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 Aug10 answered How do I estimate the joint probability of stock B moving, if stock A moves? Aug9 comment R: Fast and efficient way of running a multivariate regression across a (really) large panel (First pass of Fama MacBeth) Two ideas; (i) don't run lm(...), use $(X'X)^{-1}X'Y$. (ii) every so often do a write.csv' or a save, and rm() to clear memory, (iii) run the as.character on the whole vector of dates instead of on a single date in each loop iteration.. Aug7 revised Tools in R for estimating time-varying copulas? added 21 characters in body Aug7 answered Tools in R for estimating time-varying copulas? Aug7 revised Why are regressors squared and not ^1.5 or ^2.2 or ^2.5? added 49 characters in body Aug7 comment Why are regressors squared and not ^1.5 or ^2.2 or ^2.5? @ApprenticeQueue Agreed. The parisomny argument is only coherent if the parameter value is selected when minimising the loss function. If it is set a priori then the parsimony argument doesn't hold since 1.95 is as arbitrary as 2.00. Aug5 comment Time-varying correlation via state-space representation and Kalman filter Stochastic copulas such as SCAR are complex. First, look at normal time-varying copulas in the 2006 paper by Andrew Patton. Code and paper references for the time-varying copulas can be found on public.econ.duke.edu/~ap172/code.html. Aug3 comment Rolling window Kendall's tau against APARCH(1,1) correlation Get a time-varying Kendall's tau from Patton's (2006) time-varying copula parameterisation. Heteroscedasticity in the univariate series can be modelled by specifying the marginal distributions as a GARCH process. Fat-tailed bivariate distributions can be handled using a time-varying SJC copula or alternative. Aug3 comment Time-varying correlation via state-space representation and Kalman filter You probably already know this, but as a side note consider time-varying correlations through (i) (AG-)DCC-GARCH, (ii) time-varying copulas and stochastic copulas, (iii) wavelet coherence. If you're using Kalman Filters because you want an "online" time-varying estimate of correlation, then (i) and (ii) aren't useful but (iii) with causal wavelets might be. If you want to just know what happened in the past, (ii) would be better because you can look at non-linear time-varying correlations such as a time-varying Kendall's Tau or Spearman's rho. Jul27 answered Statistical Power and Active Management Jul22 comment Time-series similarity measures Also, cointelation. Jul22 revised How to look for fractals/harmonics patterns in time series? deleted 67 characters in body Jul22 answered How to look for fractals/harmonics patterns in time series? Jul22 comment How to look for fractals/harmonics patterns in time series? Perhaps to detect fractal behaviour you could fit something like a Daubechies wavelet. That is, $W(a,b) := \frac{1}{\sqrt{|a|}} \int_{-\infty}^{\infty} f(t) \phi((t-b)/a)dt$. Then you want to check the set $\{W(a,b) : a \in \mathbb{R}\}$ where $a$ is the scale, for some fixed $b$. If all the coefficients are similar then this might be some indication of fractal like behaviour at least in that time localisation. I'm sure there's ways from stochastic calculus. Also, might be good to explore econnometric estimators (Heterogeneous ARCH) that take in multiple freq and check coefficient stability. Jul18 revised Time-series similarity measures added 102 characters in body Jul14 awarded Nice Question May30 answered Time-series similarity measures May25 comment So many volatility models. Any comparisons of them? So you're saying that we can't compare forecasts from realized models and implied vol because realized models are based on historical prices and implied models deal with current prices? While this is true I'm still not clear why this means that we can't compare the two meaningfully. May25 comment So many volatility models. Any comparisons of them? What about comparing forecasts from a realized model to the implied vol? May24 comment So many volatility models. Any comparisons of them? Very nice answer, but could you please elaborate on "it does not make sense to compare standard deviation models with an implied vol model." Why doesn't this make sense?