Welcome to our Week in Risk blog! Our new FRM practice questions, written by CEO, David Harper, discuss country risk measures from Topic 4 of the FRM syllabus and collateralization agreements from Topic 6. David also recorded new YouTube videos covering different aspects of fixed income. We’ve also included helpful discussions from our FRM forum. Enjoy and have a great week!
1. Valuation & Risk Models: P1.T4.915. Country risk measures and historical instances of sovereign default (Damodaran) https://trtl.bz/2UO5nYf
2. Credit Risk Measurement & Management: P2.T6.906. Features of a collateralization agreement (Gregory Ch.6) https://trtl.bz/2UQHy23 This set of questions continues a fresh review of Gregory’s chapter on collateral. The goal of the first two is to give practice to the featured terms: threshold, initial margin, minimum transfer amount (MTA), rounding, and haircuts (haircuts have high exam testability as a key feature of collateral). Then the third question gives practice with a relatively simple numerical example. As is so often the case, we need numerical examples to really see these terms in action.
1. Valuation & Risk Models: Fixed Income: Infer discount factors, spot, forwards and par rates from swap rate curve (FRM T4-25) https://trtl.bz/2vfGBRO If the early Tuckman chapters do one thing, they make you realize that there are several different types of interest rates. I’m candidly happy with this video simply because in about 15 minutes I show how the key interest rates (swap, spot, forward, par yield, not to mention discount factors) are functions of each other. If fixed income is a cooking class, these are the essential ingredients (e.g., salt, butter) that show up in the later advanced recipes. Or should I say they are the utensils (e.g., knife, spatula). Hmmm, I’ll need to work on this metaphor!
2. Valuation & Risk Models: Fixed Income: Maturity versus Bond Price (FRM T4-26) https://trtl.bz/2XCalod Bonds pull to par, right? For example, a bond’s price of $105.00 decreases as it matures because it gets pulled to par; a bond’s price of $93.00 increases for the same reason. Do you recall the necessary assumption behind the pull to par rule? It’s the assumption of Unchanged Yields. But what if we assume an Unchanged Term Structure; in such an alternate scenario, how does a bond’s price change as it matures? This video illustrates unchanged term structure and shows why the bond price increases (decreases) as the bond matures if the coupon rate is less than (greater than) the expiring forward rate.
1. Risk-neutral pricing: Forum member, Jaskarn, asked a good question about the credit value adjustment (CVA) that touches on a theme in derivatives: https://trtl.bz/2ULV72V. The question is about Jon Gregory’s statement that CVA is compatible with either the financial product’s actuarial price or the risk-neutral price. This is thematic because risk-neutral pricing is a topic in John Hull’s derivative assignment and in Tuckman’s review of interest rate term structure models. If that’s not enough, it also appears in Malz’s discussion of default intensity models. FRM Part 2 candidates do want to understand, for example, the difference between risk-neutral and real-world default probabilities.
2. BSM assumptions: RYS asked about the Black-Scholes Merton option pricing model (BSM OPM) assumptions https://trtl.bz/2ULVKtj. This might seem academic, but the BSM shows up in a lot of places and its assumptions have been borrowed into unexpected places. For example, many public companies use BSM to estimate their employee stock option (ESO), although an FRM candidate might understand the arguments in favor of the binomial model for such a purpose. Many analysts use BSM to value real options. In David’s previous life, he consulted to Boards on valuation so he has some experience defending these models. It’s really important to understand the assumptions of the BSM, because they are very restrictive. Actually, they are unrealistic. But we don’t think that renders the BSM useless. All models, to some degree, are unrealistic. Models simplify. Also, the BSM models assume the lognormal property of the asset price, a so-called diffusion process in continuous time. This might be the most common assumption about stock price dynamics, if only because it’s the first one taught. But it is hardly (not even close) the only model for stock prices.
3. Foreign exchange (FX) quote convention: Forum member, JanaRad, asked for help identifying the arbitrage trade when a foreign exchange (FX) futures contract is mis-priced https://trtl.bz/2UQzC0E. We don’t think this is easy for anybody, except maybe currency professionals. Along the way, we’ve provided some input to GARP: we helped establish the consistent use of FX currency quote conventions. In this vein, you will see authors refer to the foreign-versus-domestic currency. However, our recommendation is to ignore that in favor of the base-versus-quote distinction, which follows the market convention. In the currency priority rankings, for example, the Euro ranks above the dollar; therefore, proper is EURUSD $1.126 where EUR is the base and USD is the quote. In the cost of carry model (and its interest rate parity variation), then, the EUR is the commodity, like cotton or copper or the S&P 500 Index is a commodity. And the USD is the price of the commodity. For us, the key to mastery here includes these two items: (i) clarity on the FX quote convention and (ii) treating the base currency as if it were the commodity. Then the logic of the arbitrage follows naturally.
