analyze-move-risk-gauges-leadlag

To analyze MOVE lead/lag relationships with VIX and credit spreads, apply cross-correlation on aligned public market data to identify leadership signals and quantify lag relationships between these risk indicators.

The best way to measure rate-volatility panic during JGB yield shocks is to apply event-window testing on aligned data, using Z-score standardization to quantify whether MOVE reacts calmly or fearfully to the yield moves.

You run cross-correlation analysis on MOVE and VIX data by executing the main Python script with defined start and end dates, which fetches market data, aligns the series, and computes the lag relationships automatically.

Yes, you need yfinance and pandas to perform event-window evaluation on rate volatility, as the core analysis requires fetching public market data and aligning time series for cross-correlation and Z-score standardization.

Yes, you can generate Bloomberg-style charts for macro-rate environments by running the Python script with the chart flag, which visualizes Z-score standardized data and rates-MOVE panic reactions during event windows.