Kiwiakos
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Heston gives an expression for the characteristic function, from which option prices can be computed. Therefore it can be calibrated (statically) on a set of vanilla option prices with different ...

Financial asset is the Aussie dollar bill or coin. If your numeraire happens to be the US dollar, then the FX rate is the market value of the financial asset denominated in units of the numeraire.

You can find Matlab code in these notes: http://cosweb1.fau.edu/~jmirelesjames/MatLabCode/Lecture_notes_2008d.pdf I wrote them 10 years ago and have not revisited since, but it should work.

Prices (and potentially volumes) have been adjusted for historical corporate actions. For example, if there was a 10:1 split in the past, then todays share is equivalent to 1/10th of a share before ...

Have you tried to simulate both processes together from US close -> JP close -> US close -> JP close and so on? Where the correlation is fixed, but the volatility of each step is proportional to the ...

If the call is ITM, ie $K<S$, as expiry approaches the likelihood that the option will be exercised increases, as there is now less time for it to go OTM. Delta is the position that the hedger is ...

The price of a derivative does not explicitly depend on the expected return of the underlying, however the price change or return of the derivative depends on the return of the underlying. Hence the ...

In MPT investors maximize ex ante expected return for a given level of ex ante variance. Gaussian-ity or iid-ness of returns are not requirements. The problem is estimating these ex-ante quantities ...

Perhaps they mean that if you use the ATM implied volatity as an input to price ITM and OTM options, then some will be underpriced and some overpriced compared to the true price observed in the market....

IMO: Volatility is a risk factor not an asset class. Asset classes are collections of assets and volatility is not one. Options, volatility derivatives, etc, are asset classes which might offer ...

Looks like jump diffusion. You can take $f(t,T)=\log F(t,T)$, apply Ito's formula for jump diffusions and take it from there. I cannot see how taking integral directly can lead you anywhere.

The model has effectively two free parameters, and therefore one cannot expect it to match bonds of different maturities. Typically this is how you 'get the parameters' by solving to match a given set ...

I don't see this issue in Bloomberg, therefore I would assume it is just bad data in your source. Based on my snaps I suspect that your bid-ask is 15 min delayed. This stock is very active today, ...

What happened was the BoJ announcement. Such large scale news are well covered in mainstream media (ft, bloomberg, etc) and also mainstream anti-media (eg zerohedge).

It depends how large the overlapping interval is. Conceptually an infinite rolling window is equivalent to the level, and no one would suggest to 'regress on levels and apply Newey West'. I think NW ...

In 1983 he was using Hidden Markov Models. Now he employs 100+ PhDs, therefore I expect he will have 50+ strategies using 200+ predictors. And set up as a production line, from the teams importing and ...

Levy models do that to some degree. They have the iid look and feel of the standard Gaussian models, but allow for higher moments. You can check the papers of Dilip Madan on Variance Gamma as a ...

Sharpe ratio behaviour reflects the diversification over time. I can diversify using a large number of stocks (ie toss 10 coins simultaneously) or by holding for a large number of periods (ie toss ...

Are you talking about something like this? $$dx(t)=\ldots\ dt+[x(t)]^\gamma\ dW(t)$$ If $\gamma$ is zero then you've got BM, if it's one you get GBM, inbetween you have a 'mix'.

I'd use FFT or similar rather than direct integration. Here is an old paper with Heston example: Option pricing using fractional FFT

Have you looked at money market ETFs? Something like Pimco's MINT http://finance.yahoo.com/quote/MINT

Two comments: Normal returns should always be in $[-1,+\infty)$. I believe that the way you sample $R_i$ from Stable directly violates that. You might want to sample $\log (1+R_i)$ from Stable ...

$d$ is a vector that collapses the $n$-dimensional vector into a real number. In the BS case $d=1$. There is nothing to be estimated. Also not that in practice affine pricing is done through FFT (and ...

The derivation is in "Managing Smile Risk" by Pat Hagan et al. A copy is here: http://www.math.ku.dk/~rolf/SABR.pdf It is not closed form, but rather an approximation based on expansions.

Kalman filter (or similar methods) are quite well suited to deal with observations that are of different sampling frequencies and/or asynchronous.

I would say that $\log K/F$ points towards a log-normal type model. If I were you I would experiment with the moneyness defined as $K-F$ instead. This would make it consistent with normal dynamics. ...

I believe that the confusion arises because of the wrong treatment of NIG. The answer to the question you link is misleading, as it simulates under P which is not appropriate for option pricing. None ...

The ADF test assumes the DGP $$\Delta y_t = \alpha +\beta t +\gamma y_t +\delta_1 \Delta y_{t-1}+\cdots +\delta_k \Delta y_{t-k}+\epsilon_t$$ The parameters are estimated using OLS on a sample of ...