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Aug
7
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.
Aug
5
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.
Aug
3
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.
Aug
3
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.
Jul
27
answered Statistical Power and Active Management
Jul
22
comment Time-series similarity measures
Also, cointelation.
Jul
22
revised How to look for fractals/harmonics patterns in time series?
deleted 67 characters in body
Jul
22
answered How to look for fractals/harmonics patterns in time series?
Jul
22
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.
Jul
18
revised Time-series similarity measures
added 102 characters in body
Jul
14
awarded  Nice Question
May
30
answered Time-series similarity measures
May
25
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.
May
25
comment So many volatility models. Any comparisons of them?
What about comparing forecasts from a realized model to the implied vol?
May
24
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?
May
23
asked So many volatility models. Any comparisons of them?
May
6
comment rugarch: Joint estimation leads to different results
Send a link of this thread to the package's author. He's fantastic and responds to my queries within 2 days; I'm sure he'll take a public thread even more seriously.
Apr
19
comment Resources for finding scholarly research on topics in quantitative finance?
bookos.org is good
Apr
9
comment How can I go about applying machine learning algorithms to stock markets?
I want to know why there's such a vast sea of machine learning people working at prop firms on LinkedIn if it doesn't work? Isn't this good evidence that it does work persistently in some markets at some frequencies?
Apr
8
comment Testing Significance of Correlation
Pearson correlation will be significant, assuming no assumptions are violated, when the bivariate time series is fitted well by a linear relationship // straight line. This makes sense because the square of the pearson correlation is the R^2 from the linear model y = a + bx + e. You find out whether it's spurious by seeing if the estimator's assumptions are violated, meaning the finite sample properties of the test statistic will not approximate the asymptotic case well. The Pearson correlation is highly susceptible; bivariate non-normality,hetero,serial corr,etc. Kendall/Spearman is robust.