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seen Jun 21 at 22:14

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
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.
Mar
9
comment Improving GARCH modeling approach
@ikh Nice comment. Definitely go for the AR(N) in the mean equation if there's first moment serial correlation.
Feb
27
comment Toy models of asset returns
I have no idea but perhaps some you could consider: A skewed-t distribution... Or, a gaussian distribution for the centre and generalized pareto distribution (or generalized extreme value distribution) for a parametric distribution of tails (see extreme value theory). Then you just tweak the tail parameter to get whatever tail area you want. You could maybe use a multivariate parametric Vine copula to paste the univariate distributions together and create your economy.
Feb
9
comment R ARMA-GARCH rugarch package doesn't always converge
i've found that GARCH fails to converge often due to outliers. Try winsorizing the data at 98% and it should converge. If not try asymmetric GARCH. Best is probably to use a new outlier robust GARCH but I don't think rgarch has those yet. This might be the case with datastream data if the market is open on different days to the standard daily dates that Datastream uses, in which case there'll be a large number of zero return days, but I haven't really explored this hypothesis yet (but I suspect it given the type of data that fails to converge that I've played with).
Jan
26
comment Why do low standard deviation stocks tend to have superior future returns?
Could you briefly summarize the paper (objectives, main findings) in a few sentences so I can award the answer?
Jan
22
comment Is there a copula that can estimate negative tail dependence?
Yes very nice!!
Jan
22
comment Alternative ways to understand time-varying comovement between two time-series?
Answer to the question is that time-varying Kendall's tau can be estimated through a time-varying copula with something like a GJR-GARCH-ARMA-t for the univariate marginal distributions.
Jan
19
comment Strategy of Renaissance Technologies Medallion fund: Holy Grail or next Madoff?
What are their methods? If you can claim that they're straightforward then that implies that you know what they are.
Jan
16
comment Alternative ways to understand time-varying comovement between two time-series?
@Freddy Apparently it also allows us to look at the time-varying correlation at the distributional extremes, sort of like a time-varying quantile correlation estimator (which actually has just been invented in the last 6 months, including standard error asymptotics, although nobody has referenced it yet). It's applied to good effect here: sciencedirect.com/science/article/pii/S1059056010001358
Jan
16
comment Alternative ways to understand time-varying comovement between two time-series?
@Freddy This one seems to be a good one: wisostat.uni-koeln.de/Institut/LSMosler/Manner/…
Jan
16
comment Alternative ways to understand time-varying comovement between two time-series?
@Freddy Something that captures both the time-varying linear AND non-linear correlation/relationship between two time-series. I've edited the title to make it less confusing. Also, I have just learned that various 2006+ copula methods have been developed that can achieve what I want.
Jan
16
comment Regressor: Nominal return, continuous return or first difference?
Thank you. I think because I'm using low frequency monetary policy rates it makes sense for it to be non-stationary. For example if a central bank changes mandate half way through the sample (e.g. adjusts inflation target band from 2-3% to 2.5-4%). Or if the country had hyperinflation in the early 1990s and then finally got it in control by the 2000s.
Jan
16
comment Why do low standard deviation stocks tend to have superior future returns?
@bonCodigo I don't understand.
Jan
15
comment What type of analysis is appropriate for assessing the performance time-series forecasts?
Wow! So simple!