Ready-to-read documents: analyses, reports, and technical summaries on actuarial science, finance, and statistics.
10 notes
The reference I built to prepare for Exam P. Covers general probability, random variables, and multivariate distributions, each topic with actuarial intuition, not just formulas. Includes solved problems with analysis of why the distractors mislead.
From theory to production: how insurers actually estimate claim frequency, severity, and policy lapse. This note walks from exponential families and link functions through validation with ROC curves, Gini coefficients, and relativity tables. Three full case studies: auto, life, and workers' comp. Personal actuarial synthesis note.
Bridge document connecting Adam's Law, Eve's Law, mixed distributions (Poisson-Gamma, mixed exponential), and compound distributions. Includes SOA-style solved problems with analysis of why each distractor fails. The piece that links conditional variance to credibility theory.
Lee-Carter model reestimation using Mexican mortality data. SVD decomposition of the rate matrix, time projection of the kappa index, and its direct implication for life tables and life insurance reserves. Connects demography to actuarial practice.
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.
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.
Same question, two ways of thinking: the classical approach says there's not enough evidence, the Bayesian one says B is almost certainly better. This summary explores what happens when you stop relying solely on the p-value and start thinking in terms of expected utility to decide. Personal statistical exploration project. Available in both languages.
Five years of Delhi temperature told through data: finding the seasonal pattern hidden in the noise, separating real trend from random variation, and building a model in R that can anticipate what comes next. The kind of exercise where statistics stops being abstract and becomes weather. Course project for Survival Analysis and Time Series, UNAM.
What do an actuary and a volcanologist have in common? Both try to predict rare events with enormous consequences. This note reviews how eruption intervals for Popocatepetl and Galeras are modeled with log-normal distributions, Markov models, and renewal processes. The open questions at the end are genuinely fascinating. Personal applied probability note.