Bionic Turtle’s Week in Risk (March 31, 2019)

Welcome to our Week in Risk blog! Stop by our forum to join in on the FRM discussions, and visit our YouTube channel to view in-depth videos that David Harper posts weekly! This week, we’ve included our newest FRM practice questions, forum discussions, and other great risk articles that we hope you will find interesting. Have a great week!

  • Valuation & Risk Models: P1.T4.912. Key rate exposure technique in multi-factor hedging applications (Tuckman Ch.5) The hardest part is understanding why the 10-year key-rate sensitivity of a 30-year zero-coupon bond is negative. Tukman’s key rate is a par yield. This is really hard for everybody, read it carefully.

tuckman key rate exposures

  • Valuation & Risk Models: Fixed Income: Bond’s full/flat price on settlement date (FRM T4-22) The calculator’s TVM function computes a cash (aka, full) price but only on a coupon date. If the settlement date is realistically between coupons, you need the bond worksheet or you need to adjust from the coupon-date price
  • Calculator Tutorial: TI BA II+: How to compute bond price on realistic (between coupons) settlement date (TIBA-02) This shows how to use the calculator to solve the same between-coupon bond price as illustrated in the previous video (FRM T4-22). David recommends proficiency with both the TVM functions and the bond worksheet; the only way to gain proficiency here is to practice many problems.

  • R Programming: Introduction: Matrices (R intro-05) We started with vectors (R Intro-01) which are the fundamental data structure in R. Vectors can be atomic or lists (R Intro-02). Factors are vectors that store categorical, pre-defined values (R Intro-04). This video is about matrices which are also vectors; see, everything is a vector in R! Matrices are 2-dimensional arrays and they are defined by the $DIM attribute. For example, the 4*4 matrix in the example (a covariance matrix) is a vector of length 16 with a DIM = c(4,4) attribute.

  • How many durations? Just one, really! Branislav posted here ( an impressive summary of what’s he’s learned about duration in the FRM. David added his agreement that, in fact, there is only one duration concept but it has three faces: Macaulay, modified and effective. We’ve been discussing bond duration and convexity for over a decade in the forum, and we’ve learned so much from candidates and practitioners. When David started teaching the FRM, for example, he didn’t realize the units for all of them are properly years, until a member posted the proof! Yea, David still remembers that day when he was so … um, wrong. You might think he knows his stuff, but hosting the forum means that he often gets “owned,” mathematically speaking. ;)


  • Square-root-rule (SRR): Here is a smart question which does not take for granted our commonly used square-root-rule We routinely scale a daily volatility or VaR over ten days by multiplying by sqrt(10). But what are the conditions for doing that, including are we technically making an assumption about whether the returns are arithmetic or geometric? (here’s a hint: the SRR does not require normality).

  • Free rstudio conference materials: In January, David attended the rstudio::conf ( in Austin. He also went to last year’s in San Diego and hopes to go next year when it’s held in San Francisco. This is probably the most important conference for the R community and people routinely say it’s the best conference they’ve ever attended. David has long believed that developing some data science fluency (if not some code practice) is a good career plan for those of us in technical (non-sales) fields such as financial or risk analysis, even if we are not developers. Hence his multi-year personal investment in learning data science where he happens to prefer R, but of course Python is very popular. Typical of the utterly generous community around R, the talks and materials for that conference have been made freely available at the resource page (and materials here on github at

rstudio conference

  • Better board practices? In the last WIR, David pointed to a resource on the Theranos debacle. David says, “Although I admit I’m interested in narratives related to individual psychology and startup culture, I am also mostly fascinated by the utter failure in governance. John Carreyrou’s book did not really solve this aspect of the puzzle for me: clearly, Elizabeth Holmes populated her board with “cabinet members, congressmen, and military officials” to create prestige and distract from actual science, but how exactly is an entire well-compensation board allowed to completely fail to perform any of its duties? In my previous life, I was a management consultant to boards, and while most were impressive, it led me to believe that traditional boards (especially marquee hires) are one of capitalism’s Achilles’ heel. The performance standards for board members at many public companies should be much higher. So I enjoyed Board 3.0: An Introduction published at the Harvard Law Forum, which is a great resource on governance practices. They also just published: Crisis Resilience and the Board—Taking Risk Oversight to the Next Level (”
  • Many (mental) models: As an investor and student of risk, David is attracted to the idea of mental models as a means of coping with complexity. Shane Parrish says “a mental model is simply a representation of how something works. We cannot keep all of the details of the world in our brains, so we use models to simplify the complex into understandable and organizable chunks.” ( David is almost finished reading The Model Thinker by Scott Page ( This is a strong reference-like catalog of about 25 models–only barely quantitative in exploration–including many that he has never heard of before. How many of them do we study in the FRM? Let’s see, we count at least seven: normal distributions, power-law distributions, linear models, concavity and convexity, random walks, path dependence, and Markov models. Not bad, GARP! What’s next on David’s reading list? Oh geez, where to start. He is looking forward to Infinite Powers: How Calculus Reveals the Secrets of the Universe, excerpted here

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