This discipline applies mathematical and statistical methods to assess the potential financial impact of various market scenarios on derivative positions. It moves beyond simple P&L to model the distribution of possible future values for a portfolio. Such rigorous examination is essential for setting appropriate capital reserves.
Distribution
Modeling the probability distribution of the underlying asset’s returns, often incorporating non-normal characteristics like skew and kurtosis observed in crypto markets, refines valuation accuracy. This allows for the calculation of Value at Risk under specific confidence intervals. Understanding the shape of this distribution informs tail risk management.
Backtest
The process involves simulating a trading strategy’s performance using historical market data to validate the efficacy of the risk models employed. A successful backtest provides empirical evidence of the model’s predictive power under various market regimes. Rigorous testing minimizes reliance on theoretical assumptions alone.
