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Financial Market Correlations

Introduction​

Financial markets operate as an interconnected web of relationships, where price movements in one instrument often influence or reflect changes in others. Understanding these correlations is crucial for effective trading, risk management, and portfolio optimization. This comprehensive guide explores the complex relationships between currencies, futures, commodities, and other financial instruments.

Understanding Market Correlations​

Correlation Basics​

Correlation measures the statistical relationship between two financial instruments, expressed as a coefficient ranging from -1 to +1:

  • Perfect Positive Correlation (+1.0): Assets move identically in the same direction and magnitude
  • Strong Positive Correlation (+0.7 to +0.9): Assets generally move in the same direction with high consistency
  • Moderate Positive Correlation (+0.4 to +0.6): Assets show some tendency to move together
  • Weak Correlation (-0.3 to +0.3): Little to no consistent relationship
  • Moderate Negative Correlation (-0.4 to -0.6): Assets tend to move in opposite directions
  • Strong Negative Correlation (-0.7 to -0.9): Assets consistently move in opposite directions
  • Perfect Negative Correlation (-1.0): Assets move exactly opposite to each other

Calculation Methods​

Correlations are typically calculated using:

  • Pearson Correlation Coefficient: Measures linear relationships between price changes
  • Rolling Windows: Common timeframes include 20-day, 60-day, or 252-day (annual) periods
  • Returns vs Prices: Correlations are calculated on percentage returns rather than absolute prices

Key Principles​

  1. Dynamic Nature: Correlations change over time and shift with market conditions. A historically strong correlation can weaken or reverse during market regime changes.

  2. Strength Variation: Correlation strength fluctuates even when direction remains consistent. Markets may maintain positive correlation but vary from +0.5 to +0.9.

  3. Context Dependence: Economic conditions, central bank policy, geopolitical events, and market structure all influence correlation patterns.

  4. Correlation ≠ Causation: High correlation doesn't imply one instrument causes movement in another; both may be responding to common underlying factors.

  5. Non-Linearity: Some relationships are non-linear and may not be fully captured by standard correlation measures.

Currency Pair Correlations​

Base Currency Effects​

Currency pairs sharing the same base currency typically show strong correlations due to their common denominator. When the base currency strengthens or weakens, all pairs with that base move in the same direction relative to their quote currencies.

Example Relationships:

  • EUR/USD & EUR/GBP: Strong positive correlation (+0.7 to +0.9) - Both measure EUR strength
  • EUR/USD & GBP/USD: Strong positive correlation (+0.6 to +0.8) - Both are USD-quote pairs
  • EUR/USD & USD/CHF: Strong negative correlation (-0.7 to -0.9) - Inverse USD positioning
  • AUD/USD & NZD/USD: Very strong positive correlation (+0.8 to +0.95) - Similar economic drivers

Quote Currency Effects​

Pairs sharing the same quote currency move together when that quote currency experiences broad movements:

  • USD/JPY & EUR/JPY: Positive correlation when JPY is the dominant driver
  • GBP/USD & GBP/JPY: Positive correlation when GBP is the dominant driver

Economic Integration and Geographic Relationships​

Regional economic ties create natural correlation patterns:

Australasian Pairs (AUD, NZD):

  • Share major trading partners (China, Japan)
  • Similar commodity export profiles
  • Coordinated central bank policies
  • Typical correlation: +0.85 to +0.95

European Pairs (EUR, GBP, CHF):

  • Regional economic integration
  • Trade interdependencies
  • Correlated (though not identical) monetary policies
  • EUR/GBP typical correlation: +0.4 to +0.7

North American Pairs (USD, CAD):

  • Continental economic integration
  • Major bilateral trade relationship
  • Energy relationship (Canada as major US oil supplier)
  • USD/CAD inverse correlation with oil: -0.7 to -0.9

Risk Sentiment and Safe Haven Flows​

Market risk perception creates distinct correlation patterns that intensify during periods of stress:

Risk-Off Environments (Market Stress):

  • Safe Haven Currencies Strengthen: JPY, CHF, and often USD
  • Risk-Sensitive Currencies Weaken: AUD, NZD, and emerging market currencies
  • Correlations typically strengthen during risk-off periods

