{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/317599"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/317599","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"ARBITRAGE STRATEGIES IN PERPETUAL FUTURES AND STOCK INDEX FUTURES","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.","abstract_html":"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. 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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. 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