ARBITRAGE STRATEGIES IN PERPETUAL FUTURES AND STOCK INDEX FUTURES
type: CreativeWork
description: This thesis examines two aspects of arbitrage in financial markets. The first part analyzes arbitrage in perpetual futures, which track underlying prices through a funding swap mechanism. We show that the clamping function embedded in the mechanism—previously overlooked in the literature—creates inherent no-arbitrage bounds that persist even in the absence of transaction fees. Using two years of Binance data, we empirically confirm the validity of these model-free bounds. The second part studies arbitrage strategies in stock index futures under a reinforcement learning (RL) framework. We formulate the problem as an exploratory stochastic control problem with entropy-regularized rewards, transforming the optimal switching structure into a standard control setting. An interpretable learning algorithm is developed, and a policy improvement theorem is established. Simulations and empirical results demonstrate the effectiveness of the proposed RL approach, which is broadly applicable to optimal switching problems.
abstract: This thesis examines two aspects of arbitrage in financial markets. The first part analyzes arbitrage in perpetual futures, which track underlying prices through a funding swap mechanism. We show that the clamping function embedded in the mechanism—previously overlooked in the literature—creates inherent no-arbitrage bounds that persist even in the absence of transaction fees. Using two years of Binance data, we empirically confirm the validity of these model-free bounds. The second part studies arbitrage strategies in stock index futures under a reinforcement learning (RL) framework. We formulate the problem as an exploratory stochastic control problem with entropy-regularized rewards, transforming the optimal switching structure into a standard control setting. An interpretable learning algorithm is developed, and a policy improvement theorem is established. Simulations and empirical results demonstrate the effectiveness of the proposed RL approach, which is broadly applicable to optimal switching problems.
author: LI LINFENG
keywords: reinforcement learning, stock index futures, optimal switching, clamp function, funding swap, perpetual futures
license: https://scholarbank.nus.edu.sg/bitstreams/131abb8d-5888-452f-87d5-b1085de5556a/download
date published: 2025-08-20
date modified: 2026-07-24T03:32:30Z
provider: National University of Singapore
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: National University of Singapore