Risk-On Environments (Market Confidence):

  • Risk-Sensitive Currencies Strengthen: AUD, NZD gain as investors seek yield
  • Safe Haven Currencies Weaken: JPY, CHF decline relative to others
  • Correlations may weaken as individual factors dominate

USD Behavior varies based on the nature of market stress:

  • US-specific stress: USD may weaken as other safe havens gain
  • Global stress: USD typically strengthens as world reserve currency
  • European crisis: USD strengthens as alternative to EUR

Carry Trade Dynamics​

Carry trades create correlation patterns based on interest rate differentials:

  • High-yielding currencies (historically AUD, NZD, BRL) move together
  • Low-yielding funding currencies (JPY, CHF, EUR) move together
  • Carry unwind events cause sharp reversals and correlation spikes

Futures Market Correlations​

Equity Index Futures​

Major equity indices demonstrate strong positive correlations, particularly during trending markets:

US Equity Indices:

  • E-mini S&P 500 (ES) & E-mini Nasdaq 100 (NQ): Very strong positive (+0.90 to +0.95)
  • E-mini S&P 500 (ES) & E-mini Russell 2000 (RTY): Strong positive (+0.85 to +0.90)
  • E-mini Nasdaq 100 (NQ) & E-mini Russell 2000 (RTY): Strong positive (+0.80 to +0.90)

Key Observations:

  • Russell 2000 shows higher volatility (typically 1.2-1.5x ES volatility)
  • Correlation strength increases significantly during market stress
  • Tech-heavy NQ may diverge during sector rotation
  • Small-cap RTY often leads directional changes

International Indices:

  • ES & DAX: Strong positive (+0.75 to +0.85) during overlapping hours
  • ES & Nikkei 225: Moderate to strong positive (+0.60 to +0.80)
  • ES & FTSE 100: Strong positive (+0.70 to +0.85)
  • Correlations vary by time zone and local economic factors

Interest Rate Futures (US Treasuries)​

Treasury futures correlations reflect yield curve relationships:

Long-End Treasuries:

  • 10-Year Notes (ZN) & 30-Year Bonds (ZB): Very strong positive (+0.95 to +0.98)
  • Similar duration and interest rate sensitivity
  • Move in near-lockstep during rate moves

Curve Dynamics:

  • 2-Year Notes (ZT) & 10-Year Notes (ZN): Moderate to strong positive (+0.60 to +0.85)
  • Correlation weakens during curve steepening/flattening
  • Fed policy expectations drive short-end independently

Treasury-Equity Relationship:

  • ZN & ES: Typically positive (+0.3 to +0.6) in normal markets
  • Can turn negative during risk-off events (flight to quality)
  • Relationship depends on whether rates rise due to growth or inflation concerns

Energy Futures​

The energy complex shows strong internal correlations but varying relationships across products:

Crude Oil Complex:

  • WTI Crude (CL) & Brent Crude (BRN): Very strong positive (+0.95 to +0.98)
  • WTI Crude (CL) & RBOB Gasoline (RB): Strong positive (+0.80 to +0.90)
  • WTI Crude (CL) & Heating Oil (HO): Strong positive (+0.85 to +0.92)
  • RBOB Gasoline (RB) & Heating Oil (HO): Strong positive (+0.80 to +0.88)

Natural Gas Independence:

  • Natural Gas (NG) & Crude Oil (CL): Weak to moderate (+0.2 to +0.5)
  • Natural gas driven by unique factors (weather, storage, regional supply)
  • Can temporarily correlate during broad energy moves
  • US shale production creates some link but less than historically

External Factors Affecting Energy Correlations:

  • Geopolitical events strengthen correlations across complex
  • Refinery capacity affects gasoline-crude relationships
  • Seasonal demand patterns (heating oil in winter, gasoline in summer)
  • Supply disruptions create temporary decoupling

Metals Futures​

Precious Metals:

  • Gold (GC) & Silver (SI): Strong positive (+0.70 to +0.85)
  • Gold (GC) & Platinum (PL): Moderate positive (+0.50 to +0.70)
  • Silver shows higher volatility and industrial demand sensitivity
  • Gold-USD inverse relationship: (-0.6 to -0.8)

