Advanced Volatility Models
Analyze and forecast volatility with advanced time series models
Heterogeneous Autoregressive (HAR) Model
The HAR model captures the long memory property of volatility by using different time horizons (daily, weekly, monthly).
- Captures volatility clustering effectively
- Accounts for the long memory property of volatility
- Simple yet powerful modeling approach
- Excellent for single-asset volatility forecasting
Dynamic Conditional Correlation GARCH Model
The DCC-GARCH model captures time-varying correlations between assets, allowing for better portfolio risk management.
- Models time-varying correlations between assets
- Combines univariate GARCH models with dynamic correlation
- Ideal for portfolio optimization and risk management
- Accounts for volatility clustering and spillover effects
Why Use Advanced Volatility Models?
Better Risk Management
Advanced volatility models provide more accurate estimates of future volatility, enabling better risk management decisions and more precise VaR calculations.
Portfolio Optimization
Dynamic correlation models allow for optimal asset allocation that accounts for changing market conditions and relationships between assets.
Trading Strategies
Accurate volatility forecasts can be used to develop trading strategies, improve option pricing models, and optimize position sizing.
Key Features of Our Volatility Models
- Interactive visualization of volatility forecasts and model outputs
- Custom model parameters to adapt to different market conditions
- Support for multiple assets with dynamic correlation estimation
- Portfolio optimization based on model forecasts
- Detailed model diagnostics and performance metrics