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I've very little time to figure out the following problem ... and Im wandering if some of you can give me any help or just suggest me any good reading material...

The question is how you can prove a process [tex] P_t[/tex], given the dynamics, is Markov.

In short my process is on alternate intervals, a

**mean reverting brownian bridge**[tex]dP_t = \frac{\alpha}{G-t}(Q-P_t)dt + \sigma dW_t [/tex], and a

**mean reverting proportional volatility**process : [tex]dP_t = K(\theta -P_t)dt + \nu dW_t [/tex]. The length of the intervals and their occurence is determined by an exogenous bootstrap procedure, which I believe, doesn't give any problems, being a resampling procedure with replacement, it doesn't generate any dependence with the past history...

How should I procede on your opinion? Any hints ?

Thank you very much in advance,

Vale