Designing a Combinatorial Financial Options Market

TL;DR

This paper introduces a novel market mechanism for standard and combinatorial financial options, improving matching efficiency, reducing arbitrage, and enabling complex asset combinations, with empirical validation and computational analysis.

Contribution

It proposes a polynomial-time, risk-free matching mechanism for standard options and extends it to combinatorial options, including an algorithm for optimal matching despite computational complexity.

Findings

Mechanism finds new option matches and reduces bid-ask spreads.

Optimal clearing of combinatorial options is coNP-hard.

Algorithm effectively matches complex options in synthetic markets.

Abstract

Financial options are contracts that specify the right to buy or sell an underlying asset at a strike price by an expiration date. Standard exchanges offer options of predetermined strike values and trade options of different strikes independently, even for those written on the same underlying asset. Such independent market design can introduce arbitrage opportunities and lead to the thin market problem. The paper first proposes a mechanism that consolidates and matches orders on standard options related to the same underlying asset, while providing agents the flexibility to specify any custom strike value. The mechanism generalizes the classic double auction, runs in time polynomial to the number of orders, and poses no risk to the exchange, regardless of the value of the underlying asset at expiration. Empirical analysis on real-market options data shows that the mechanism can find new matches for options of different strike prices and reduce bid-ask spreads. Extending standard options written on a single asset, we propose and define a new derivative instrument -- combinatorial financial options that offer contract holders the right to buy or sell any linear combination of multiple underlying assets. We generalize our single-asset mechanism to match options written on different combinations of assets, and prove that optimal clearing of combinatorial financial options is coNP-hard. To facilitate market operations, we propose an algorithm that finds the exact optimal match through iterative constraint generation, and evaluate its performance on synthetically generated combinatorial options markets of different scales. As option prices reveal the market's collective belief of an underlying asset's future value, a combinatorial options market enables the expression of aggregate belief about future correlations among assets.