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:
Tonight, I’ll be clearing out a backlog of papers built up over the last few days. Here’s the first paper (not including mine) – Network-Based Modeling and Analysis of Systemic Risks in Banking Systems. The paper is interesting for two reasons. First, they model a banking system with an explicit network of bank-to-bank relationships and correlated bank balance sheets. Second, they simulate their model to compare the ability of bank risk measures to predict contagion. I’m not sure their LASER algorithm is actually any different than the Kleinberg HITS method applied to an edge-weighted network, but it does outperform the standard accounting measures in their simulations. Abstract and download below:
I’ve been offline for a few days wrapping up some contracts and academic work, but I wanted to highlight an exciting paper that Dan and I have been working on – Measuring the Complexity of Law: The United States Code. This law review is a thorough description of our method for measuring legal complexity and is the counterpart to A Mathematical Approach to the Study of the United States Code, recently published in Physica A. Given the recent chatter on possible tax reform and simplification lately, Tax Code complexity may be popular topics in the near future. The paper isn’t public yet, but you can read the abstract below:
Here’s one of those papers that you’d always meant to write. In this case, I think I even suggested it on the blog once – if you have to use some parametric VaR/ES method, why not replace the 2-moment normal characterization of return with its generalization, the 4-moment Johnson characterization?
Here’s a new paper from the Fed that tries to determine the actual effect of asset purchases on the yield curve. Abstract below!
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%.
Data has driven finance forward in ways that theory alone could not have. In recent years, a similar trend has developed in the study of law, especially within fields such as “law and economics” or “empirical legal studies.” I find this “data-driven” approach to law fascinating (having been introduced to it by Dan), and have been active in a number of projects on the topic.
The paper below, which is forthcoming in the Virginia Tax Review (one of the top three tax reviews), is an excellent example of this trend. In it, we examine a number of aspects of the population of the Tax Court’s written decisions. Obtaining and analyzing these decisions required a significant degree of technical sophistication, and interpreting the results in their legal context has provided a number of insights. The abstract is below: