A discrete-time quantum walk model is proposed to generate and characterize asymmetric and bimodal probability distributions for long-term financial asset returns.
Journal of Political Economy81(3), 637–654 (1973)
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For Gamma-family fits to NYSE volume-price data the shape parameter follows diffusive mean-reverting dynamics while the scale parameter shows dominant jump-diffusion with elevated higher moments, and jumps explain a large share of variance; the log-normal model reverses the pattern.
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Characterizing asymmetric and bimodal long-term financial return distributions through quantum walks
A discrete-time quantum walk model is proposed to generate and characterize asymmetric and bimodal probability distributions for long-term financial asset returns.
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Jump-diffusion models of parametric volume-price distributions
For Gamma-family fits to NYSE volume-price data the shape parameter follows diffusive mean-reverting dynamics while the scale parameter shows dominant jump-diffusion with elevated higher moments, and jumps explain a large share of variance; the log-normal model reverses the pattern.