m*********g
发帖数: 646 | 1 1) Let X and Y be two gaussian random variables with N(0,a) and N(0,b),
respectively. X and Y are correlated with correlation \rho.
What is E (X − Y |2X + Y ) ?
2) For American option, when risk free interest rate increase, will it
increase the possibility of early excises?
(related question: When would people early exercise the American put option?
Increase of
interest rate ?)
3) \sum (N from 0 to infinity) N^2*a^N. a = constant
4) Which method is the best to price the long-term American put option?
Lattice Models ? Binomial/Trinomial Tree (can adjust the change of interest
rate and dividend payments at the nodes).
5) For some reason we can't price the one-year American put.
But we have the prices of 1-month, 3-month, 6 month and 1-year European put
options avaialbe.
Should the price of the one-year American put equal to the highest of the 1-
month, 3-month, 6 month and 1-year European put
options?
6) x follows f(x), y follows g(y), x y independent, x+y follow what ?
7) an Asian forward contract, price it using replication
9) what's the algorithm to generate normal r.v. and exponential r.v.?
10) let let X~Normal(0,1), F(x) is CDF of X, Y~Normal(mu, sigma), what is E(
F(y))
11) Give me the price of derivatives which pays log(S(T))S(T), you can
assume
S(T) follow GBM. How to get it more efficiently?
12) what is ln(-n) | c**********e
发帖数: 2007 | | m*********g
发帖数: 646 | 3 why peng?... I think people here are better than on Wilmott on average, in
terms of solving interview questions. | c****o
发帖数: 1280 | 4 You can find answer to these questions in the OLD posts.
【在 m*********g 的大作中提到】
: why peng?... I think people here are better than on Wilmott on average, in
: terms of solving interview questions.
| m*********g
发帖数: 646 | 5 oops.. Didn't check. The op from wilmott said they are recent interview
questions.
【在 c****o 的大作中提到】
: You can find answer to these questions in the OLD posts.
| x**********2
发帖数: 3 | 6 what is ln(-n)? negative number in log has meaning? | x******a
发帖数: 6336 | 7 may consider complex number.
【在 x**********2 的大作中提到】
: what is ln(-n)? negative number in log has meaning?
| p******e
发帖数: 756 | 8 有大牛能给看看第5个和第7个么。thx~~
5) For some reason we can't price the one-year American put.
But we have the prices of 1-month, 3-month, 6 month and 1-year European put
options avaialbe.
Should the price of the one-year American put equal to the highest of the 1-
month, 3-month, 6 month and 1-year European put
options?
7) an Asian forward contract, price it using replication
option?
【在 m*********g 的大作中提到】
: 1) Let X and Y be two gaussian random variables with N(0,a) and N(0,b),
: respectively. X and Y are correlated with correlation \rho.
: What is E (X − Y |2X + Y ) ?
: 2) For American option, when risk free interest rate increase, will it
: increase the possibility of early excises?
: (related question: When would people early exercise the American put option?
: Increase of
: interest rate ?)
: 3) \sum (N from 0 to infinity) N^2*a^N. a = constant
: 4) Which method is the best to price the long-term American put option?
| S****h
发帖数: 558 | 9 How to do the first one? X-Y and 2X+Y are not joint-normal as indicated in
my other post. | EM
发帖数: 715 | 10 Is the result to Q1 (sqrt(a)-sqrt(b))/(2sqrt(a)+sqrt(b))*(2X+Y)
My solution is to find a linear combination of X and Y such that its
covariance with 2X+Y is zero, so independent of 2X+Y
An example is sqrt(b)X-sqrt(a)Y
then write X-Y in terms of 2X+Y and sqrt(b)X-sqrt(a)Y
in
【在 S****h 的大作中提到】
: How to do the first one? X-Y and 2X+Y are not joint-normal as indicated in
: my other post.
| S****h
发帖数: 558 | 11 Your method works if X and Y are joint-normal, which is not necessarily true
but needed to make further
calculation. Check my other post about further discussion.
【在 EM 的大作中提到】
: Is the result to Q1 (sqrt(a)-sqrt(b))/(2sqrt(a)+sqrt(b))*(2X+Y)
: My solution is to find a linear combination of X and Y such that its
: covariance with 2X+Y is zero, so independent of 2X+Y
: An example is sqrt(b)X-sqrt(a)Y
: then write X-Y in terms of 2X+Y and sqrt(b)X-sqrt(a)Y
:
: in
|
|
