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将去中心化借贷合约作为期权定价与对冲

文章 arXiv papers · 作者: Lukasz Szpruch et al.
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总结

本研究将去中心化借贷协议合约建模为期权：借款人相当于期权买方，贷款人相当于卖方，贷款价值比则有助于确定权利金。研究使用无套利定价理论分析合约价值，并报告称，在没有市场摩擦或借贷利率差的情况下，最优选择永远是不进入合约。随后，分析纳入利率差和交易成本，这些条件会改变实际定价和对冲问题。

作者开发了一种深度神经网络算法，用于学习外部市场中的策略，以复制借贷合约的收益，包括未被最优行权的合约。此类复制可能对冲贷款人的敞口，并可补充或取代清算机制。当贷款价值比设置不当或市场对风险的定价存在差异时，该框架还可能识别统计套利机会。作者报告了使用历史数据和模拟情景进行的仿真实验。这些结果支持该方法在测试设定中的应用，但描述没有提供绩效数据或实盘协议证据；学习得到的对冲仍取决于市场条件、成本和模型质量。

核心观点

  • 本文将去中心化借贷合约视为期权，借款人买入、贷款人卖出类期权敞口。
  • 初始贷款价值比决定权利金，可用无套利定价理论进行分析。
  • 模型发现，在没有市场摩擦或借贷利率差的情况下，进入合约并非最优选择。
  • 深度神经网络学习外部市场策略，以复制合约收益并对冲贷款人的风险。
  • 贷款价值比定价失当或市场风险定价差异可能带来统计套利机会。

标签

去中心化金融加密资产衍生品定价机器学习套利
全文
# Pricing and hedging of decentralised lending contracts


# Pricing and hedging of decentralised lending contracts









We study the loan contracts offered by decentralised loan protocols (DLPs) through the lens of financial derivatives. DLPs, which effectively are clearinghouses, facilitate transactions between option buyers (i.e. borrowers) and option sellers (i.e. lenders). The loan-to-value at which the contract is initiated determines the option premium borrowers pay for entering the contract, and this can be deduced from the non-arbitrage pricing theory. We show that when there are no market frictions, and there is no spread between lending and borrowing rates, it is optimal to never enter the lending contract. Next, by accounting for the spread between rates and transactional costs, we develop a deep neural network-based algorithm for learning trading strategies on the external markets that allow us to replicate the payoff of the lending contracts that are not necessarily optimally exercised. This allows hedge the risk lenders carry by issuing options sold to the borrowers, which can complement (or even replace) the liquidations mechanism used to protect lenders' capital. Our approach can also be used to exploit (statistical) arbitrage opportunities that may arise when DLP allow users to enter lending contracts with loan-to-value, which is not appropriately calibrated to market conditions or/and when different markets price risk differently. We present thorough simulation experiments using historical data and simulations to validate our approach.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。