Bionic Turtle’s Week in Risk (March 24th, 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!

New Practice Questions

  • Valuation & Risk Models: P1.T4.911. Multi-factor interest rate risk models (Tuckman Ch.5) After a long sequence on bond duration and convexity, we step up to multi-factor models. The current interest rate environment illustrates the realistic weakness of single-factor models: investors are highly focused on the flattening yield curve, and wondering if it will invert.
  • Credit Risk Measurement & Management: P2.T6.902. xVA components (Gregory Ch.4) We are still carefully defining terms in counterparty risk (xVA refers to the set of CVA, DVA, FVA etc). We need to understand Gregory’s terms in order to tackle the harder stuff later. I re-drew the CVA desk diagram in MS Visio; it’s a small thing, but I hope a little bit of color makes this dry topic less dry.

xVA components

New YouTube

  • Valuation & Risk Models: Fixed income: Law of One Price (FRM T4-21) The Law of One Price basically means that each maturity has one and only one discount factor, which, at first, is a difficult concept to grasp! The caveat (“absent confounding factors”) has been the source of rich discussion in the forum over the years. Mostly, I’ve learned more from our practitioner members more than I ever taught about this. Specifically, why does a bond “trade rich” or “trade cheap” relative to its theoretically fair value? It turns out there are many reasons, although I tend to bucket them into fundamental and technical.
  • General Risk: TI BA II+ Calculator: Essential Settings (TIBA – 01) Because the exam is time-constrained, fluency with your calculator is essential. This series begins with the essential settings. Sometimes a new candidate is doing the right steps but is not aware their calculator settings have influenced the result. You need to know your toolset. 

  • R Programming: Introduction: Factors (R Intro-04) Factors are vectors that store categorical data, including ordinal values. Factors are extremely common in data analysis; for example, male/female, months of the year, educational brackets. This video has three parts: 1. The theory of Factors (so we better understand their behavior), 2. A very simple example (male, female), and 3. A more realistic example where I plot derivatives trading volume by weekday during the month of Jan 2019.

In the forum (highlights only)

  • Bodie’s APT: Abhinav asked a question ( that goes to a common source of confusion in Bodie’s APT (I wrote about this more extensively two years ago at Here is a very general question: is there any mechanical difference between the capital asset pricing model (CAPM) and the multi-factor arbitrage pricing theory (APT) model? My word “mechanical” is added so that I can say, no, there is not a difference that really matters. The multifactor CAPM is similar enough to the multifactor APT and the CAPM is just a special case where the only factor is the market portfolio’s excess return. Differences are subtle and tend not to be exam-worthy. Our chief concern is that expected returns are a reward for exposure to risk. In these linear models, the relationship is a simple multiplication: sensitivity multiplied by factor; e.g., β × ERP. Abhinav asked an understandable question because there are two ways to represent CAPM (and the APT). Say our beta, β is 1.2; our riskfree rate, Rf = 1%; and the equity premium, E[ERP] = E[R(m) – Rf] = 4%. First is the more familiar expected return: E[Rp] = Rf + β*ERP = 1%+1.2*4% = 5.8%. Second is the less familiar: R(p) = E[Rp] + β*F(ERP) = 5.80% + 1.2*F(ERP), where F(ERP) is the factor’s surprise such that E[F(ERP)] = 0. The second is how the APT is presented. The actual question is about gross versus excess returns, but the answer depends on seeing there is one model with two different presentations.

bodie multi-factor

  • Counterparty credit risk terms: Adele asked (at about a sentence in Lynch’s reading on Stress Testing. Here is the sentence: “Since most financial institutions will do some form of stressing current exposure, it is tempting to use those stresses of current exposure when combining the losses with loans or trading positions. The analysis above shows that expected exposure or expected positive exposure should be used as the exposure amount, and that using current exposure instead would be a mistake. In fact, the use of current exposure instead of expected exposure can lead to substantial errors.” To agree/disagree with this, we need to be clear on the differences between current, expected, expected positive, and potential future exposure. I am currently writing fresh questions on counterparty risk (for a new Gregory sequence) and I’m starting with questions that test an understanding of the definitions of the counterparty risk terms that are used in applications; e.g., here is last week’s
  • Malz’s cash flow waterfall (aka, credit scenario analysis of a securitization): Nansverma asked a good question (at about Malz’s securitization example (Malz Table 9.1). Questions like this can be tough, but I do appreciate the engagement. Many candidates will read the text and skip the numbers, but I don’t see how you can really grok the securitization without going through the numbers. How are the excess spread and equity cash flows determined exactly? I was only able to answer the question because I much earlier re-created Malz’s Table in a spreadsheet, which can be found (downloadable) at the top of the solution.

Industry News

  • Antifragile: I always enjoy Eric Falkenstein’s writing, he just blogged for the first time in months: Why Taleb’s Antifragile Book is a Fraud I find this piece compelling, including the assertion that “incoherence is Taleb’s explicit strategy.” But I share it because it happens to contain so many references to core FRM concepts. With only a few words, he (inadvertently?) maps to at least a dozen FRM concepts or topics!
  • Global skills: Coursera published its inaugural Global Skills Index (my pdf copy is here at I started BT because I wanted to be in the business of helping build skills to get better jobs. My passion project is data science. I’m actually not too surprised to read their finding that “Finance surprises with below-average skills performance. Despite its pursuit of digital transformation, Finance ranks second to last in Business (#9) and Data Science (#9), and hovers near the middle in Technology (#5).”


  • Theranos: I continued to be fascinated by the story of Theranos and I hope it will one day become an FRM case study (in particular for lessons on governance and culture). I still recall listening to Jason Calacanis, well before John Carreyrou broke the story, speculate that her adventure would end in tears because he saw too many red flags (e.g., Google’s venture capital had taken a pass because they couldn’t get past the first step in due diligence). The point is, so many people could have known, if they really wanted to. In any case, there is the NY Times with a guide: Theranos and Elizabeth Holmes: What to Read, Watch and Listen To Also: Case study: Lessons learned from Theranos’ corporate culture


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