Value-at-Risk (VaR) Models are statistical techniques used to quantify the potential financial loss of an investment portfolio or position over a specified time horizon, at a given confidence level. In crypto investing, VaR models estimate the maximum expected loss in the value of digital assets or derivatives under normal market conditions, providing a critical metric for risk assessment and capital allocation.
Mechanism
The operational mechanism of VaR models involves processing historical price data, volatility metrics, and asset correlations to generate a statistical distribution of potential future portfolio values. Common approaches include historical simulation, parametric methods (e.g., variance-covariance), and Monte Carlo simulation. These calculations produce a single value representing the loss threshold that will not be exceeded with a certain probability (e.g., 95% or 99%) over a defined period.
Methodology
The strategic application of VaR models enables institutional crypto investors to understand and manage market risk exposure, set risk limits, and comply with internal or external regulatory guidelines. While providing a concise risk summary, it is important to acknowledge VaR’s limitations, such as its inability to capture extreme “tail” events or account for liquidity risk in volatile crypto markets. Therefore, VaR is often complemented by stress testing and expected shortfall metrics.
Initial Margin is a pre-funded guarantee against potential future default; Variation Margin is the daily settlement of realized market gains and losses.
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