Andrew Skabar, PhD·Jun 18The One-History ProblemWhat Financial Markets Reveal About Learning from a Single History
Andrew Skabar, PhD·Mar 18From Calm to Crisis: Early Warnings in Portfolio DiversificationHow scenario-aware models generate synthetic portfolios that reveal hidden risks before market stress hits
Andrew Skabar, PhD·Jan 27When Market Extremes Cluster: Modeling Plausible Black SwansWhy tail risk is as much about timing as magnitudeA response icon1A response icon1
Andrew Skabar, PhD·Dec 11, 2025Tracking Tomorrow’s Risk in the S&P500How Return Distributions Shift Day by DayA response icon1A response icon1
Andrew Skabar, PhD·Nov 22, 2025Beyond Volatility: Capturing the Hidden Shape of Market RiskHow structural dynamics in returns reveal more than volatility aloneA response icon8A response icon8
Andrew Skabar, PhD·Oct 22, 2025Beyond the Point EstimateScenario-aware conditional PDFs: a richer view of risk and uncertainty in financial markets
Andrew Skabar, PhD·Aug 30, 2024Generating Realistic Synthetic Financial Time SeriesA non-parametric approach using weighted, variable-width kernels to estimate conditional PDFsA response icon1A response icon1
InTDS ArchivebyAndrew Skabar, PhD·Feb 14, 2024Evaluating Synthetic Data — The Million Dollar QuestionAre my real and synthetic datasets random samples from the same parent distribution?A response icon4A response icon4
InTDS ArchivebyAndrew Skabar, PhD·Nov 15, 2023Graph Data Science for Tabular DataGraph methods are more general than you may thinkA response icon4A response icon4