4. Quantile-quantile (QQ) plot: Every year there are questions about the QQ plot; e.g., https://trtl.bz/2UQzC0E. Its inclusion in the FRM is completely understandable: quantitative risk is a lot about the specification (and estimation) of a future distribution. We don’t mean to over-simplify, but isn’t that the hard work? If we can successfully specify a future distribution, we’ve overcome the problem of Knightian uncertainty https://en.wikipedia.org/wiki/Knightian_uncertainty and many business needs become utterly solvable; e.g., risk capital attribution. But it’s audacious to pretend to be able to anticipate the future. In practice, the exercise often amounts to the comparison of a lumpy, messy historical (aka, empirical) distribution to a clean parametric function. Enter the utility of the QQ plot! However, CEO, David Harper, confesses that he finds them hard to interpret. Only by talking with candidates on the forum did he start to grok them a bit. If you are new to QQ, here is our recommendation: study them sufficiently so that you know which features betray positive/negative skew and heavy/light tails in comparison to the normal distribution. For example, what graphical feature betrays left (aka, negative) skew; can you quickly identify negative skew?
1. A random forest is many decision trees. One of the best assigned readings in Current Issues (Part 2 Topic 9) is Hal Varian’s Big Data: New Tricks for Econometrics (https://trtl.bz/2VhNq3H). It contains a nice introduction to decision trees using the famous Titanic dataset. In January David spent a week at a data science bootcamp where we participated in a contest using the Titanic dataset, it’s a popular training set. Our best source for good articles is Medium. For example, this week was published The Complete Guide to Decision Trees https://trtl.bz/2GvFGSB. Also, Random Forests for Complete Beginners https://trtl.bz/2GtyVR1 (If you want a GENTLER introduction: Decision Trees–An Intuitive Introduction https://trtl.bz/2CQ7Bui; for something more dense, here is a good tutorial from Analytics Vidhya https://trtl.bz/2GqwajA).
2. The hard problem of helping people reskill without taking forever. CEO, David Harper, started Bionic Turtle because he wanted more than anything else to be in the business of helping people add skills to get better jobs. It’s a very different perspective than thinking of yourself as a publisher of learning materials. Publishing content is easier than helping customers develop skills. So he was very interested in this: Why Companies Are Failing at Reskilling (In a tight labor market, employers from Amazon to JPMorgan are trying to get better at retraining the workers they have) https://trtl.bz/2GyhsH4. When David started BT many years ago, he attended several eLearning conferences (he even presented at one where he shared his object-oriented software kit, which did not take off with customers). Even when he was new to the field, David could tell that too many approaches were simply too slow: the people and time and the cost of a traditional instructional design can be rather weighty and lumpy, for lack of a better word. That’s even more true today. Speed is absolutely essential. If you can’t be fast, you can’t make it work. As the article says, “Employers are still trying to master the challenge of mapping the skills of their current workers, identifying the skills required of their future workforce and filling the gaps between the two. By the time many companies figure out exactly who they need, it’s often too late to invest the necessary time and money into retraining.“
3. Cryptocurrency risks: CEO, David Harper, confesses (or is It a boast?) that he holds some cryptocurrencies; we think it’s okay to allocate a very small percentage of your portfolio to highly speculative investments. David sold most of his bitcoin subsequent to its peak (at a price of $10,636 per coin, so his timing was neither terrific nor terrible). The problem is that David doesn’t know how to analyze cryptocurrencies a fundamental investment: if the only thing he can do is watch price and volume, then he is just being a technician, or worse, just a timer. David probably just doesn’t know enough about the sector. In any case, here is an informative summary of regulator perspectives on crypto by John Hintze at the GARP site: The BIS-BCBS-FSB View of Crypto (Risk guidance takes shape from a series of the global oversight bodies’ pronouncements) https://trtl.bz/2UPowcs.