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Quantitative Finance

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  • $\begingroup$ The approach in the paper that you reference is indeed very interesting and close to our paper. However, they use a $VAR(1)$ mode, in which auto-correlations do not vanish but just decay. We chose a $VMA(1)$ model as there the autocorrelations vanish after one time step. If autocorrelation comes from non-contemporaneous trading then it should vanish after one day. In practice a $VMA(1)$ and a $VAR(1)$ model will give similar results in most applications. $\endgroup$ Commented Apr 3, 2013 at 8:57
  • $\begingroup$ I tend to shy away from moving average models and just increase the number of lags in a autoregressive model, but what you're saying makes sense. $\endgroup$ Commented Apr 3, 2013 at 13:38
  • $\begingroup$ Just one more and last comment: if you look at the preprint above page 5 formula 1.6 then you see how the regression of returns on lagged returns is represented. This looks at first glance like $VAR(1)$ but when you analyze the residual then this can not be shown to be White Noise, which it should be for $VAR(1)$. $\endgroup$ Commented Apr 4, 2013 at 7:20
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    $\begingroup$ I don't keep my promise, one more comment: you are right, that the calibration of $VAR(1)$ is more intuitive (it cna be done by a regression) than the calibration of $VMA(1)$. Our experiments in this context gave good results for the calibration of $VMA(1)$ too. $\endgroup$ Commented Apr 4, 2013 at 7:41
  • $\begingroup$ I've read about the use of a garch process, what do you thik about it ? $\endgroup$ Commented Apr 11, 2013 at 13:43