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.
16,761 documents
The document asks how to choose a probability measure when pricing derivatives with Monte Carlo simulation. It starts from risk-neutral valuation, where price is the discounted expected payoff, and notes that simulations may require modeling state variables…
The document discusses choosing the ARCH and GARCH lag orders for a volatility model. One proposed approach estimates a range of candidate orders, checks whether coefficients are statistically significant, inspects residual autocorrelation, and uses…
The document explains how to generate multivariate Gaussian returns with a target covariance structure. Starting with a vector of independent standard normal draws, apply a matrix square root of the desired covariance matrix. A Cholesky factor is one such…
The document examines a proposed monthly regression of year-over-year stock index returns on lagged returns and macroeconomic variables, including credit spreads, inflation, and industrial production. It describes a forecasting setup that uses ARIMA…
The document explains why quasi-Monte Carlo methods use low-discrepancy sequences: these points fill a space more evenly than random samples, reducing large gaps. It defines discrepancy as a measure of how closely the fraction of points in a region matches…
The question seeks a long historical daily closing-price series for the individual companies that made up the Dow Jones Industrial Average, rather than a series for the index itself. The response points to a dataset of intraday one-minute bars covering the…
This exchange diagnoses a dimensional error in a one-step geometric Brownian motion simulation. The closed-form process uses a drift term of (μ − σ²/2)T and a random term of σ√T multiplied by a standard normal shock. The code in the question instead…
The document introduces basis-function selection in Longstaff–Schwartz least-squares Monte Carlo pricing for options on multiple underlyings. In this approach, continuation values are approximated with basis functions during backward induction, which…
The document shows how to generate joint spot and variance paths from QuantLib's Heston process in Python. It constructs the process from initial variance, mean-reversion, long-run variance, volatility-of-variance, correlation, spot, and rate inputs, then…
The document discusses how to evaluate residuals when comparing time-series forecasts, including volatility forecasts from models such as GARCH. It recommends examining autocorrelation: if errors contain predictable structure, the model may be missing a…
The document explains a data choice for studying market expectations of Federal Reserve policy. Actual 30-day Fed Funds futures reference the average effective rate over a calendar month, so they do not offer a contract with a continuously rolling 30-day…
The document raises a problem in simulation-based testing of investment strategies that use valuation measures such as price-to-book or dividend yield. A simulation that generates prices while retaining historical values for accounting variables may create…
The document asks whether historical listed SPX options data from the CBOE data shop is reliable enough for backtesting, especially regarding missing or incorrect prices and Greeks. The response recommends checking a small sample of purchased contracts…
The document considers whether historical equity prices should be adjusted for ordinary dividends when backtesting strategies, alongside the existing adjustments for splits and special dividends. Its practical guidance separates signal construction from…
The document proposes revisiting the portfolio construction rule at every rebalance instead of committing to one allocation scheme throughout an investment horizon. It contrasts optimized approaches, such as minimum variance and maximum Sharpe portfolios,…
The document explains why multiplying single-period variance by the number of periods can misstate the variance of compounded horizon returns when returns are serially correlated. Under covariance stationarity and a common single-period variance,…
The document presents a beginner’s question about why summing rate-of-change values multiplied by trading signals gives a different return from the price change between a stated entry and exit. The example uses Bollinger Bands, stochastic indicators, and…
This collection of answers challenges widely used ideas in quantitative finance, including correlation-based diversification, CAPM as an allocation rule, normally distributed independent returns, value at risk, efficient markets, copulas, and perfect delta…
The discussion considers how to scale a large set of technical indicators when training a model across many stocks with different price levels and feature ranges. Suggested approaches include range scaling, mean-zero standardization, and converting skewed…
The document presents four regressions for a series and its first and second differences, then asks which integration level is supported. The examples include lagged-level coefficients, standard errors, and Breusch–Godfrey p-values for residual…
The document explains how to turn a sequence of periodic returns into a continuous index or equity curve starting from a base value. It distinguishes adding return increments from compounding them and gives the accepted answer: multiply the index by one plus…
The document outlines the two-step Fama–MacBeth procedure: estimate cross-sectional return regressions each month, then average each coefficient’s time series. It describes the usual standard error as the sample standard deviation of those monthly estimates…
The document addresses an unexpected result in a Sharpe ratio sensitivity analysis: lowering the assumed risk-free rate appears to lower the ratio for both a strategy and its benchmark. The calculation described converts an annual rate into a daily rate,…
The document raises a data-continuity question for a pair-trading model using futures contracts. It says the training series for each contract has been forward-adjusted and asks whether live prices should receive the same adjustment when the model is tested.…