Subordinator (mathematics)

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In probability theory, a subordinator is a stochastic process that is non-negative and whose increments are stationary and independent.[1] Subordinators are a special class of Lévy process that play an important role in the theory of local time.[2] In this context, subordinators describe the evolution of time within another stochastic process, the subordinated stochastic process. In other words, a subordinator will determine the random number of "time steps" that occur within the subordinated process for a given unit of chronological time. In order to be a subordinator a process must be a Lévy process[3] It also must be increasing, almost surely,[3] or an additive process.[4]

Definition

A subordinator is a real-valued stochastic process X=(Xt)t0 that is a non-negative and a Lévy process.[1] Subordinators are the stochastic processes X=(Xt)t0 that have all of the following properties:

  • X0=0 almost surely
  • X is non-negative, meaning Xt0 for all t
  • X has stationary increments, meaning that for t0 and h>0, the distribution of the random variable Yt,h:=Xt+hXt depends only on h and not on t
  • X has independent increments, meaning that for all n and all t0<t1<<tn , the random variables (Yi)i=0,,n1 defined by Yi=Xti+1Xti are independent of each other
  • The paths of X are càdlàg, meaning they are continuous from the right everywhere and the limits from the left exist everywhere

Examples

The variance gamma process can be described as a Brownian motion subject to a gamma subordinator.[3] If a Brownian motion, W(t), with drift θt is subjected to a random time change which follows a gamma process, Γ(t;1,ν), the variance gamma process will follow:

XVG(t;σ,ν,θ):=θΓ(t;1,ν)+σW(Γ(t;1,ν)).

The Cauchy process can be described as a Brownian motion subject to a Lévy subordinator.[3]

Representation

Every subordinator X=(Xt)t0 can be written as

Xt=at+0t0xΘ(dsdx)

where

The measure μ is called the Lévy measure of the subordinator, and the pair (a,μ) is called the characteristics of the subordinator.

Conversely, any scalar a0 and measure μ on (0,) with max(x,1)μ(dx)< define a subordinator with characteristics (a,μ) by the above relation.[5][1]

References

  1. 1.0 1.1 1.2 Kallenberg, Olav (2002). Foundations of Modern Probability (2nd ed.). New York: Springer. pp. 290. 
  2. Kallenberg, Olav (2017). Random Measures, Theory and Applications. Switzerland: Springer. pp. 651. doi:10.1007/978-3-319-41598-7. ISBN 978-3-319-41596-3. 
  3. 3.0 3.1 3.2 3.3 Applebaum, D.. "Lectures on Lévy processes and Stochastic calculus, Braunschweig; Lecture 2: Lévy processes". University of Sheffield. pp. 37–53. http://www.applebaum.staff.shef.ac.uk/Brauns2notes.pdf. 
  4. Li, Jing; Li, Lingfei; Zhang, Gongqiu (2017). "Pure jump models for pricing and hedging VIX derivatives". Journal of Economic Dynamics and Control 74. doi:10.1016/j.jedc.2016.11.001. 
  5. Kallenberg, Olav (2002). Foundations of Modern Probability (2nd ed.). New York: Springer. pp. 287. 




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