A general approach for lookback option pricing under Markov models

Gongqiu Zhang and Lingfei Li

Quantitative Finance, 2023, vol. 23, issue 9, 1305-1324

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.

Date: 2023
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DOI: 10.1080/14697688.2023.2230254

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