  Accurate modeling of the temporal evolution of asset prices is crucial for
understanding financial markets. We explore the potential of discrete-time
quantum walks to model the evolution of asset prices. Return distributions
obtained from a model based on the quantum walk algorithm are compared with
those obtained from classical methodologies. We focus on specific limitations
of the classical models, and illustrate that the quantum walk model possesses
great flexibility in overcoming these. This includes the potential to generate
asymmetric return distributions with complex market tendencies and higher
probabilities for extreme events than in some of the classical models.
Furthermore, the temporal evolution in the quantum walk possesses the potential
to provide asset price dynamics.
