pm me your email id and I will mail you the file

I sent you a pvt message smarttrader..see if that helps..

What is the variable 'Y' that you are referring to?

<t>Here is a test file I created to compare a Monte-Carlo and a semi analytic (SA) implementation. In the SA, I have used spline itnerpolation with a Gaussian quadrature to generate the truncated (with barrier) densities for the intermediate fixings, leading all the way up to the terminal date. The ...

<t>A quick check using Monte-Carlo, with the following: r_domestic = 0.0015, r_foreign = 0.0006, shows a vlaue of about ~5.229, with various fixing prices as: 1st 1.62nd 0.92593rd 0.61744th 0.45775th 0.366th 0.29437th 0.22948th 0.178359th 0.1668710th 0.1502711th 0.127412th 0.1209I use 10000 paths fo...

If you send the test parameters, I can try some test cases and verify

<t>A pure random walk (Brownian motion) is a non-stationary series (variance blows up with time) and the increments are uncorrelated! On the other hand, AR(1), for e.g. is a stationary process with finite autocorrelation!! On beta estimation, I suppose ordinary least squares assumes that the residua...

<t>right now i am concerned with data sampled intra-day, say 15-30 minutes and the look-back period is few weeks or months at best. True that in G10, the intervention is rare, so probably not so much of an issue there. I would nevertheless want to get rid of the outliers and random shocks, as these ...

I still don't see the connection! , where T is forward expiry and t is the current time. As you state it, let T'<T be the expiry of the option: and then you have . Now let us say you want to value the option at time t (<T'<T), the discounting you are suggesting is , doesn't help!

For stocks 10-20 bps sounds reasonable, for futures around 2-5 bps probably; to allow for slippage in your back-test, a few bps higher transaction cost might be used..

A long call and a short put gives you a futures payoff, so maybe in that sense options are margined (leveraged?). Your underlying is the forward contract itself (and quotes are available) why do you want to discount this to the option expiration day?

<t>Let us say one is working with a state-space model for the purpose of (short term) forecasting of an underlying (say FX rates), now for model estimation from historical data, how far back should one look? Say if we do a maximum likelihood estimation and get two different estimates using window si...

With the now constant intervention of the central banks in the markets, historical patterns of time-series behaviour would change perhaps significantly; any thoughts on how to go around this problem while modeling?

You could try using Alan Genz's - 'Numerical Computation of Multivariate Normal Probabilities' (if that is what you are looking for!)?

Maybe you can also look at the paper 'Estimating volatility and model parameters of stochastic volatility models with jumps using particle filter' - by Shin Ichi Aihara et al.Cheers.