자산 상관관계 매트릭스
ETF와 자산 클래스가 어떻게 함께 움직이는지 시각화하세요.
이 도구가 마음에 드시나요? portfolios.tools를 영원히 무료로 유지하는 데 도움을 주세요.
작동 방식
Select ETF tickers from the bundled catalog spanning US broad market, sector, international, bond, gold, and REIT proxies. The tool computes pairwise Pearson correlation coefficients across ten years of synthetic monthly return data stored locally in your browser. Start with your current core holdings plus one candidate fund to see whether diversification score improves before adding position size. A classic three fund portfolio might include US total market, international equities, and aggregate bonds before adding satellite sleeves like listed real estate or commodities. Start with core holdings plus one candidate ETF before sizing new sleeve: diversification score should rise before adding dollars. Add international sleeve VGK or VWO to US core and watch diversification score versus all US basket. Start with core holdings plus one candidate ETF before sizing new sleeve: diversification score should rise before adding dollars. Add international sleeve VGK or VWO to US core and watch diversification score versus all US basket.
Review the full symmetric correlation matrix, the three highest and three lowest correlated pairs, and the overall diversification score. A high score suggests better shock absorption when equities sell off, though correlations often spike toward one during global crises. Re run the selection after swapping candidate tickers to confirm the new sleeve lowers average pairwise correlation rather than adding redundant exposure to the same market factor. Flag any pair above 0.85 as a sign you may own two funds tracking nearly identical risk. Export ranked pairs into your investment policy statement when documenting diversification rationale for advisory clients. Flag pairs above 0.85 correlation as redundant exposure to same factor even when ticker names differ. Remove redundant SPY and VOO pair when both track US large cap: keeps matrix readable. Ten year synthetic monthly data approximates relationships: confirm with live quotes before trades. Flag pairs above 0.85 correlation as redundant exposure to same factor even when ticker names differ. Remove redundant SPY and VOO pair when both track US large cap: keeps matrix readable. Ten year synthetic monthly data approximates relationships: confirm with live quotes before trades.
rationale for advisory clients. Flag pairs above 0.85 correlation as redundant exposure to same factor even when ticker names differ. Remove redundant SPY and VOO pair when both track US large cap: keeps matrix readable. Ten year synthetic monthly data approximates relationships: confirm with live quotes before trades. Flag pairs above 0.85 correlation as redundant exposure to same factor even when ticker names differ. Remove redundant SPY and VOO pair when both track US large cap: keeps matrix readable. Ten year synthetic monthly data approximates relationships: confirm with live quotes before trades.
입력값이 변경될 때마다 자산 상관관계 매트릭스을(를) 사용하세요: 시장 변동, 새로운 기여금 또는 수정된 개인 가정 후. 소프트웨어 설치 없이 빠른 재실행을 위해 페이지를 북마크하세요.
단계별 안내
- 자산 상관관계 매트릭스을(를) 열고 현재 입력값을 입력하세요.
- 계산된 출력과 요약 테이블을 검토하세요.
- 가정을 조정하고 시나리오를 나란히 비교하세요.
실전 예제
Select ETF tickers from the bundled catalog spanning US broad market, sector, international, bond, gold, and REIT proxies. Enter the sample inputs described in How it works to reproduce the scenario step by step.
한 번에 하나의 입력을 조정하여 민감도를 확인하세요. 자산 상관관계 매트릭스은(는) 즉시 업데이트되므로 행동하기 전에 낙관적 및 보수적 가정을 스트레스 테스트할 수 있습니다.
이 계산기를 사용할 때
Reach for Asset Correlation Matrix when visualize how etfs and asset classes move together.. It suits quick what if analysis before trades, allocation changes, or plan updates.
결정이 세금, 유동성 또는 하나의 공식이 포착하는 것 이상의 다년 전망을 포괄할 때 관련 도구와 함께 사용하세요.
흔한 실수
입력 단위나 오래된 시장 가격을 확인하지 않고 출력을 복사하는 것은 자산 상관관계 매트릭스에서 흔한 오류입니다. 행동하기 전에 티커, 백분율 및 날짜를 확인하세요.
단일 기준 시나리오만 실행하면 꼬리 위험을 무시합니다. 보수적 입력으로 스트레스 테스트하고 결정이 중요할 때 아래 나열된 관련 도구와 비교하세요.
공식
Pearson ρ = Σ(x_i - x̄)(y_i - ȳ) / √(Σ(x_i - x̄)² × Σ(y_i - ȳ)²). Mean and std dev computed on 120 monthly return rows per ETF. Matrix is n×n symmetric with diagonal = 1.
