- #1
vale
- 3
- 0
This is my first time here, so... Hi everybody!
I've very little time to figure out the following problem ... and I am 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
I've very little time to figure out the following problem ... and I am 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