361 reputation
17
bio website guseynovrv.wordpress.com
location Moscow, Russia
age 31
visits member for 1 year, 8 months
seen yesterday

My area of interests includes programming (.Net, Java), corporate finance (CFA harterholder) and quantitative finance (CQF candidate).


Nov
11
comment converting US tickers into Reuters RIC
Looks like that's the case. Unfortunately, I can't say for sure, but I'd suggest you to try.
Nov
1
comment From Fourier Transforms to Option Values
+1 nice article
Oct
25
comment Risk Neutral Probability
Why do two probability measures differ? Because of the way they are constructed. Do you ask why risk-neutral measure is constucted in a different way then real-world measure? Or why it is constructed at all?
Oct
17
comment SDE simulation: P or Q?
You only need Brownian motion.
Oct
16
comment Bond duration as estimation to holding return
Any link to where you found such estimate?
Aug
16
comment how to extend lognormal model so that $\sigma$ is correlated to $\mu$?
What do you mean saying mu(t) is correlated to sigma(t)? They are determenistic functions, there can only be case when sigma = f(mu(t)), something you have already mentioned.
Aug
13
comment How popular is the IRR as a tool for capital budgeting, nowadays?
And that is why managerial experience (in case their decisions are not biased by their personal interest) might be more sound than any of these stupid metrics
Aug
13
comment How popular is the IRR as a tool for capital budgeting, nowadays?
Some Monte-Carlo simulation of the project might look more helpful. But such models are highly susceptible to model risk (i.e. such models use unrealistic assumptions and thus might be even more deceptive)
Aug
13
comment How popular is the IRR as a tool for capital budgeting, nowadays?
I don't know how to answer. Simple answer would be: Yes, it should, but not always happens. Then, if we do not account for any possible corruption (which is of course impossible!), even in this case I am sure that no metric can be a good forecast of possible project outcome. None of them (IRR, NPV) accounts for risks well.
Jun
27
comment How to numerically obtain delta?
in this case it's hard for me to tell anything without looking at actual code and values
Jun
27
comment How to numerically obtain delta?
But basically I'd suggest you to check if your black-scholes price coincides with Matlab's blsprice.
Jun
27
comment How to numerically obtain delta?
That's really interesting. I never used gradient function to calculate derivatives manually. In case C(:) is vector of call prices and S(:) is vector of spot prices, I calculate delta numerically like this: Delta = diff(C)./diff(S).
Jun
26
comment How to numerically obtain delta?
I can only imagine option with non-smooth value, like barrier option close to the barrier. In this case numerical derivative might give result very different from analytical one. Could it be your case?
Jun
21
comment Applicability of PCA to get historical volatilities to calibrate interest rates trees
3) I completely agree with you that first eigenvector would not give a great estimate, it will explain 20-40% of all variability of the curve. But! Maybe it is still better then simple historical volatilities?
Jun
21
comment Applicability of PCA to get historical volatilities to calibrate interest rates trees
2) Not sure whether that question is quite relevant, because the guy in there wants to price option on basket, while I want to model short rate using information on dynamics of the whole curve
Jun
21
comment Applicability of PCA to get historical volatilities to calibrate interest rates trees
1) 1D tree is a kind of nonsense, sorry for that. I meant, well, usual tree in two dimensions - asset and time. Now, such tree is built using two 1xN vectors - interest rate curve and volatility. Example code here link
Jun
20
comment Applicability of PCA to get historical volatilities to calibrate interest rates trees
You see, I want to build 1D flat tree, the question is only how would it be better to choose volatility structure to calibrate the model. The choice I see now is use either straightforward historical volatilities without any account for correlations, or calculate covariance matrix and take first PCA component from it. In both cases I have 1xN vector with volatilities, which I (technically) can use as a starting point of building a tree
Jan
17
comment BDT model implementation
My question was how to select appropriate parameters for BDT in order to simulate it with Monte-Carlo. I am trying to avoid trees (as suggested by papers 1 and 2). And the third paper is too general and tells nothing about calibration