Happy New Year, dear reader!

Getting access to high-frequency data in crypto is easier than in any other market. Even if you don’t want to spend a couple of hundred or thousand dollars on historical data, you can set up data collection yourself pretty cost-efficiently and reliably.

We show how to use this high-frequency data to get better forecasts of volatility, compare different volatility forecasting models, and show how to properly diagnose a volatility forecasting model.

Table of Contents

1. Realised Variance from High-Frequency Returns — Our Forecast Target

2. Range-based Variance Estimators — Sometimes, Less is More

3. Sampling Frequency — The Problem with High Frequency Data

4. Rolling Averages and EWMA — Baseline Variance Forecasts

5. Tuning EWMA via QLIKE — A Proper Scoring Rule for Variance

6. GARCH Family — Conditional Heteroskedasticity Models

7. Stochastic Volatility and Kalman Filters — How to Actually Use a Kalman Filter

8. Volatility Forecast Diagnostics — What Model is The Best?

9. Final Remarks — Conclusion, Code, and Discord

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