Bionic Turtle’s Week in Risk (ending February 19th)

New Practice Questions

In the forum this week (selected only) or Major News

Bank and banking

Political and regulatory risk, including Systemic Risk (including BIS)

International

 

Technology, including FinTech and Cybersecurity

Natural Science, including Climate and Energy

  • The crisis at Oroville Dam, explained http://www.vox.com/science-and-health/2017/2/13/14598042/oroville-dam-flood-evacuationBack in 2005, a handful of environmental groups warned that the dam failed to meet modern safety standards for severe flooding. They urged federal officials to mandate concrete fortifications along the auxiliary spillway as part of the relicensing process for the dam’s hydroelectric plant. But as the Sacramento Bee reports, federal and state officials rejected this request, arguing that a disaster like we’re seeing now was unlikely, and the dam could handle worst-case scenarios.

Data science (primarily R), including Alternative Data

Enterprise risk management (ERM) including governance

Quantitative Analysis (FRM P1.T2)

  • Some Notes on the Cauchy Distribution (how cool is this?!) https://www.rstudio.com/rviews/2017/02/15/some-notes-on-the-cauchy-distribution/The extreme values that dominate the Cauchy distribution make it the prototypical heavy-tailed distribution. Informally, a distribution is often described as having heavy or “fat” tails if the probability of events in the tails of the distribution are greater than what would be given by a Normal distribution. While there seems to be more than one formal definition of a [heavy-tailed distribution] (https://en.wikipedia.org/wiki/Heavy-tailed_distribution), the following diagram, which compares the right tails of the Normal, Exponential and Cauchy distributions, gets the general idea across … As exotic as the Cauchy distribution may seem, it is not all that difficult to come face-to-face with the Cauchy Distribution in every-day modeling work. A student t distribution with one degree of freedom is Cauchy, as is the ratio of two independent standard normal random variables.

Financial Markets and Products, including Interest Rates, Commodity Risk, and Foreign Exchange (FX)(FRM P1.T3)

  • Dole Food Had Too Many Shares https://www.bloomberg.com/view/articles/2017-02-17/dole-food-had-too-many-sharesThe way short-selling works is: 1. Mr. A owns a share of stock. 2. Mr. B borrows Mr. A’s share of stock. 3. Mr. B sells the share to Mr. C. But now Mr. A and Mr. C each own one share of stock. Where there was only one share, now there are two. A “phantom share” has been created. Well, not really. The trick to balancing the books is to remember that Mr. B owes Mr. A a share of stock. So Mr. B now owns negative one share of stock. There’s a total of one share: one for A, and one for C, and negative one for B. One plus one minus one is one. It’s no problem.

Valuation and Risk Models, including Country risk (FRM P1.T4)

Operational risk, including Legal risk (FRM P1.T7)

Investment risk, including Pensions (FRM P1.T8)

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