arXiv:2606.16961v1 Announce Type: new Abstract: We present a convolutional variational autoencoder for cryptocurrency implied-volatility surfaces, together with a deployable predictor that combines it with a quadratic smile re-fit through a deterministic per-tenor routing rule. Trained on 6,034 fully-filled hourly Binance Options surfaces of BTC and ETH spanning May-October 2023 and parameterised on a common $6 \times 7$ tenor-delta grid, the model attains a hidden-cell surface-completion RMSE in the 0.94-1.56 vol-point range across both markets and mask rates 10-50%. The hybrid predictor atta
The rapid growth and increasing sophistication of the crypto derivatives market necessitate advanced computational tools for risk management and prediction.
This development allows for more accurate forecasting and hedging of volatility in cryptocurrency markets, impacting institutional participation and financial product development.
The ability to accurately model cryptocurrency implied-volatility surfaces is significantly improved, offering better insights into market dynamics and pricing.
- · Quantitative trading firms
- · Crypto exchanges offering derivatives
- · Hedge funds specializing in crypto
- · High-frequency traders
- · Less technologically advanced market makers
- · Retail traders without access to sophisticated tools
Increased availability of accurate crypto volatility predictions.
Enhanced liquidity and institutional engagement in crypto derivatives markets due to better risk management.
Potential for new structured financial products based on crypto volatility, further integrating crypto into broader financial systems.
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Read at arXiv cs.LG