<t>Why stability of a model for option pricing is so important? I think that parameters of a model give information about the market, so if the parameters change as effect of small changes in the set of option prices used to calibrate the model, then a trader that construct his strategy within such ...

What is an infinitely divisible distribution and how it can be used in financial modeling?

Well, but i expected that a pure jump process like VG be useful in pricing option with very short maturity (like 1 week)...I'm wrong if i say that tipically jumps are added in order to consider extreme events in short time interval?

<t>Anyone have use matlab blsimplv routine (financial toolbox) for compute implied volatility?I have a lot of problem with this routine: e.g. blsimpv(28368,28500,.09145,8/365,38) return -1.340780792994260e+154 that' s impossible as value. Anyone has experienced such problem?Anyway, who can suggest m...

<t>I calibrated VG model and i found that the fit to market data is quite good...Anyway, when i transform price in implied volatility (with matlab blsimpv routine) i get that the fit isn't so good...The biggest problem is that for maturity =1 week the pattern of implied volatility isn't a smile but ...

<t>Bingfei, i don't get (4,5,7) values! It was an example...I'm using the Fractional FT approach .Which optimization algorithm you' re using? Anyway, my doubt remain:QuoteMy doubt is that with option market calibration i obtain the "risk-neutral" parameters and so i don't know if it's right give the...

<t>Can i accept any value for model parameters from calibration (provided that the objective function has a low value)?e.g.: if i calibrate VG model with option market data and i obtain a value for parameters (\theta,\sigma,\nu)=(4,5,7) and value for obj. function = 0.001, is this solution better th...

I'm doing Heston and VG calibration with option market data. My question is: could it be useful to have bounds on the values that parameter can be assumed? e.g.: for \sigma parameter in VG model impose that 0<\sigma<70%...

Anyway, i don't understand the relation between vanilla and exotic option (i.d. my second question...)

<t>But if i have a model that fit very well for vanilla options may i say that it will work well also for exotic options? Further: suppose i calibrate a model for vanilla options and the obj function has many equivalent minima...My routine find one of them: the pricing of exotics option may be quite...

Could anyone explain me why it said that to correctly pricing exotic options is fundamental pricing in the correct manner plain vanilla options?I'm grateful if someone could suggest me a reference about that (preferably on the web)

Mmm...However i still have some doubts about my first question...And what about my second question:"is it true that a models for asset return may be good in representing time series but not good in option pricing model?"

<t>QuoteOriginally posted by: marcsterAs I see it, a time series would be an instance, whereas an assumed type of stochastic process is a template. Yes, anyway every time you want utilize such template you need historical data in order to calibrate it...Further, when youQuoteAlso, if you have an act...

Sure, but one couldn't calculate the distribution just from time series?Anyway, what about the second question?

Why, in a option pricing model, one need of stochastic process describing the dynamics of the underlying?And then, is it true that a models for asset return may be good in representing time series but not good in option pricing model?