New Practice Questions
1. Bodie's APT: Abhinav asked a question (https://trtl.bz/2FtP7S1) that goes to a common source of confusion in Bodie's APT (I wrote about this more extensively two years ago at https://trtl.bz/2FssuwZ). 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 premimum, 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.
2. Counterparty credit risk terms: Adele asked (at https://trtl.bz/2FvtBfI) 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 https://trtl.bz/2u6HSKp
3. Malz's cash flow waterfall (aka, credit scenario analysis of a securitization): Nansverma asked a good question (at https://trtl.bz/2FvqQL4) 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.
External
1. 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 http://falkenblog.blogspot.com/2019/03/why-talebs-antifragile-book-is-fraud.html 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!
2. Global skills: Coursera published its inaugural Global Skills Index https://blog.coursera.org/introducing-the-coursera-global-skills-index/ (my pdf copy is here at https://trtl.bz/2CBb150). I started BT because I wanted to be in the business of helping build 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)."
3. 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, here is the NY Times with a guide: Theranos and Elizabeth Holmes: What to Read, Watch and Listen To https://www.nytimes.com/2019/03/18/arts/television/theranos-elizabeth-holmes.html. Also: Case study: Lessons learned from Theranos’ corporate culture https://trtl.bz/2FwQaSo
- P1.T4.911. Multi-factor interest rate risk models (Tuckman Ch.5) https://trtl.bz/2HBVEMS
- P2.T6.902. xVA components (Gregory Ch.4) https://trtl.bz/2HI9IVi
- Fixed income: Law of One Price (FRM T4-21) https://trtl.bz/2Wo4JgF
- TI BA II+ Calculator: Essential Settings (TIBA - 01) https://trtl.bz/2UOSfOF
- R Programming: Introduction: Factors (R Intro-04) https://trtl.bz/2YgZlgV
1. Bodie's APT: Abhinav asked a question (https://trtl.bz/2FtP7S1) that goes to a common source of confusion in Bodie's APT (I wrote about this more extensively two years ago at https://trtl.bz/2FssuwZ). 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 premimum, 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.
2. Counterparty credit risk terms: Adele asked (at https://trtl.bz/2FvtBfI) 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 https://trtl.bz/2u6HSKp
3. Malz's cash flow waterfall (aka, credit scenario analysis of a securitization): Nansverma asked a good question (at https://trtl.bz/2FvqQL4) 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.
External
1. 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 http://falkenblog.blogspot.com/2019/03/why-talebs-antifragile-book-is-fraud.html 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!
2. Global skills: Coursera published its inaugural Global Skills Index https://blog.coursera.org/introducing-the-coursera-global-skills-index/ (my pdf copy is here at https://trtl.bz/2CBb150). I started BT because I wanted to be in the business of helping build 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)."
3. 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, here is the NY Times with a guide: Theranos and Elizabeth Holmes: What to Read, Watch and Listen To https://www.nytimes.com/2019/03/18/arts/television/theranos-elizabeth-holmes.html. Also: Case study: Lessons learned from Theranos’ corporate culture https://trtl.bz/2FwQaSo
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