We present a framework for describing the evolution of stochastic observables having a nonstationary distribution of values. The framework is applied to empirical volume-prices from assets traded at the New York Stock Exchange, about which several remarks are pointed out from our analysis. Using Kullback-Leibler divergence we evaluate the best model out of four biparametric models commonly used in the context of financial data analysis. In our present data sets we conclude that the inverse $\mathrm{\Gamma }$ distribution is a good model, particularly for the distribution tail of the largest volume-price fluctuations. Extracting the time series of the corresponding parameter values we show that they evolve in time as stochastic variables themselves. For the particular case of the parameter controlling the volume-price distribution tail we are able to extract an Ornstein-Uhlenbeck equation which describes the fluctuations of the highest volume-prices observed in the data. Finally, we discuss how to bridge the gap from the stochastic evolution of the distribution parameters to the stochastic evolution of the (nonstationary) observable and put our conclusions into perspective for other applications in geophysics and biology.

• Received 29 July 2015
• Revised 29 December 2015

DOI:https://doi.org/10.1103/PhysRevE.93.052122