market_volatility_transformer
Overview
market_volatility_transformer is a specialized time-series model designed to predict realized volatility in equity and crypto markets. By processing a 168-hour context window of price action and order book depth, the model outputs a probabilistic forecast for the next 24 hours of market turbulence.
Model Architecture
The model utilizes an Encoder-Decoder Transformer optimized for multi-variate time-series.
- Encoder: Processes historical lags (Open, High, Low, Close, Volume, and RSI) to create a temporal representation of market sentiment.
- Decoder: Employs masked self-attention to predict the volatility distribution sequentially.
- Attention Mechanism: Uses sparse attention to focus on critical "flash" events (spikes in volume) while ignoring low-volatility noise.
- Output Layer: Maps the final state to a Student's T-Distribution, allowing the model to capture "fat tails" common in financial crises.
Intended Use
- Risk Management: Estimating Value-at-Risk (VaR) for institutional portfolios.
- Option Pricing: Informing implied volatility surfaces for derivative hedging.
- Liquidity Provision: Assisting automated market makers (AMMs) in adjusting spreads during high-risk periods.
Limitations
- Exogenous Shocks: Cannot account for "Black Swan" events such as sudden regulatory changes or geopolitical conflicts not reflected in historical price data.
- Stationarity: Assumes that the underlying market dynamics remain consistent with the training window; performance degrades during structural regime shifts.
- Data Latency: Optimized for hourly data; not suitable for high-frequency trading (HFT) environments.
