Welcome to our Week in Risk blog! The May FRM exam is less than two weeks away and our forum is getting very busy with questions and helpful discussions. This week, we’ve highlighted a few of those forum discussions here, along with new YouTube videos on fixed income and new de Servigny and Gregory practice questions. Enjoy and have a great week!
1. Valuation & Risk Models: P1.T4.917. External credit rating scales (de Servigny Ch.2) https://trtl.bz/2vDjAZe This begins a new question series in Topic 4 of the FRM curriculum describing external rating scales, the rating process, and the link between ratings and default.
2. Credit Risk Measurement & Management: P2.T6.908. Credit exposure metrics (expected exposure and potential future exposure) (Gregory Ch.7) https://trtl.bz/2vBshDj Continuing our question series in Topic 6 of the FRM curriculum, we describe and calculate the following metrics for credit exposure: expected mark-to-market, expected exposure and potential future exposure.
1. Valuation & Risk Models: Fixed Income: Yield to Maturity (FRM T4-29) https://trtl.bz/2JdlNCu Yield is shorthand for yield to maturity, and we’ve covered that in a previous video quite deeply, but that was based on John Hull. We are currently in the subseries of Bruce Tuckman so here, David will share a different perspective on yield by Tuckman, who is arguably more expert in fixed income.
2. Valuation & Risk Models: Fixed Income: Term Structure Scenarios (FRM T4-30) https://trtl.bz/2GWcYdM When we go to break down the components of a bond’s profit and loss or return, we will find that an important assumption that we make is how the term structure behaves over time. Broadly, following Bruce Tuckman, we have three scenarios from which to choose, which David Harper will discuss in this video.
1. How wide is the operational risk bucket? Forum member, Stuart D Moncrieff raised a good question about whether unanticipated costs associated with a reorganization should be counted as an operational loss type(s). Does it matter if it’s a restructuring as opposed to a mere reorganization? What about incentive plan impacts? https://trtl.bz/2PQthgi
2. Taylor Series. Maxim Rastorguev asked about the apparent difference in the application of the Taylor series to options as opposed to bonds. The Taylor expansion is easily one of the most discussed concepts in our forum over the last decade. It governs risk approximation in bonds, options, and even portfolio VaR. See https://trtl.bz/2PQtqjQ.
3. GARP Practice Papers. As the exam is only two weeks away, members are discussing some of GARP’s prior exam questions. A classic confusion *again* arose this week around inconsistent default probability applications (https://trtl.bz/2PM2bXA) but even the latest paper contains imprecisions; e.g., here the VaR backtest trade-off is imprecisely explained https://trtl.bz/2PPSAin.
4. Options are interesting. We had some interesting threads on options. Forum member, abhinavkhanna asked why the put is naturally better suited to early exercise https://trtl.bz/2PSYZJO. Maxim Rastorguev makes a delightful observation that the sum of call delta and put delta must be one (cool!), except under a specific circumstance, but we can conveniently quantify the difference https://trtl.bz/2J1vaWy
1. LIBOR. It’s been over a decade since the LIBOR scandal first began with the earliest reports of its manipulation, but the transition to an alternative rate(s)–in particular the Secured Overnight Financing Rate (SOFR)– is finally underway. The SOFR is the subject of an FRM Part 2 Current Issues reading. The transition is marked by Hull’s 10th edition where alternative risk-free rates. Here is PIMCO on The Future Without Libor, Part 1 https://trtl.bz/2vDEjMn and Part 2 https://trtl.bz/2vG6VEV.
2. Climate Risk. Climate risk–in particular as an environmental, social and governance (ESG) factor–seems to be gaining traction with institutional investors. Wired: Companies can predict climate catastrophes for you as a service https://trtl.bz/2PKoZXJ “Jupiter explicitly incorporates climate change into its models for catastrophe risk, both proprietary and public, and then offers that knowledge to the kind of people who might lose money when the floods, fires, storms, and heat waves really kick in … they come down to this: Hedge. Understand the price of the risk, and aggregate it, maybe into a financial instrument of some sort.“
3. Decision science: I think risk managers need to be aware of developments in data science, which includes decision science and its inevitable impact on risk management. Isn’t part of the job helping the company to make better decisions? Here is McKinsey: Three keys to faster, better decisions https://trtl.bz/2PM4aLz. They distinguish between Big-bet decisions (infrequent but high-risk), cross-cutting decisions such as pricing (frequent, high-risk and cross-functional), and delegated decisions (frequent but low-risk).