Who this is for. Options traders and risk quants reading the volatility literature, and students approaching derivatives research. Assumes Black-Scholes level background.
How to use it. Read the steps in order; each one assumes the previous. The “why here” line says what the step adds. Where a step is a tool, run it on your own numbers before moving on. The exemplar papers at the end are the archive’s highest-rigor papers for the path’s methods; read two of them with the checklists in hand.
Steps
- Options-Implied Volatility: A Practical Research Primer
Implied volatility for researchers: what IV actually is, surface anatomy, the variance risk premium, using IV as a signal, and the data pitfalls that corrupt options research.
Why here: The surface, its conventions, and what it encodes.
- Microstructure Noise and Realized Volatility
Why high-frequency volatility estimates explode: bid-ask bounce, discreteness, the signature plot, noise-robust estimators, and the practical sampling rules for realized volatility.
Why here: Measuring realized volatility without being fooled by noise.
- A Visual Map of Volatility Forecasting Research
A Visual Map of Volatility Forecasting Research: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored.
Why here: The forecasting literature mapped by score: GARCH to HAR-RV to deep learning.
- Regime Dependence: When a Historical Edge Stops Working
Regime dependence in trading strategies: why edges are conditional on market states, how to detect regime-carried backtests, decay vs regime-shift diagnosis, and what to do when an edge goes quiet.
Why here: Volatility regimes and why models fitted across them mislead.
- Auditing a Monte Carlo: Method Notes from a Leveraged-ETF Simulation
Every technique used to take four Monte Carlo simulation scripts apart, find what they were actually measuring, and rebuild them — with the real numbers each step produced.
Why here: Checking a simulation before trusting a price from it.
- Volatility Targeting: Benefits, Hidden Leverage, and Drawdown Risk
Volatility targeting mechanics: why scaling by inverse vol has worked, the leverage it quietly embeds, gap risk and vol-spike deleveraging, and the estimator choices that change everything.
Why here: Using the forecast: benefits, hidden leverage, drawdown risk.
- Monte Carlo Equity Curve Simulator tool
Simulate thousands of trading equity curves from win rate, reward-to-risk, and position size. Percentile bands, drawdown statistics, and risk of ruin — all in your browser.
Why here: Simulate what volatility targeting does to a path.
Related
All paths: learning paths. Methods, with their own guides and exemplars: methods pages. Programmatic access to the papers: agents page.