Industrial Metals:

  • Copper (HG) & Aluminum: Strong positive (+0.70 to +0.85)
  • Copper (HG) & S&P 500 (ES): Moderate to strong positive (+0.50 to +0.75)
  • Copper known as "Dr. Copper" for economic forecasting ability
  • Industrial metals correlate with global growth expectations

Agricultural Futures​

Agricultural commodities show more complex and variable correlations:

Grain Complex:

  • Corn (ZC) & Soybeans (ZS): Moderate to strong positive (+0.50 to +0.75)
  • Correlation due to crop rotation and land use competition
  • Weather events can create strong temporary correlations

Softs:

  • Coffee, Cocoa, Sugar: Typically low correlation unless driven by common factors (USD, weather)
  • More influenced by individual supply/demand dynamics

Commodity-Currency Relationships​

Understanding commodity-currency correlations is essential for global macro trading and provides natural hedging opportunities.

Australian Dollar (AUD)​

The AUD is heavily influenced by commodity prices, particularly industrial metals:

Correlations:

  • Copper: Strong positive (+0.70 to +0.85)
  • Iron Ore: Very strong positive (+0.75 to +0.90)
  • Gold: Moderate positive (+0.40 to +0.60)
  • China Economic Data: Strong positive relationship

Drivers:

  • Australia is major exporter of iron ore (world's largest), coal, and LNG
  • Chinese demand is crucial (China is Australia's largest trading partner)
  • Mining sector represents significant portion of GDP
  • Risk sentiment amplifies commodity relationship

Canadian Dollar (CAD)​

The CAD is often called the "loonie" and shows strong energy correlation:

Correlations:

  • WTI Crude Oil: Strong positive (+0.75 to +0.90)
  • USD/CAD: Strong negative with oil prices (-0.75 to -0.90)
  • Natural Gas: Moderate positive (+0.40 to +0.60)

Drivers:

  • Canada is major oil exporter to the United States
  • Oil industry represents significant portion of economy and exports
  • Bank of Canada policy influenced by oil price impacts
  • NAFTA/USMCA integration also creates strong US economic correlation

New Zealand Dollar (NZD)​

The NZD is heavily influenced by agricultural commodities and risk sentiment:

Correlations:

  • Dairy Prices: Strong positive (+0.60 to +0.80)
  • Agricultural Commodities: Moderate to strong positive (+0.50 to +0.70)
  • AUD: Very strong positive (+0.85 to +0.95)

Drivers:

  • New Zealand is world's largest dairy exporter
  • Agricultural products comprise major portion of exports
  • Similar economic structure and trading partners as Australia
  • Small, open economy sensitive to global risk sentiment

Norwegian Krone (NOK)​

Correlations:

  • Brent Crude Oil: Strong positive (+0.70 to +0.85)
  • Natural Gas: Moderate to strong positive (+0.50 to +0.70)

Drivers:

  • Norway is major European oil and gas producer
  • Sovereign wealth fund (largest in world) funded by oil revenues

Swiss Franc (CHF)​

Correlations:

  • Gold: Historically moderate positive (+0.30 to +0.60)
  • Risk-Off Assets: Strong positive during market stress
  • EUR: Strong positive (+0.70 to +0.90) due to economic ties

Drivers:

  • Traditional safe haven currency
  • Historically backed by gold reserves
  • Banking secrecy and stability reputation
  • Swiss National Bank interventions can disrupt correlations

Japanese Yen (JPY)​

Correlations:

  • Gold: Moderate positive (+0.30 to +0.60) during risk-off
  • Risk Assets: Negative correlation (safe haven flows)
  • VIX: Positive correlation during volatility spikes

Drivers:

  • Primary safe haven currency in Asia
  • Low interest rates make it funding currency for carry trades
  • Carry trade unwinds create sharp JPY appreciation
  • Bank of Japan ultra-loose policy affects relationships

US Dollar (USD)​

Correlations:

