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.
4 documents
This tutorial explains how conditional volatility in an ARCH or GARCH process can produce return series with heavier tails than a normal distribution. It simulates a GARCH(1,1) series, compares its tail behavior with Gaussian samples, and outlines a…
This lecture presents parameter estimates as uncertain quantities that can change with new observations or with the sample window. It suggests measuring that instability by estimating a statistic on multiple subsets of data and examining how the resulting…
The document surveys measures of how widely observations vary around a central value. It defines the range, mean absolute deviation, variance, and standard deviation, noting that standard deviation is expressed in the same units as the observations and that…
The document introduces autoregressive models, which predict a time series from its own lagged values, and explains that meaningful estimation requires covariance stationarity: a stable finite mean, variance, and lagged covariance over time. Financial series…