FHS-VaR, or Filtered Historical Simulation Value-at-Risk, is a statistical method used to estimate potential financial loss over a specific time horizon with a given probability, incorporating dynamic volatility modeling. This approach enhances traditional VaR calculations by accounting for changing market conditions, making it relevant for assessing risk in volatile crypto asset portfolios.
Mechanism
The mechanism begins by fitting a time-series model, often a GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model, to historical returns to capture time-varying volatility. Residuals from this model are then standardized and used in a historical simulation to generate a distribution of future portfolio returns. The VaR is subsequently derived from the chosen percentile of this simulated distribution, reflecting the current market volatility regime.
Methodology
The methodology applies a two-step process: first, parameterizing a volatility model using recent market data, such as crypto price fluctuations, to filter historical returns. Second, constructing a conditional distribution of returns by sampling from the filtered historical residuals, effectively re-weighting past observations to reflect current market dynamics. This method provides a more responsive and accurate risk measure than basic historical simulation, aiding in dynamic risk management for crypto positions.
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