Theoretical basis
Last updated
Last updated
UFD Protocol Mechanism Risk Model Considerations
Complex Model Considerations
Risks in the financial industry generally come from market risk, credit risk, and operational risk. These risks are also increasingly appearing in the ever-growing financial ecosystem on the chain. The bridging of risks often indicates the continuous spiral integration of the two worlds of finance. The UFD protocol is the first to boldly try many classic probability theories, operational research theories, and modern financial risk management models in the decentralized financial system on the chain, which helps to test the robustness of the entire system, timely control the key global data of the system, and Assist in the formulation of rate policies, and move towards a professional and modern financial risk management mechanism on the chain.
Extreme Risk Model Considerations
VaR is an accurate, intuitive and easy-to-operate risk measurement and management technology, which can more effectively predict the maximum value fluctuation and probability of the assets that provide liquidity in the future, but it needs to follow market efficiency assumptions and assumptions Market fluctuations are random (under normal market conditions), but for the phenomenon of peaks and thick tails, fluctuations are clustered.
The prediction of (volatility clustering) and extreme risk situations (in fact, these situations will be more frequent) needs to be coordinated with other models or higher-order stochastic simulation methods (such as GARCH family models and Monte Carlo simulation methods) to further avoid underestimation The possibility of small probability events and more accurate assessment of loss probability. As shown in the figure, Cauchy Distribution (Cauchy Distribution) is a well-known type of fat-tailed distribution, which is suitable for the prediction of small probability events such as financial crises, which is different from normal distributed.
The CVaR model [20] is often used to monitor "black swan" events (extreme events on the left), pointing out that losses exceed the conditional mean value of VaR, and are more sensitive to extreme risk assessments on the asset side.