  • Gold: Strong negative (-0.60 to -0.80)
  • Commodities (broad): Negative correlation (commodities priced in USD)
  • DXY vs Risk Assets: Variable based on context

Drivers:

  • World reserve currency status
  • Commodities priced in dollars creates inverse relationship
  • Safe haven during global stress
  • Federal Reserve policy dominates direction

Cross-Asset Correlations​

Equity-Bond Relationships​

Normal Market Conditions:

  • Stocks & Bonds: Typically negative (-0.2 to -0.5)
  • Risk-off flows into bonds, risk-on into equities
  • "Stocks up, bonds down" traditional relationship

Regime Changes:

  • Inflation Concerns: Correlation can turn positive (both sell off)
  • Deflation/Crisis: Negative correlation strengthens (flight to quality)
  • Growth Concerns: Strong negative correlation (bonds rally, stocks fall)

Equity-Volatility Relationships​

VIX and S&P 500:

  • ES & VIX: Very strong negative (-0.80 to -0.95)
  • VIX measures implied volatility of S&P 500 options
  • Known as the "fear gauge"
  • Spikes sharply during market declines
  • Slowly grinds lower during rallies

Dollar Index Correlations​

DXY (US Dollar Index) Relationships:

  • Gold: Strong negative (-0.60 to -0.80)
  • Commodities: Generally negative (-0.40 to -0.70)
  • Emerging Markets: Negative (strong dollar pressures EM)
  • Foreign Equity Indices: Variable based on local factors

Trading Applications​

Divergence Trading​

Trading divergences between highly correlated instruments can be profitable but requires careful execution:

Implementation Steps:

  1. Identify Correlated Pairs: Focus on instruments with historical correlation greater than +0.75 or less than -0.75

  2. Monitor for Divergences:

    • Calculate z-score of the spread
    • Look for >2 standard deviation moves
    • Verify divergence isn't driven by fundamental change

  3. Trade Expected Convergence:

    • Enter when divergence is significant
    • Position for convergence (long underperformer, short outperformer)
    • Use ratio analysis or spread trading

  4. Risk Management:

    • Strict stop losses (divergence could signal regime change)
    • Position sizing based on spread volatility
    • Time stops (convergence trades can take time)
    • Monitor for fundamental drivers of divergence

Example:

  • If AUD/USD and NZD/USD typically move together (+0.90 correlation)
  • But AUD/USD drops 2% while NZD/USD only drops 0.5%
  • Investigate: Is there AUD-specific news? China data? Commodity moves?
  • If no fundamental reason: Consider long AUD/USD, short NZD/USD for convergence

Pairs Trading​

More sophisticated version of divergence trading:

Statistical Approach:

  • Calculate historical spread/ratio
  • Identify mean reversion levels
  • Enter when spread deviates significantly
  • Exit when spread returns to mean

Cointegration Analysis:

  • More robust than correlation for pairs trading
  • Tests whether spread is mean-reverting
  • Provides statistical framework for entries/exits

Portfolio Diversification​

Understanding correlations is crucial for proper portfolio construction:

Diversification Principles:

  1. Avoid Correlation Concentration:

    • Don't hold multiple highly correlated long positions
    • Being long EUR/USD, GBP/USD, and AUD/USD simultaneously provides false diversification
    • Calculate portfolio-level correlation exposure

  2. Balance Risk-On and Risk-Off Exposure:

    • Combine risk-sensitive assets (AUD, equities) with safe havens (JPY, treasuries)
    • Correlation shifts during market stress can hurt or help
    • Maintain awareness of tail risk

  3. Create Natural Hedges:

    • Long crude oil futures + long USD/CAD creates partial hedge
    • Long gold + long CHF provides correlated safe haven exposure
    • Use negative correlations to reduce portfolio volatility

  4. Regular Correlation Review:

    • Update correlation matrices monthly
    • Identify regime changes
    • Rebalance when correlations shift significantly

Hedging Strategies​

Cross-Hedging:

  • Hedge AUD exposure using copper futures
  • Hedge oil exposure using CAD currency positions
  • Hedge equity exposure using VIX futures or options

Proxy Hedging:

