Pricing and Hedging Decentralized Lending Contracts as Options

Summary

This study models decentralized lending protocol contracts as options: borrowers act as option buyers and lenders as sellers, while the loan-to-value ratio helps determine the premium. It uses no-arbitrage pricing theory to analyze contract value and reports that, without market frictions or a lending-borrowing rate spread, the optimal choice is never to enter the contract. The analysis then incorporates rate spreads and transaction costs, conditions that alter the practical pricing and hedging problem.

The authors develop a deep neural network algorithm to learn strategies in external markets that replicate lending-contract payoffs, including contracts that are not optimally exercised. Such replication may hedge lenders’ exposure and could complement or replace liquidation mechanisms. The framework may also identify statistical arbitrage when loan-to-value settings are poorly calibrated or markets price risk differently. The authors report simulation experiments using historical data and simulated scenarios. These results support the approach within the tested settings, but the description gives no performance figures or evidence from live protocols, and learned hedges remain dependent on market conditions, costs, and model quality.

Key ideas

• The paper treats decentralized lending contracts as options, with borrowers buying and lenders selling the option-like exposure.
• Loan-to-value at initiation determines a premium that can be analyzed using no-arbitrage pricing theory.
• Without market frictions or a spread between lending and borrowing rates, the model finds that entering the contract is not optimal.
• A deep neural network learns external-market strategies to replicate contract payoffs and hedge lenders’ risk.
• Mispriced loan-to-value settings or differences in market risk pricing may create statistical arbitrage opportunities.

Tags

# 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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.