Infinitesimal generators of bridged stochastic process

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Discussion Overview

The discussion revolves around deriving the infinitesimal generator for a bridged gamma process within the context of stochastic control problems. Participants explore various methodologies and theoretical frameworks related to stochastic processes, particularly focusing on the challenges associated with this specific generator.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant seeks guidance on deriving the infinitesimal generator for a bridged gamma process, indicating a specific application in stochastic control.
  • Another participant expresses uncertainty about the nature of the generator, questioning whether it is an infinitesimal delta or operator related to a stochastic process.
  • A formal definition of the infinitesimal generator is provided, highlighting its action on test functions and the limit involving expected values.
  • There is acknowledgment that deriving the generator for a bridged gamma process is more complex than for general Ito or Lévy processes, suggesting the presence of a missing trick.
  • One participant suggests exploring a more tractable example, such as the Brownian bridge, and proposes using the forward and backward Kolmogorov equations to approach the problem.
  • A detailed methodology for deriving the generator of the Brownian bridge is shared, emphasizing the need for bespoke steps tailored to the specific process.
  • Another participant elaborates on the idea of using conditional transition densities and differentiating with respect to variables, referencing the Kolmogorov equations.

Areas of Agreement / Disagreement

Participants do not reach a consensus, as there are varying levels of understanding and differing approaches to the problem. Some express uncertainty about the methods discussed, while others propose specific techniques without agreement on their effectiveness.

Contextual Notes

The discussion reflects limitations in the participants' knowledge regarding the intricacies of the bridged gamma process and the specific requirements for deriving its infinitesimal generator. There are also unresolved mathematical steps and dependencies on definitions that may affect the proposed methodologies.

river_rat
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I hope someone can put me on the right track here. I need to derive the infinitesimal generator for a bridged gamma process and have come a bit stuck (its for a curve following stochastic control problem - don't ask). Any tips, papers, books that could guide me out of my hole would be greatly appreciated.

RR
 
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Hey river_rat and welcome to the forums.

I don't know the ianswer to your question, but I also don't fully follow it either.

Is this generator some kind of infinitesimal delta or operator that generates a specific stochastic process?
 
Hi chiro

The formal definition is the operator [itex]\mathcal{L}[/itex] where acts on [itex]\mathcal{C}^{2, 1}[/itex] test functions so that [itex]\mathcal{L} f(x, t) = \lim_{h\rightarrow 0^+} \frac{\mathbb{E}(f(X_{t+h}) | X_t = x) - f(x)}{h}[/itex].

For general ito processes or levy processes it is easy to find, but for a bridged gamma process there is some trick I seem to be missing as I know you can do this in closed form.
 
I wish I could you out but this is beyond my current knowledge and skill set.
 
Have you tried a more tractable example yet, such as the Brownian bridge?

I haven't checked the details but perhaps you could apply the forward and backward Kolmogorov equations to the conditional joint distribution. From there it wouldn't be too difficult to modify with jump terms.
 
Hi bpet

The methodology I know for the brownian bridge goes as follows: first prove the Brownian bridge is a gaussian process, then find an equivalent process that is adapted to the original filtration generated by your brownian motion and that is a scaled ito integral. Then using ito's lemma on this new scaled ito integral you can arrive at the infinitesimal generator of the brownian bridge.

However, each of those steps are rather bespoke for the process at hand, especially the form of the scaled ito integral required.

I am interested on your forward and backward equation idea, care to elaborate?
 
The idea was to write the (conditional) transition density as [itex]\frac{f(t,u,x,y)f(u,v,y,z)}{f(t,v,x,z)}[/itex] and differentiate wrt u with the Kolmogorov equations. Does that help?
 

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