  • Use highly correlated but more liquid instrument
  • Example: Hedge Euro Stoxx 50 exposure with S&P 500
  • Monitor basis risk (hedges are imperfect)

Risk Management​

Correlation-Based Position Sizing:

  1. Calculate Correlated Exposure:

    • Aggregate positions with >+0.70 correlation
    • Treat as single larger position for risk purposes
    • Example: 2 lots ES + 4 lots NQ = higher effective exposure than simple addition

  2. Adjust Position Sizes:

    • Reduce individual position sizes when holding correlated instruments
    • Use correlation-adjusted risk limits
    • Formula: Effective Risk = Position1 + Position2 + (2 × Correlation × Position1 × Position2)^0.5

  3. Implement Correlation-Based Stops:

    • If holding correlated positions, one stop-out may signal exit for others
    • Use portfolio heat as aggregate stop
    • Monitor correlation breakdown (may signal regime change)

  4. Develop Hedging Strategies:

    • Use negative correlations to reduce portfolio risk
    • Dynamic hedging based on correlation strength
    • Cost-benefit analysis of hedge ratios

Advanced Correlation Concepts​

Time Variance and Regime Dependency​

Correlations are not static; they vary significantly across timeframes and market regimes:

Short-Term vs Long-Term:

  • Intraday correlations can differ from daily or weekly
  • High-frequency relationships may break down over longer periods
  • Match correlation timeframe to trading timeframe

Regime Changes:

  • Crisis vs normal markets show different correlations
  • Inflation vs deflation regimes alter relationships
  • Central bank policy shifts can restructure correlations
  • Identify regime by volatility, macroeconomic indicators, and market behavior

Correlation Stability Analysis:

  • Rolling correlation windows reveal stability
  • Stable correlations more reliable for trading
  • Unstable correlations may signal structural changes
  • Monitor standard deviation of correlation over time

Stress Behavior and Correlation Breakdown​

Market stress typically affects correlations in predictable ways:

Correlation Strengthening During Stress:

  • Risk-off correlations intensify (safe havens move together)
  • Equity indices show higher correlation during declines
  • "Correlation goes to 1" during crashes (everything sells except safe havens)
  • Flight-to-quality creates binary market behavior

Correlation Breakdown:

  • Idiosyncratic events can break typical correlations
  • Regulatory changes (Swiss franc de-pegging in 2015)
  • Monetary policy divergence (Fed tightening vs ECB easing)
  • Structural market changes (energy revolution affecting oil-CAD)

Recovery Analysis:

  • Monitor how correlations return to normal after stress
  • Prolonged correlation changes may signal new regime
  • Recovery speed varies by asset class

Multi-Factor Models​

Correlations are driven by underlying common factors:

Interest Rate Effects:

  • Central bank policy affects multiple asset classes
  • Rate expectations drive currency and bond correlations
  • Real rates (nominal - inflation) affect gold and TIPS

Economic Growth Expectations:

  • Risk assets correlate positively with growth expectations
  • Industrial commodities and cyclical currencies respond to growth
  • Defensive assets (bonds, JPY) inversely correlated

Inflation Expectations:

  • Commodities and inflation-linked bonds
  • Currency real exchange rates
  • Central bank policy responses

Political and Geopolitical Events:

  • Trade wars affect multiple correlated instruments
  • Elections create uncertainty that affects risk assets
  • Regional conflicts impact energy, currencies, and safe havens

Market Microstructure:

  • Algorithmic trading can create artificial correlations
  • ETF flows drive baskets of correlated assets
  • Systematic strategies (risk parity, CTA) affect correlations

Non-Linear Relationships​

Some important relationships are non-linear:

Volatility-Based:

  • Correlations change based on volatility levels
  • Tail correlations (extreme moves) differ from normal
  • Skewness and kurtosis affect relationship structure

Threshold Effects:

  • Correlations activate only above/below certain levels
  • Oil-CAD correlation stronger above certain oil prices
  • Equity-bond correlation flips based on inflation level

Practical Implementation​

Analysis Framework​

1. Weekly Correlation Review:

  • Calculate 20-day, 60-day rolling correlations for key pairs
  • Compare current values to historical averages
  • Create correlation matrices for portfolio instruments
  • Use tools: Excel, Python (pandas), R, or specialized platforms

