Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter

ArXiv ID: 2311.04727 “View on arXiv”

Authors: Unknown

Abstract

We extend the application and test the performance of a recently introduced volatility prediction framework encompassing LSTM and rough volatility. Our asset class of interest is cryptocurrencies, at the beginning of the “crypto-winter” in 2022. We first show that to forecast volatility, a universal LSTM approach trained on a pool of assets outperforms traditional models. We then consider a parsimonious parametric model based on rough volatility and Zumbach effect. We obtain similar prediction performances with only five parameters whose values are non-asset-dependent. Our findings provide further evidence on the universality of the mechanisms underlying the volatility formation process.

Keywords: LSTM, Rough volatility, Volatility prediction, Cryptocurrency, Zumbach effect

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts like quadratic rough Heston, fractional Brownian motion, and the Zumbach effect, while also conducting a comprehensive empirical analysis on a large dataset of 213 cryptocurrencies with specific train-test splits and rigorous evaluation metrics (MSE).

  flowchart TD
    A["Research Goal<br>Forecast Cryptocurrency Volatility<br>using ML & Rough Volatility"] --> B{"Methodology"};
    B --> C["Universal LSTM Model<br>Trained on Asset Pool"];
    B --> D["Rough Volatility &<br>Zumbach Effect Model"];
    C --> E["Data: BTC/ETH/Altcoins<br>2022 Crypto-Winter Period"];
    D --> E;
    E --> F["Computational Process"];
    F --> G["Comparison of<br>Prediction Performance"];
    G --> H["Key Findings/Outcomes<br>Universal LSTM Outperforms Traditional Models<br>Rough Volatility Model Achieves Similar Performance<br>with Only 5 Non-Asset-Dependent Parameters"];