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Why volatility isn't just noise but a force that pulls the median away from the mean, and how that makes options worth more than intuition suggests. A walkthrough of geometric Brownian motion, the terminal price distribution, and the "volatility drag" effect. Personal quantitative finance note.
The full path to modeling returns: from choosing between normal and log-normal, through moments and maximum likelihood, all the way to parametric VaR. With goodness-of-fit tests, Q-Q plots, and information criteria so you don't settle for the first distribution that looks right. Financial Markets course note, UNAM.
One document tying four worlds together: how to price what nobody knows the value of (exotic derivatives), how to read the future in Treasury yield curves, how two firms can swap risk through an IRS, and how to build a portfolio that doesn't rely on luck. All solved in Python and bound by the same no-arbitrage logic. Integrative exam for Quantitative Methods in Finance, UNAM.