Michael Bommarito

Capitalization Coupling: Major Indices at Correlation Highs

These are all from June 2001 to yesterday, and significant to at least p=0.025 again.

Capitalization Coupling: Major Indices at Correlation Highs

Middle of exams here so I don't have time for any stochastic volatility or adaptive models, but feel free to provide any results you find doing so. Here are some more extensive results just under this simple model with non-parametrics for comparison.

Colums:
Pearson/Sign Pearson/Spearman Rank/Sign Spearman Rank

Rows:
1st: Pearson correlation between return & each correlation
2nd: Spearman rank correlation between return & each correlation

Results:

0-Day (no lag)
ans =
-0.0279 -0.0283 -0.0377 -0.0268
-0.0006 -0.0555 -0.0439 -0.0555

1-Day
ans =
0.0115 0.0147 0.0236 0.0145
0.0261 0.0224 0.0368 0.0269

2-Day
ans =
0.0010 0.0035 0.0004 0.0029
0.0015 0.0074 -0.0087 0.0057

Capitalization Coupling: Major Indices at Correlation Highs

Yes, I accept that my suggestion about the trend was not significant under this model, but what I meant to show in my reply was that the opposite was not true either, as you argued. My p-values were all significant at least to the 0.025 level, though I did not record them.

I would also note that your samples were contained strictly within the local phenomenon that I am trying to explain, and thus not only do they have no chance of demonstrating a difference from prior relationships, but you have an N smaller than mine by an order of magnitude wrt power.

Capitalization Coupling: Major Indices at Correlation Highs

By the way, I'd appreciate links to studies or your own analysis in the future instead of suggestions about tea leaves.

Capitalization Coupling: Major Indices at Correlation Highs

Rudi, are you talking about correlations between assets *within* an index or correlation *between* these specific capitalization-oriente... indexes?

Both rank and Pearson-product correlations indicate that:
i) daily R^2 between the average correlation change and return is -1%
ii) weekly R^2 between the average correlation change and return is 2%
iii) monthly R^2 between the average correlation change and return is 4%
iv) quarterly R^2 between the average correlation change and return is 6%

Which seems to contradict your statement.

Moment and Omega Rankings of Index ETFs

Hello Susan, the double accounting is for two reasons - firstly, to confirm the ranking of the first four moments, and secondly, as you mention, to include the higher order moments.

Also, if you'd like an example of a distribution with negative mean and positive skewness, take December 31, 2001 to March 15th, 2003, and you should get just such a distribution. For reference, I calculate a log-return mean of -0.0011 and skewness of 0.4057. Make sure you're not using excess moments either.

Top ETF Dollar Volumes Over the Past 50 Days

Hello flstearns,
Unfortunately I'm not really in control of the page layout here, and it seems as if my table won't fit into their layout. You can always follow the links in an article to the original source, where you should be able to read the tables: www.etf-central.com/to...

As far as the example, there are 50 multipliers, as each day's close and volume need to be multiplied. If you're confused about the units, as they're probably blocked by the frame as well, they're in millions of dollars averaged per day.

Dow Components Furthest Above, Below Their 50-DMAs

For anyone interesting in trading ETFs/CEFs, I have a very similar table with 50/200 MA's here.

513 Days: Longest Dow Has Gone Without 2% Gain Since '66

I've been running similar numbers lately, trying to get a statistically meaningful gauge of what to at least vaguely expect out of this market. Here's the number of times the Dow has attained maximum percentage of last sessions up and the last date it did so for 10 to 400 sessions, as well as actual time series analysis of the percentage of past 25/50/200 days up and correlation to following return.