Hi EdisonCruise,

what is the meaning of [$]\nu^{*}[$] in the paper?

- May 11th, 2019, 5:29 am
- Forum: Numerical Methods Forum
- Topic: Why the minimum is taken here when its derivative > 0?
- Replies:
**2** - Views:
**678**

Hi EdisonCruise,

what is the meaning of [$]\nu^{*}[$] in the paper?

what is the meaning of [$]\nu^{*}[$] in the paper?

- August 3rd, 2018, 7:10 am
- Forum: Technical Forum
- Topic: Does Cov(X,I{Y>K}) = Cov(X,Y|Y=K)f_Y(K) hold in general?
- Replies:
**4** - Views:
**534**

Well, your first equation holds in general only for a non-negative random variable Y and the integral should go from 0 to +infinity . And the second equation does not hold at all (unless X and Y are uncorrelated), since the term Cov(X,Y|Y=K) would always be zero. One of the ways to think about i...

- June 18th, 2018, 3:41 pm
- Forum: Student Forum
- Topic: Are these two RVs independent?
- Replies:
**15** - Views:
**998**

I think, unfortunately neither of the statements can be proved for general \(x_t\). Consider a trivial example of any deterministic process \(x_t\). Then the random variables \(Z_T\) and \(Y_T\) are independent (and hence the expectation of the product of these random variables becomes zero due to \...

- June 18th, 2018, 8:47 am
- Forum: Student Forum
- Topic: Are these two RVs independent?
- Replies:
**15** - Views:
**998**

Okay, in that case \( Y_T \) can be dependent on the whole information till time \(T \) by definition. Here, some simple example, \(\int_1^2B_tdt\) is not independent of \(B_{0.5} \).

- June 18th, 2018, 6:26 am
- Forum: Student Forum
- Topic: Are these two RVs independent?
- Replies:
**15** - Views:
**998**

What do you actually mean by E_T and E_t? Is it the conditional expectation given all the information generated by W_s till the time T and t, respectively?

- January 30th, 2018, 8:10 am
- Forum: Technical Forum
- Topic: Compound Poisson Process
- Replies:
**4** - Views:
**555**

The second equation (1.23) is simply the differential notation of the integral form. The first one (1.22) is due to the fact that a Poisson process only changes when it jumps, hence d π ( s ) is zero everywhere except when the jump occurs, then it is 1. Thus, the integral simply becomes the sum of...

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