Paper: arXiv 2311.04727

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.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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).

Research Flowchart

  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"];

Cited References (44)

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  6. Adam: a method for stochastic optimization (2015)
  7. Gaussian Error Linear Units (GELUs) (2020)
  8. Volatility is rough (2018)
and 36 more
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  2. Volatility forecast comparison using imperfect volatility proxies (2011)
  3. What is stablecoin?: A survey on price stabilization mechanisms for decentralized payment systems (2019)
  4. A hybrid volatility forecasting framework integrating GARCH , artificial neural network, technical analysis and principal components analysis (2018)
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  7. Deep learning in predicting cryptocurrency volatility (2022)
  8. A GMM approach to estimate the roughness of stochastic volatility (2023)
  9. Factors affecting institutional investors to add crypto-currency to asset portfolios (2021)
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0 of 44 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.