2. Documentation of Changes:

  • Maintain correlation journal
  • Note significant shifts (>0.20 change)
  • Record potential drivers of changes
  • Review patterns over time

3. Pattern Identification:

  • Seasonal patterns (agricultural correlations, holiday effects)
  • Event-driven changes (central bank meetings, economic releases)
  • Structural shifts (policy changes, market structure evolution)

4. Adjustment Triggers:

  • Define thresholds for position adjustments
  • Example: Reduce position if portfolio correlation >0.75
  • Rebalance triggers based on correlation shifts
  • Hedge activation thresholds

Position Management​

1. Correlation-Based Sizing:

Calculate effective exposure considering correlations:

If holding Position A and Position B with correlation ρ:
Effective Exposure = √(A² + B² + 2ρAB)

Example:
- Long 10 contracts ES (A = 10)
- Long 5 contracts NQ (B = 5)
- Correlation = 0.90
Effective = √(100 + 25 + 2×0.90×10×5) = √(125 + 90) = √215 ≈ 14.7 ES equivalents

2. Exposure Limits:

  • Set maximum correlated exposure limits
  • Example: No more than 15% portfolio risk in >+0.70 correlated positions
  • Use correlation-adjusted risk rather than nominal position size
  • Dynamic limits based on market volatility

3. Rebalancing Rules:

  • Quarterly correlation review and portfolio rebalance
  • Rebalance when correlations shift by >0.20
  • Reduce exposure when portfolio correlation exceeds targets
  • Opportunistic rebalancing during correlation extremes

4. Exit Strategies:

  • Correlated stop losses (one position stopped may trigger review of others)
  • Portfolio heat limits (aggregate risk across correlated positions)
  • Time-based exits for divergence trades
  • Profit targets based on historical spread levels

Practical Tools and Resources​

Calculation Tools​

Python Example (using pandas):

import pandas as pd
import yfinance as yf

# Download data
data = yf.download(['SPY', 'QQQ'], start='2024-01-01')['Adj Close']

# Calculate returns
returns = data.pct_change()

# Calculate correlation
correlation = returns.corr()

# Rolling correlation
rolling_corr = returns['SPY'].rolling(window=20).corr(returns['QQQ'])

Excel:

  • Use CORREL() function for simple correlations
  • Create correlation matrix with Data Analysis Toolpak
  • Chart rolling correlations for visualization

Market Data Sources​

  • Bloomberg Terminal (professional)
  • Thomson Reuters Eikon (professional)
  • TradingView (retail-friendly, has correlation tools)
  • Quandl/Nasdaq Data Link (API access)
  • FRED Economic Data (free historical data)

Monitoring Best Practices​

  1. Set up alerts for significant correlation changes
  2. Create dashboards with key correlation pairs
  3. Automate calculations where possible
  4. Regular reviews even if no trades planned
  5. Document reasoning for correlation-based decisions

Common Pitfalls and Limitations​

Pitfall 1: Correlation ≠ Causation​

  • High correlation doesn't mean one instrument drives another
  • Both may respond to common underlying factor
  • Don't assume causal relationship without fundamental analysis

Pitfall 2: Looking at Wrong Timeframe​

  • Day trader using monthly correlations will be misled
  • Match correlation calculation period to trading timeframe
  • Very short-term correlations can be noise

Pitfall 3: Ignoring Regime Changes​

  • Historical correlation may not apply in new regime
  • Crisis periods show different correlations than calm markets
  • Central bank policy shifts can restructure relationships

Pitfall 4: Assuming Stationarity​

  • Correlations change over time
  • Past correlation doesn't guarantee future correlation
  • Regular updates essential

Pitfall 5: Overfitting​

  • Finding correlations that are spurious or coincidental
  • Small sample sizes can produce misleading correlations
  • Need sufficient data for robust estimates

Pitfall 6: Ignoring Transaction Costs​

  • Divergence trading requires frequent rebalancing
  • Spreads and commissions can eliminate edge
  • Account for all costs in strategy evaluation

