Variational Autoencoders in Volatility and Option Pricing

Key Takeaways

• VAE captures skewness and fat tails in NIFTY50 returns
• LightGBM predicts implied volatility using option and sentiment features
• Multi-Level Monte Carlo reduces pricing computation time versus standard Monte Carlo
• Framework outperforms Black‑Scholes across calls, puts, and long expiries
• No‑arbitrage enforcement remains an open challenge

Pulse Analysis

The Black‑Scholes‑Merton formula has long been the industry standard for option valuation, but its reliance on constant volatility and log‑normal returns limits its realism. As markets exhibit skewed distributions and abrupt jumps, researchers have turned to machine‑learning techniques that can model these complexities. Recent advances in deep generative models, particularly Variational Autoencoders, enable the synthesis of realistic return paths that retain extreme tail behavior, addressing a key shortfall of purely parametric approaches.

In the proposed pipeline, a VAE is trained on historical NIFTY50 log‑returns, learning a latent space that faithfully reproduces both typical and rare market moves. Simultaneously, a LightGBM regression model ingests option‑specific variables—strike, moneyness, time to expiry—alongside sentiment indicators to forecast implied volatility across the surface. These two components feed a two‑level MLMC simulation, which dramatically reduces the number of required paths compared with conventional Monte Carlo while preserving pricing fidelity. The result is a pricing engine that matches or exceeds Black‑Scholes accuracy at a fraction of the computational cost.

For practitioners, this hybrid framework offers tangible benefits: quicker risk‑adjusted pricing, better hedging signals for long‑dated contracts, and a data‑driven method to stress‑test portfolios under extreme scenarios. However, the model does not enforce no‑arbitrage constraints, a regulatory and theoretical requirement that must be addressed before production deployment. Future research may integrate arbitrage‑free regularization or combine the VAE with physics‑informed networks, further bridging the gap between sophisticated AI tools and the rigorous demands of quantitative finance.