Latest posts
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What Is a Risk-Neutral Measure? (And Why It Matters)
“What is a risk-neutral measure, and why is it important?” It’s one of the most common opening questions in a quant interview, and one of the most commonly fumbled. Not because candidates don’t know the definition — they do — but because the definition, recited alone, doesn’t show you understand it. This is the answer
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What CVA Actually Is (Explained Like on a Desk, Not in a Paper)
CVA gets explained in one of two ways. Either it’s a formula in a paper, wrapped in enough notation that you finish no wiser about what it’s for. Or it’s a one-liner — “the market value of counterparty credit risk” — that’s technically correct and completely useless. Neither tells you what a CVA desk actually
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Put-Call Parity: The Free Lunch Check Every Pricer Needs
Most of what you know about option pricing depends on a model. Black-Scholes needs lognormal returns. Heston needs its own dynamics. Every one of them can be wrong, and when they are, the prices they produce are wrong with them. Put-call parity is different. It holds without assuming anything about how the stock behaves —
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Black-Scholes From Scratch: The Derivation That Actually Makes Sense
Everyone can quote the Black-Scholes formula. Far fewer can say what it is — where it comes from, why those two normal CDFs are there, and what would have to be true for it to be wrong. The formula is not a model of what stocks do. It’s the answer to a narrower and much
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The Change of Measure (P vs Q), Explained Without the Measure Theory
The first time you see the Black-Scholes derivation, one thing should bother you: the stock’s expected return vanishes. You start with a stock you believe will return 12% a year, you turn the crank, and the answer depends on the risk-free rate instead. Your view on the stock — the thing you’d think matters most
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Monte Carlo Option Pricing in Python: A Practical Guide
You can derive Black-Scholes on a whiteboard and still freeze the first time someone asks you to put a number on the screen. That gap — between the formula and the working price — is where most quant work actually lives. Monte Carlo is the tool that closes it. This is a hands-on walkthrough of