Felix Salmon

How does JP Morgan respond to a crisis?

If you have a bit of time today, the official JP Morgan post mortem on the London Whale affair is well worth reading. The whole thing is 132 pages long, although the executive summary — which is very clearly written — is only 17 pages.

Jamie Dimon’s failure

Ina Drew — the JP Morgan executive who famously “loves crises” — is out; it seems the buck for the $2 billion trading loss in her unit has stopped with her. And slowly, a few shapes are beginning to emerge from the fog of what exactly happened here.

When investment banks hire risk-takers

Matt Taibbi is quite right about the $2 billion of rogue-trading losses at UBS. Basically, investment banks hire for risk-takers; they shouldn’t be surprised when this kind of thing happens.

How has VaR changed over time?

Whenever I write about banks’ rising Value-at-Risk, a bunch of commenters tells me that duh of course VaR is rising, because VaR is a function of volatility, and volatility has gone up. So here’s my question: can someone come up with a baseline VaR chart, for a hypothetical bank which had, say, a fixed $1 million investment in the S&P 500. What would its quarterly Value-at-Risk have looked like over the past couple of years?

McNamara and model risk

Philip Delves Broughton notes that Robert McNamara, one of Harvard Business School’s most notorious graduates, basically did in the field of war what Wall Street quants did in the field of finance:

Modelling model risk

Paul Wilmott has words of wisdom for anybody in the financial-services industry who’s putting a model together:

Are the new securitization regulations workable?

Binyamin Appelbaum has details of the new regulations surrounding securitization, and it all looks incredibly unworkable to me, especially the central plank:

Taleb’s data dump

Back in March, Rick Bookstaber published a blog entry entitled “The Fat-Tailed Straw Man”, which seems to imply that Nassim Taleb attacks Wall Street practices on the grounds that its practitioners believe in normal distributions.