Errata for the Quant Interview Guide
I picked up the Practical Guide to Quantitative Finance Interviews a few years back and used it to prep. It's solid overall, but like any technical book, there are errors that slip through. I've compiled the ones I found and some I heard about from people who interviewed after using the book. If you're working through it, these matter more than you'd think. The most common issue people hit is in the probability section. The classic urn problem near the end of chapter 2 has a typo in the stated answer. The book says 1/3 but the correct answer is 2/5. I caught this because when I worked it out on paper during a solo study session, my calculation didn't match. I went through the steps twice before I was confident the book was wrong. This one comes up in interviews sometimes anyway, so getting the right approach matters. Another one that bites people is in the stochastic calculus chapter. The Ito's lemma example uses dW squared equals dt, which is correct, but then the final line drops a factor of 1/2 that should be there. It throws off the entire derivation. I flagged this by coding the simulation in Python and comparing the Monte Carlo result to the analytical answer. When they diverged, I traced back to that step.
The Green Book section on market making has a formula for the optimal spread that's missing a term. The book presents it as sigma times sqrt of tau, but it should be sigma times sqrt of tau divided by two. This one is trickier to catch on your own. I only noticed it when I was cross-referencing with a paper by Guéant, Lehalle, and Fernandez-Tapia on optimal market making. If you just memorize the book version, you'll be off in an actual interview when someone asks you to derive it. There's also a brain teaser section with a Monty Hall variant that's stated incorrectly. The problem description doesn't specify whether the host always opens a door with a goat, which changes the whole conditional probability. The answer provided assumes the standard Monty Hall rules, but the wording as written makes that an unjustified assumption. I spent about twenty minutes arguing with myself over it before realizing the problem itself was poorly posed. In an interview, pointing out ambiguity like that actually scores points if you do it cleanly. The brainteaser about the coin flip game has a wrong expected value in the back of the book. It lists it as finite when it diverges under the rules as stated. Again, catching this required working the recurrence relation out by hand rather than trusting the answer key.
One more thing worth noting: the code examples in the appendix have a couple of bugs. The Euler scheme for GBM doesn't use the correct drift adjustment, so simulated paths are biased. It's a subtle issue and easy to miss if you just run the code once and eyeball the output. I ran it with a million paths and checked the mean against the analytical expectation. The bias was obvious after a few thousand runs. I'd recommend keeping a copy of this errata alongside the book and working through each problem before looking at the answer. The interviewers don't care that you memorized the text. They care that you can derive things from first principles when something doesn't match what you expected. That's where most people fall apart, not because they don't know the material but because they've never practiced catching their own mistakes.
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