GARCH Risk Management refers to the application of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to forecast financial asset volatility and integrate these predictions into risk assessment frameworks. This approach is particularly relevant for crypto markets due to their characteristic volatility clustering and non-normal return distributions.
Mechanism
GARCH models analyze historical price data to estimate time-varying volatility, identifying periods of heightened or reduced market fluctuations. These estimated volatility parameters are subsequently incorporated into risk calculation systems, such as Value-at-Risk (VaR) or Expected Shortfall models, to generate more accurate and adaptive risk measures. The mechanism provides a statistical basis for predicting future price dispersion.
Methodology
The methodology involves selecting an appropriate GARCH model specification, parameter estimation using extensive historical market data, and rigorous backtesting of model performance against actual outcomes. Risk managers use GARCH-derived volatility forecasts to set dynamic margin requirements, adjust trading limits, and optimize portfolio hedging strategies. This data-driven approach enhances the resilience of trading systems against unexpected market movements in digital assets.
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