Limitations of Correlation Analysis​

  1. Linear Measure Only: Doesn't capture non-linear relationships
  2. Assumes Normality: Extreme events may not follow normal distribution
  3. Backward Looking: Calculated from historical data
  4. Doesn't Predict Timing: Tells relationship but not when it will manifest
  5. Ignores Asymmetry: Different behavior in up vs down markets not captured

Quick Reference Sheet​

Common Currency Correlations​

Pair 1Pair 2Typical RangeNotes
EUR/USDGBP/USD+0.60 to +0.80Shared USD quote
EUR/USDUSD/CHF-0.70 to -0.90Inverse USD
AUD/USDNZD/USD+0.85 to +0.95Similar drivers
USD/CADWTI Oil-0.75 to -0.90Canada oil exporter
AUD/USDCopper+0.70 to +0.85Australia commodity exporter
USD/JPYS&P 500+0.40 to +0.70Risk sentiment (variable)
GoldUSD-0.60 to -0.80Inverse relationship

Futures Correlations​

Future 1Future 2Typical RangeNotes
ESNQ+0.90 to +0.95Both US equity indices
ESRTY+0.85 to +0.90Large vs small cap
ZNZB+0.95 to +0.98Both long-term rates
CLHO+0.85 to +0.92Energy complex
CLRB+0.80 to +0.90Energy complex
GCSI+0.70 to +0.85Precious metals
GCUSD-0.60 to -0.80Gold-dollar inverse
ESVIX-0.80 to -0.95Equities-volatility
ZNES+0.30 to +0.60Variable by regime

Commodity-Currency Correlations​

CurrencyCommodityTypical RangeKey Driver
AUDCopper+0.70 to +0.85Mining exports
AUDIron Ore+0.75 to +0.90Major exporter
CADWTI Oil+0.75 to +0.90Energy exports
NZDDairy+0.60 to +0.80Agricultural exports
NOKBrent Oil+0.70 to +0.85Norway oil producer
CHFGold+0.30 to +0.60Safe haven

Risk Management Checklist​

1. Regular Correlation Review

  • Calculate weekly rolling correlations (20-day, 60-day)
  • Document significant changes (>0.20 shift)
  • Monitor during stress periods for regime change
  • Update correlation matrices for portfolio

2. Position Sizing Rules

  • Calculate correlation-adjusted exposure
  • Reduce size when holding highly correlated positions (>+0.70)
  • Implement portfolio-level correlation limits
  • Review aggregate exposure weekly

3. Risk Control Measures

  • Set correlation-based stops and alerts
  • Develop hedging strategies for correlated exposure
  • Monitor diversification targets
  • Track portfolio correlation heat map

4. Market Regime Monitoring

  • Identify current volatility regime (VIX levels)
  • Assess macro environment (growth/inflation/policy)
  • Note correlation stability vs instability
  • Adjust strategy for regime changes

5. Trading Implementation

  • Match correlation timeframe to trading timeframe
  • Account for transaction costs in correlation trades
  • Set clear entry/exit rules for divergence trades
  • Document all correlation-based trade reasoning

Key Takeaways​

  1. Correlations are Dynamic: Never assume historical relationships will persist indefinitely. Regular monitoring is essential.
  2. Context Matters: Understand why instruments correlate, not just that they do. Fundamental drivers determine robustness.
  3. Regime Awareness: Crisis correlations differ dramatically from normal markets. Adjust expectations and strategies accordingly.
  4. Portfolio Perspective: Evaluate correlations at portfolio level, not just individual pairs. Aggregate exposure matters.
  5. Risk Management First: Use correlations primarily for risk management, secondarily for opportunity. False diversification is a major risk.
  6. Timeframe Alignment: Match correlation calculation periods to your trading timeframe for relevant analysis.
  7. Limitations Exist: Correlation is one tool among many. Combine with fundamental analysis, technical analysis, and risk management.

Remember: Correlations are powerful tools for understanding market relationships, but they are descriptive rather than predictive. Use them to inform decisions, not to replace judgment. Markets can and do change, and today's strong correlation may be tomorrow's broken relationship.