Market Risk

Concordia's risk engine responds quickly and accurately to market conditions. In a world where cryptocurrency prices fluctuate wildly from hour to hour, having a real-time model that can keep up with these changes is crucial for lenders looking to manage their risks effectively.

At a fundamental level, the model requires the user to deposit collateral that protects the portfolio from the risk of the fluctuations of the asset prices over a certain period of risk (e.g., 1 day) with a probability (e.g., 99%) by using the appropriate filtered historical simulation VAR (value-at-risk). Below, we break this model into several steps, highlighting exactly how the risk model calculates the real-time price risk that drives its liquidation mechanism.

Step 1 -> Calculate the historical returns:

A filtered historical simulation VAR (value-at-risk) formula achieves a dynamic model. The model looks at the unweighted historical returns over a long time window (i.e., 180 days). The volatility measure implied from those returns alone would be similar to conventional protocols, which cannot note or predict unconditional volatility (i.e., future volatility not influenced by historical risk variables).

Step 2 -> Calculate Dvols (devolatilised returns).

By removing the volatility from these historical returns, we arrive at devolatilised returns (Dvols). This is an essential step because Dvols provide the unconditional volatility (not influenced by real-time variables) data needed to re-apply real-time, conditional volatility.

Step 3 -> Revolatilising Returns (Rvol)

Revolatilising returns (Rvol) attempt to predict real-time or conditional volatility. We do this by choosing a shorter time window (e.g., one day) to calculate the most recent volatility and apply that measure to the Dvols. We then get Rvols, a dynamic conditional vol measure that can more accurately pick up real-time price risks in the market.

Protocols that use static historical volatility measures have inefficient Health Ratio ratios that liquidate positions too late, creating toxic liquidity (bad debt). Moreover, the protocol loses potential loan revenue when Health Ratio ratios are too high because they use static historical measures. In summary, the filtered historical simulation VAR model turns down Vol when markets are calm and turns up Vol as markets become more volatile, enabling a more capital-efficient real-time model.

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