Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
5,018 documents
The document surveys possible developments in quantitative finance that could gain wider use or appear in future textbooks. It describes machine learning as a newer tool applied to established tasks, including risk analysis, risk management, and algorithmic…
The document raises a regression-design question: whether to exclude a covariate when it has substantially fewer observations than the other variables. The author gives an example in which one variable has 80,000 observations while others have 100,000, and…
The document asks where academics can obtain free data to train an approximation of the Black–Scholes pricing model. It identifies the model inputs the author has in mind: share price, strike, risk-free rate, volatility, and time to expiration. The author…
The discussion considers how Jim Simons may have recognized that market prices contain exploitable structure. Answers suggest that mathematical skill, curiosity about price patterns, and experience with signal detection can help researchers distinguish weak…
The post asks whether QuantLib’s Longstaff–Schwartz Monte Carlo engine for American options can use non-Gaussian random draws, such as Student-t variates, while pricing under a Black–Scholes–Merton process. The author considers changes to distribution and…
The document surveys reasons a quantitative team might choose R for financial data modeling instead of spreadsheets or conventional statistical packages. It emphasizes reusable scripts, access to packages that implement statistical methods, flexibility for…
The document asks how to apply the FDR+ method to select trading rules that appear to outperform. It says the author has computed p-values using a stationary bootstrap, following a cited study, but provides no worked calculation, code, or detailed definition…
The document considers whether ordinary least squares can estimate the parameters of a GARCH model, which are commonly fitted using quasi-maximum likelihood. It summarizes a reference describing explicit OLS estimation for ARCH models through an…
The discussion considers whether Markov chain Monte Carlo is useful for Bayesian inference in quantitative finance. It describes potential applications to estimating hidden-process parameters, including stochastic volatility models, and to inference for…
The document considers whether predicting an asset's current price from other assets has practical value when the market already reports that price. The responses identify data alignment and missing observations as possible uses. A model could estimate…
This community discussion describes several routes into quantitative finance, including transitions from computer science, physics, statistics, and other academic or commercial backgrounds. It stresses that “quant” covers distinct jobs, from derivatives…
The document discusses maximum-likelihood estimation for a linear Gaussian two-factor state-space model of commodity prices, using the Schwartz-Smith framework. The observed log price is modeled as the sum of two latent state components plus measurement…
The document considers how to estimate a house’s current value from property characteristics and an earlier sale date. It contrasts including date directly in a gradient-boosting model with first adjusting historical prices for market inflation, then…
The document discusses how to interpret and empirically assess Geometric Brownian Motion (GBM). Under GBM, log returns follow a normal distribution whose mean combines the drift and a volatility adjustment. The drift is therefore a model parameter: its…
The document questions the assumption behind conventional strategy optimization: that the process generating historical market data remains stable. It notes that out-of-sample checks and periodic reruns can help address overfitting, but may not resolve the…
The document describes a credit-risk data preparation workflow for estimating probability of default. The example defines a default event from arrears, term-expiry, or bankruptcy indicators, recodes the outcome for modeling convenience, and uses stratified…
The document presents an implementation question about fitting a Heston–Nandi GARCH model by maximum likelihood. The author scales log returns, recursively computes conditional variance and standardized residuals, and uses differential evolution to search…
The document describes an R workflow for estimating a CARMA(2,1) model with the yuima package from daily market price data. The initial attempt passes a price column directly into the package and produces an error while constructing the model data. The…
The document presents a time-series modeling problem: an ARFIMA(12,0,12) mean with an eGARCH volatility specification produces plausible one-step forecasts on a sample of 2,000 observations, but extreme mean and variance forecasts after fitting the same…
The document asks why fitted copula models are used to generate new observations after marginal distributions have been transformed to uniform variables and dependence has been modeled. It distinguishes fitting a dependence structure to existing data from…
The document presents a slow nested-loop implementation for pricing European calls, Asian options, digital calls, and up-and-in barrier calls under the Merton jump-diffusion model. It motivates the problem as a comparison with the exponential variance-gamma…
The question concerns a cumulative series of positive integer values across independent entities. Its cross-sectional distributions appear lognormal, suggesting geometric Brownian motion, but the usual GBM specification has normally distributed random shocks…
The document points readers seeking research on currency baskets to two approaches. One cited paper applies principal component analysis to a basket of nine foreign-exchange pairs over a rolling two-month window. It then uses the first principal component as…
The post considers whether regime-switching models can detect market shifts and support trading. It outlines a common approach: model market behavior using states with specified distributions, then estimate a Markov transition matrix. The intended benefit is…