ETF Central » correlation

Paper: D. J. Fenn, M. A. Porter, S. Williams, M. McDonald, N. F. Johnson, N. S. Jones. Temporal Evolution of Financial Market Correlations.

Michael Bommarito — Tue, 16 Nov 2010 04:44:28 +0000

Here’s a paper out of the CabDyn group at Oxford from D. Fenn and M. Porter. Mason is also one of the leading researchers in network science, and their group has entered into a number of joint Ph.D./post-doctoral hires with the business school there. The result is a large number of interesting papers. This particular paper investigates correlation matrices through RMT, which is exactly what my Quantitative Finance paper and recent working paper address. Though they don’t examine their calculations in an applied context, the results provide additional view into recent correlation dynamics. Abstract and download below:

Abstract: We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We then characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007–2008 credit and liquidity crisis.

D. J. Fenn, M. A. Porter, S. Williams, M. McDonald, N. F. Johnson, N. S. Jones. Temporal Evolution of Financial Market Correlations. http://arxiv.org/abs/1011.3225.

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Zoomable Visualization of Market and Sector Performance and Correlation, Nov. 5, 2010

Michael Bommarito — Sat, 06 Nov 2010 14:04:44 +0000

Last week, I posted a zoomable visualization of the weekly market and sector performance and correlation. People seem to find this image both useful and “cool,” so here is this week’s edition and takeaways below:

Green, green, green (on the diagonal). Other than healthcare (XLV), every sector was up at least 1%, and most were up well over 3%.
More green (off the diagonal). Most sectors were strongly correlated with one another, with the exception of financials (XLF) and healthcare (XLV). Healthcare, as noted above, underperformed the market significantly by 2.5%. The story with financials is the opposite – financials were up a whopping 6.8% this week, putting them over 3% ahead of the market.
Correlation was strongest between energy (XLE) and materials (XLB) at 99.5% and weakest between financials (XLF) and healthcare (XLV) at -21.8%.

By the way, this figure is produced with Python and cairo. The code is fairly ugly and long, so I probably won’t release it unless there’s some demand.

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Paper: I. Mastromatteo, M. Marsili, P. Zoi. Financial correlations at ultra-high frequency: theoretical models and empirical estimation

Michael Bommarito — Fri, 05 Nov 2010 14:49:17 +0000

Another one fresh off the pre-printing press at arXiv. Having skimmed the paper, this looks like a serious treatment of a very serious problem – reconstructing the coefficient on the correlation term of models when returns are sampled asynchronously, as is almost always the case when using tick data. On a related note, Section 2 is the best presentation of the Epps effect in this context I’ve seen.

Abstract: A detailed analysis of correlation between stock returns at high frequency is compared with simple models of random walks. We focus in particular on the dependence of correlations on time scales – the so-called Epps eect. This provides a characterization of stochastic models of stock price returns which is appropriate at very high frequency.

I. Mastromatteo, M. Marsili, P. Zoi. Financial correlations at ultra-high frequency: theoretical models and empirical estimation. arXiv:1011.1011

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