Models Comparison and Strategy Analysis of Futures Option Pricing and Arbitrage

Abstract

Futures options are important derivative instruments in modern financial markets, playing a crucial role in risk management, price discovery, speculation and arbitrage. Scientific and accurate pricing is not merely related to investors' trading decisions, but directly affects the effectiveness of arbitrage strategies and the operational efficiency of the market. After reviewing relevant theories, this paper conducts a comprehensive comparison of several typical futures option pricing models, including the Black-Scholes model, binomial tree model and Monte Carlo method, stochastic volatility model (Heston model), GARCH-type models, and machine learning models. These models have both advantages and disadvantages in terms of pricing accuracy, applicable scenarios, and complexity. The operation of Black-Scholes model is simple and effective, but it neglects the dynamic characteristics of volatility. Stochastic volatility models and GARCH-type models can well explain the volatility smile, while machine learning methods have potential in prediction and nonlinear modeling. Furthermore, this paper introduces common arbitrage strategies based on the no-arbitrage principle, analyzes the arbitrage opportunities brought by model errors, and discusses the impact of transaction costs and liquidity constraints on arbitrage strategies. Through theoretical comparison and strategy analysis, it is argued that the rational selection and combination of pricing models is the key to improving the feasibility of arbitrage strategies, which provides reference significance for both the research and practice of the futures option market.