Abstract
A stochastic process is a time-dependent random variable. Stochastic processes such as Brownian motion and Ito processes develop in continuous time. This means that time is a real variable that can assume any real value. In many financial modeling applications, however, it is convenient to constrain time to assume only discrete values. A time series is a discrete-time stochastic process; that is, it is a collection of random variables Xi indexed with the integers …–n, … ,–2,–1,0,1,2, …,n, … Keywords: discrete-time stochastic processes; filtration; finite-dimensional distributions; autopredictive model; adjustment models; data generation process; DGP; stationary time series; multivariate time series; correlated random walks; Factor models; cointegrated model; infinite moving-average representation; causal time series
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