vs. DeFi -- Comparing Centralized to Decentralized Finance
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To non-experts, the traditional Centralized Finance (CeFi) ecosystem may seem obscure, because users are typically not aware of the underlying rules or agreements of financial assets and products. Decentralized Finance (DeFi), however, is making its debut as an ecosystem claiming to offer transparency and control, which are partially attributable to the underlying integrity-protected blockchain, as well as currently higher financial asset yields than CeFi. Yet, the boundaries between CeFi and DeFi may not be always so clear cut. In this work, we systematically analyze the differences between CeFi and DeFi, covering legal, economic, security, privacy and market manipulation. We provide a structured methodology to differentiate between a CeFi and a DeFi service. Our findings show that certain DeFi assets (such as USDC or USDT stablecoins) do not necessarily classify as DeFi assets, and may endanger the economic security of intertwined DeFi protocols. We conclude this work with the exploration of possible synergies between CeFi and DeFi.
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Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial system, is mushrooming. One year ago the total value locked in DeFi systems was approximately 700m USD, now, as of April 2021, it stands at around 51bn USD. The frenetic evolution of the ecosystem makes it challenging for newcomers to gain an understanding of its basic features. In this Systematization of Knowledge (SoK), we delineate the DeFi ecosystem along its principal axes. First, we provide an overview of the DeFi primitives. Second, we classify DeFi protocols according to the type of operation they provide. We then go on to consider in detail the technical and economic security of DeFi protocols, drawing particular attention to the issues that emerge specifically in the DeFi setting. Finally, we outline the open research challenges in the ecosystem.
Financial speculators often seek to increase their potential gains with leverage. Debt is a popular form of leverage, and with over 39.88B USD of total value locked (TVL), the Decentralized Finance (DeFi) lending markets are thriving. Debts, however, entail the risks of liquidation, the process of selling the debt collateral at a discount to liquidators. Nevertheless, few quantitative insights are known about the existing liquidation mechanisms. In this paper, to the best of our knowledge, we are the first to study the breadth of the borrowing and lending markets of the Ethereum DeFi ecosystem. We focus on Aave, Compound, MakerDAO, and dYdX, which collectively represent over 85% of the lending market on Ethereum. Given extensive liquidation data measurements and insights, we systematize the prevalent liquidation mechanisms and are the first to provide a methodology to compare them objectively. We find that the existing liquidation designs well incentivize liquidators but sell excessive amounts of discounted collateral at the borrowers expenses. We measure various risks that liquidation participants are exposed to and quantify the instabilities of existing lending protocols. Moreover, we propose an optimal strategy that allows liquidators to increase their liquidation profit, which may aggravate the loss of borrowers.
The trustless nature of permissionless blockchains renders overcollateralization a key safety component relied upon by decentralized finance (DeFi) protocols. Nonetheless, factors such as price volatility may undermine this mechanism. In order to protect protocols from suffering losses, undercollateralized positions can be liquidated. In this paper, we present the first in-depth empirical analysis of liquidations on protocols for loanable funds (PLFs). We examine Compound, one of the most widely used PLFs, for a period starting from its conception to September 2020. We analyze participants behavior and risk-appetite in particular, to elucidate recent developments in the dynamics of the protocol. Furthermore, we assess how this has changed with a modification in Compounds incentive structure and show that variations of only 3% in an assets dollar price can result in over 10m USD becoming liquidable. To further understand the implications of this, we investigate the efficiency of liquidators. We find that liquidators efficiency has improved significantly over time, with currently over 70% of liquidable positions being immediately liquidated. Lastly, we provide a discussion on how a false sense of security fostered by a misconception of the stability of non-custodial stablecoins, increases the overall liquidation risk faced by Compound participants.
In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy. Specifically, we discuss the prediction of financial crashes as well as dynamic portfolio optimization. We comment on the different types of quantum strategies to carry on these optimizations, such as those based on quantum annealers, universal gate-based quantum processors, and quantum-inspired Tensor Networks.
Supply chains lend themselves to blockchain technology, but certain challenges remain, especially around invoice financing. For example, the further a supplier is removed from the final consumer product, the more difficult it is to get their invoices financed. Moreover, for competitive reasons, retailers and manufacturers do not want to disclose their supply chains. However, upstream suppliers need to prove that they are part of a `stable supply chain to get their invoices financed, which presents the upstream suppliers with huge, and often unsurmountable, obstacles to get the necessary finance to fulfil the next order, or to expand their business. Using a fictitious supply chain use case, which is based on a real world use case, we demonstrate how these challenges have the potential to be solved by combining more advanced and specialised blockchain technologies with other technologies such as Artificial Intelligence. We describe how atomic crosschain functionality can be utilised across private blockchains to retrieve the information required for an invoice financier to make informed decisions under uncertainty, and consider the effect this decision has on the overall stability of the supply chain.
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