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Stochastic) Process {Xt, T ∈ T } Is a Collection of Random Variables on the Same Probability Space (Ω, F,P

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Stochastic) Process {Xt, T ∈ T } Is a Collection of Random Variables on the Same Probability Space (Ω, F,P Random Processes Florian Herzog 2013 Random Process Definition 1. Random process A random (stochastic) process fXt; t 2 T g is a collection of random variables on the same probability space (Ω; F;P ). The index set T is usually represen- ting time and can be either an interval [t1; t2] or a discrete set. Therefore, the stochastic process X can be written as a function: X : R × Ω ! R; (t; !) 7! X(t; !) Stochastic Systems, 2013 2 Filtration Observations: • The amount of information increases with time. • We use the concept of sigma algebras. • We assume that information is not lost with increasing time • Therefore the corresponding σ-algebras will increase over time when there is more information available. • ) This concept is called filtration. Definition 2. Filtration/adapted process A collection fFtgt≥0 of sub σ-algebras is called filtration if, for every s ≤ t, we have Fs ⊆ Ft. The random variables fXt : 0 ≤ t ≤ 1g are called adapted to the filtration Ft if, for every t, Xt is measurable with respect to Ft. Stochastic Systems, 2013 3 Filtration Example 1. Suppose we have a sample space of four elements: Ω = f!1;!2;!3;!4g. At time zero, we don't have any information about which T f g ! has been chosen. At time 2 we know wether we will have !1;!2 or f!3;!4g. r D = f!1g rB r E = f!2g r A r r F = f!3g C r G = f!4g - t T 0 2 T Abbildung 1: Example of a filtration Stochastic Systems, 2013 4 Difference between Random process and smooth functions Let x(·) be a real, continuously differentiable function defined on the interval [0;T ]. Its continuous differentiability implies both a bounded total variation and a vanishing \sum of squared increments": 1. Total variation: Z T dx(t) dt < 1 0 dt 2. \Sum of squares": N ( ( ) ( )) X T T 2 lim x k − x (k−1) = 0 N!1 N N k=1 Random processes do not have either of these nice smoothness properties in general. This allows the desired \wild" and \random" behavior of the (sample) \noise signals". Stochastic Systems, 2013 5 Markov Process A Markov process has the property that only the instantaneous value X(t) is relevant for the future evolution of X. Past and future of a Markov process have no direct relation. Definition 3. Markov process A continuous time stochastic process X(t); t 2 T; is called a Markov process if for any finite parameter set fti : ti < ti+1g 2 T we have P (X(tn+1) 2 BjX(t1);:::;X(tn)) = P (X(tn+1) 2 BjX(tn)) Stochastic Systems, 2013 6 Transition probability Definition 1. Let X(t) be a Markov process. The function P (s; X(s); t; B) is the conditional probability P (X(t) 2 B j X(s)) called transition proba- bility or transition function. This means it is the probability that the process X(t) will be found inside the area B at time t, if at time s < t it was observed at state X(s). Stochastic Systems, 2013 7 Transition probability Geometric Brownian motion with µ=0.08 σ=0.2 50 45 40 35 30 B 25 x(t) 20 15 10 X s 5 t s s+t 0 0 5 10 15 20 25 30 35 40 45 50 t Abbildung 2: Transition probability P (s; x; t + s; B). Stochastic Systems, 2013 8 Gaussian Process • A stochastic process is called Gaussian if all its joint probability distributions are Gaussian. • If X(t) is a Gaussian process, then X(t) ∼ N (µ(t); σ2(t)) for all t. • A Gaussian process is fully characterized by its mean and covariance function. • Performing linear operations on a Gaussian process still results in a Gaussian process. • Derivatives and integrals of Gaussian processes are Gaussian processes themselves (note: stochastic integration and differentiation). Stochastic Systems, 2013 9 Martingale Process A stochastic process X(t) is a martingale relative to (fFtgt≥0;P ) if the following conditions hold: • X(t) is fFtgt≥0-adapted, E[jX(t)j] < 1 for all t ≥ 0. • E[X(t)jFs] = X(s) a.s. (0 ≤ s ≤ t). • The best prediction of a martingale process is its current value. • Fair game model Stochastic Systems, 2013 10 Diffusions A diffusion is a Markov process with continuous trajectories such that for each time t and state X(t) the following limits exist 1 µ(t; X(t)) := lim E[X(t + ∆t) − X(t)jX(t)]; ∆t#0 ∆t 1 σ2(t; X(t)) := lim E[fX(t + ∆t) − X(t)g2jX(t)]: ∆t#0 ∆t For these limits, µ(t; X(t)) is called drift and σ2(t; X(t)) is called the diffusion coefficient. Stochastic Systems, 2013 11.
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