Correlation Analyzer

At least 30 data points for basic reliability; 60-90 days is common. For stable long-term relationships, use 252 days (1 year). Shorter periods are more responsive but noisier. Match the period to your intended use.

No. Correlation measures how two assets move together, not which direction they'll move. Two stocks with 0.9 correlation might both go up or both go down - you can't tell from correlation alone.

Prices are non-stationary (they trend). Correlation of prices is misleading. Returns are stationary and measure relative changes. Always calculate correlation on returns (daily % changes), not price levels.

Below 0.3 is good for diversification. Near zero is excellent. Negative correlation is ideal (positions hedge each other). Above 0.7 provides little diversification benefit - assets move too similarly.

Yes, especially over short periods. This is called spurious correlation. Always look for fundamental reasons behind high correlations. Two truly unrelated assets with high correlation will likely see that correlation break down.

During crises, fear dominates and investors sell indiscriminately. Liquidity becomes scarce, and everything trades on risk-on/risk-off sentiment rather than fundamentals. Panic selling affects all assets similarly, driving correlations toward 1.

Monitor rolling correlation continuously. Look for: declining rolling correlation, increased volatility of correlation, divergence from historical norms. Set alerts when rolling correlation drops below thresholds (e.g., drops from 0.8 to 0.6).

Covariance measures how two assets move together but depends on their scales. Correlation is normalized covariance - divided by both standard deviations - giving a standardized measure from -1 to +1. Correlation is easier to interpret.

Daily updates for rolling correlations. Full matrix recalculation weekly or monthly. More frequent during volatile periods. Balance between responsiveness (more frequent) and stability (less frequent).

Yes, but carefully. Option returns are non-linear, so Pearson correlation may miss relationships. Use correlation of underlying assets for option strategies. For complex option portfolios, correlation of P&L changes may be more useful.

Use Pearson for normal data with linear relationships. Use Spearman when: data has outliers, non-normal distribution, or you want to capture monotonic (not just linear) relationships. Spearman ranks values first, making it robust to extremes.

1) Fit univariate GARCH to each return series. 2) Obtain standardized residuals. 3) Fit DCC model to correlations of residuals. In Python, use 'arch' package. In R, use 'rmgarch'. This gives time-varying correlation estimates that update with new data.

Tail dependence measures correlation in extreme events (both tails). Assets may have low normal correlation but high tail dependence (crash together). Gaussian copula misses this; t-copula captures it. Critical for risk management - diversification may fail in tails.

Ledoit-Wolf shrinkage: Σshrunk = α×Σsample + (1-α)×Σtarget. Target can be identity, constant correlation, or factor model. Optimal shrinkage α minimizes expected loss. In Python: sklearn.covariance.ledoit_wolf. Reduces estimation error for portfolio optimization.

1) Calculate correlation matrix. 2) Threshold: keep correlations above cutoff (e.g., 0.5). 3) Create graph with assets as nodes, correlations as edges. 4) Calculate network metrics (centrality, clustering). Packages: NetworkX (Python), igraph (R). Visualize with Gephi or matplotlib.