Full Risk Analytics Chain AI Prompt

Step 1: Return data profiling — profile the return data quality. Check for missing dates, zero returns, extreme outliers, survivorship bias, and corporate action contamination. Compute basic statistics and confirm data is suitable for analysis.
Step 2: Distributional analysis — test for normality, measure skewness and excess kurtosis, estimate tail behavior using EVT (GPD fitting). Determine which risk models are appropriate given the distributional properties.
Step 3: VaR and CVaR — compute using all three methods (historical, parametric, Monte Carlo). Backtest VaR with Kupiec POF test and Christoffersen interval forecast test. Report which method is most appropriate.
Step 4: Drawdown analysis — compute maximum drawdown, average drawdown, recovery time, and the full distribution of drawdown episodes. Report Calmar ratio, Ulcer index, and current drawdown status.
Step 5: Factor decomposition — run factor model regression. Decompose total risk into systematic (factor) and idiosyncratic components. Identify the dominant factor exposures driving portfolio risk.
Step 6: Stress testing — apply at least 3 historical stress scenarios and 3 hypothetical scenarios. For each: P&L impact, VaR comparison, and which positions contribute most to stress loss.
Step 7: Risk report — write a 1-page risk summary: current risk level vs target, factor exposures of concern, tail risk assessment, liquidity profile, and top 3 risk management recommendations.