Gaussian and non-Gaussian processes of zero power variation, and related stochastic calculus
Francesco Russo (UMA), Frederi Viens

TL;DR
This paper studies the power variation of a broad class of stochastic processes, including Gaussian and non-Gaussian types, establishing conditions for their variation to vanish and developing related stochastic calculus tools.
Contribution
It introduces new conditions under which the $m$th power variation of these processes exists and is zero, and extends stochastic calculus to non-stationary Gaussian processes.
Findings
Power variation exists and is zero under certain smoothness conditions.
Includes Gaussian processes like fractional Brownian motion within the framework.
Defines and proves the existence of symmetric integrals for non-stationary Gaussian processes.
Abstract
We consider a class of stochastic processes defined by for , where is a square-integrable continuous martingale and is a deterministic kernel. Let be an odd integer. Under the assumption that the quadratic variation of is differentiable with finite, it is shown that the th power variation exists and is zero when a quantity related to the variance of an increment of over a small interval of length satisfies . When is the Wiener process, is Gaussian; the class…
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Taxonomy
TopicsStochastic processes and financial applications · Financial Risk and Volatility Modeling · Statistical Mechanics and Entropy
