Individual light curves of active galactic nuclei ( AGNs ) are nowadays successfully modelled with the damped random walk ( DRW ) stochastic process , characterized by the power exponential covariance matrix of the signal , with the power \beta = 1 .
By Monte Carlo simulation means , we generate mock AGN light curves described by non-DRW stochastic processes ( 0.5 \leq \beta \leq 1.5 and \beta \neq 1 ) and show they can be successfully and well modelled as a single DRW process , obtaining comparable goodness of fits .
A good DRW fit , in fact , may not mean that DRW is the true underlying process leading to variability and it can not be used as a proof for it .
When comparing the input ( non-DRW ) and measured ( DRW ) process parameters , the recovered time-scale ( amplitude ) increases ( decreases ) with the increasing input \beta .
In practice , this means that the recovered DRW parameters may lead to biased ( or even non-existing ) correlations of the variability and physical parameters of AGNs if the true AGN variability is caused by non-DRW stochastic processes .
The proper way of identifying the processes leading to variability are model-independent structure functions and/or power spectral densities and then using such information on the covariance matrix of the signal in light curve modelling .