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VolatilityGARCHMachine LearningTime SeriesPythonQuantitative FinanceCorrelation Analysis
Volatility Estimation Framework
A comprehensive volatility estimation tool combining traditional statistical methods with machine learning for multi-timeframe analysis.
GARCH, EGARCH, Neural Net
Models
LSTM, Attention
Features
YF, Alpha Vantage
Data Sources
# Methodology
• GARCH Family Models: Implements GARCH(1,1), EGARCH, and GJR-GARCH specifications with asymmetric volatility clustering detection. The models use maximum likelihood estimation with robust standard errors and incorporate exogenous variables such as trading volume and order flow imbalance for enhanced predictive power.
• Neural Network Architecture: Features a dual-path LSTM network with attention mechanisms that processes both price sequences and market microstructure data. The network uses temporal convolution layers for short-term pattern detection and self-attention for long-range dependency modeling, enabling capture of both intraday and interday volatility dynamics.
• Uncertainty Quantification: Employs Monte Carlo dropout and Bayesian neural network techniques to generate predictive distributions rather than point estimates. The system calculates confidence intervals using bootstrap methods and implements model uncertainty decomposition to separate parameter uncertainty from model misspecification.
• Real-time Adaptation: Features online learning capabilities with incremental parameter updates using stochastic gradient descent. The system monitors forecast accuracy and automatically triggers model retraining when performance degrades, ensuring robust performance during market regime changes and structural breaks.
• Neural Network Architecture: Features a dual-path LSTM network with attention mechanisms that processes both price sequences and market microstructure data. The network uses temporal convolution layers for short-term pattern detection and self-attention for long-range dependency modeling, enabling capture of both intraday and interday volatility dynamics.
• Uncertainty Quantification: Employs Monte Carlo dropout and Bayesian neural network techniques to generate predictive distributions rather than point estimates. The system calculates confidence intervals using bootstrap methods and implements model uncertainty decomposition to separate parameter uncertainty from model misspecification.
• Real-time Adaptation: Features online learning capabilities with incremental parameter updates using stochastic gradient descent. The system monitors forecast accuracy and automatically triggers model retraining when performance degrades, ensuring robust performance during market regime changes and structural breaks.
Core Model
GARCH(1,1) + EGARCH + LSTM Neural Networks
# Data Source
Yahoo Finance, Alpha Vantage, CoinGecko API