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A general approach for lookback option pricing under Markov models

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  • Gongqiu Zhang
  • Lingfei Li

Abstract

We propose a computationally efficient method for pricing various types of lookback options under Markov models. We utilize the model-free representations of lookback option prices as integrals of first passage probabilities. We combine efficient numerical quadrature with continuous-time Markov chain approximation for the first passage problem to price lookbacks. Our method is applicable to a variety of models, including one-dimensional time-homogeneous and time-inhomogeneous Markov processes, regime-switching models and stochastic local volatility models. We demonstrate the efficiency of our method through various numerical examples.

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  • Gongqiu Zhang & Lingfei Li, 2023. "A general approach for lookback option pricing under Markov models," Quantitative Finance, Taylor & Francis Journals, vol. 23(9), pages 1305-1324, September.
    • Handle: RePEc:taf:quantf:v:23:y:2023:i:9:p:1305-1324
    DOI: 10.1080/14697688.2023.2230254
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