Twenty ETF catalog with synthetic monthly returns over 120 months. Diversification score equals one minus average absolute correlation. Synthetic returns approximate historical relationships but differ from live market data. Refresh selections when your strategic allocation policy changes materially each year before rebalancing holdings. Rolling three year live correlations often exceed long run averages during prolonged bull markets when diversification benefits feel stronger than crisis data suggests. Synthetic monthly returns approximate history: confirm with live data before trading on correlation insights alone. Pearson correlation on monthly returns misses tail dependence visible only in daily crisis data. Bond equity correlation was positive in twenty twenty two stress: historical synthetic data may understate tail co movement. Crisis months push correlations toward one: synthetic data may understate tail dependence. Refresh ETF selections when strategic allocation policy changes each year. Use live market data confirmation before trades. Synthetic monthly returns approximate history: confirm with live data before trading on correlation insights alone. Pearson correlation on monthly returns misses tail dependence visible only in daily crisis data. Bond equity correlation was positive in twenty twenty two stress: historical synthetic data may understate tail co movement. Crisis months push correlations toward one: synthetic data may understate tail dependence.
제한 사항 및 가정
Twenty ETF catalog with synthetic monthly returns over 120 months. Diversification score equals one minus average absolute correlation. Synthetic returns approximate historical relationships but differ from live market data. Refresh selections when your strategic allocation policy changes materially each year before rebalancing holdings. Rolling three year live correlations often exceed long run averages during prolonged bull markets when diversification benefits feel stronger than crisis data suggests. Synthetic monthly returns approximate history: confirm with live data before trading on correlation insights alone. Pearson correlation on monthly returns misses tail dependence visible only in daily crisis data. Bond equity correlation was positive in twenty twenty two stress: historical synthetic data may understate tail co movement. Crisis months push correlations toward one: synthetic data may understate tail dependence. Refresh ETF selections when strategic allocation policy changes each year. Use live market data confirmation before trades. Synthetic monthly returns approximate history: confirm with live data before trading on correlation insights alone. Pearson correlation on monthly returns misses tail dependence visible only in daily crisis data. Bond equity correlation was positive in twenty twenty two stress: historical synthetic data may understate tail co movement. Crisis months push correlations toward one: synthetic data may understate tail dependence. Asset Correlation Matrix does not replace personalized advice. Fees, slippage, account specific rules, and behavioral constraints may change real world outcomes.
주요 용어
- How is correlation calculated
- Pearson correlation coefficient rho equals covariance divided by the product of standard deviations.
- What data does the correlation matrix use
- The tool bundles twenty ETF proxies including SPY, VTI, QQQ, IWM, VGK, VWO, BND, TLT, GLD, and VNQ with ten years of seeded synthetic monthly returns across categories.
- 모델 가정
- Diversification Score equals one minus the average absolute correlation across selected pairs.
대안 비교
Size allocation weights with Risk Parity Allocator, measure overall portfolio heat with Portfolio Temperature, and backtest multi asset blends with Lost Decade Backtester on portfolios. Use those calculators when asset correlation matrix alone does not capture the full decision.
portfolios.tools의 내부 링크는 계산기 체인을 도와줍니다: 먼저 자산 상관관계 매트릭스을(를) 실행한 다음, 아래 관련 섹션의 전문 도구로 엣지 케이스를 검증하세요.
FAQ
How is correlation calculated?
Pearson correlation coefficient rho equals covariance divided by the product of standard deviations. Values range from negative one for perfect inverse movement to positive one for lockstep behavior. Zero indicates no linear relationship over the sample period. Pearson rho on monthly returns captures co movement but not causation: two equity funds may correlate because both respond to the same interest rate and growth shocks rather than because one drives the other. Pearson correlation on monthly returns over ten year synthetic window estimates co movement not causation. Diversification score equals one minus average absolute pairwise correlation across selected tickers. Pearson correlation on monthly returns over ten year synthetic window estimates co movement not causation. Diversification score equals one minus average absolute pairwise correlation across selected tickers.
What data does the correlation matrix use?
The tool bundles twenty ETF proxies including SPY, VTI, QQQ, IWM, VGK, VWO, BND, TLT, GLD, and VNQ with ten years of seeded synthetic monthly returns across categories. Data is educational: confirm relationships with live data before trading decisions. Synthetic series smooth extremes that appear in live crisis months when correlations often converge toward one regardless of prior diversification benefits. Twenty ETF catalog spans bonds gold REITs and sectors for educational correlation exploration. Twenty ETF catalog spans bonds gold REITs and sectors for educational correlation exploration.
What is the diversification score?
Diversification Score equals one minus the average absolute correlation across selected pairs. Higher scores mean portfolio components move more independently. Near zero means assets largely move together. Mixing stocks, bonds, and gold typically raises diversification score versus an all equity basket. Scores above 0.4 on this scale often indicate meaningful sleeve differentiation worth maintaining through periodic rebalances. Crisis months push correlations toward one: synthetic ten year data smooths extremes that live portfolios suffer. Crisis months push correlations toward one: synthetic ten year data smooths extremes that live portfolios suffer.
Which pairings tend to have high vs low correlation?
SPY and VTI typically show correlation above 0.9 because both track US large cap equity. SPY and aggregate bonds often show low or negative correlation useful for classic stock bond balance. Sector ETFs like XLE and XLK may correlate less with broad market during regime shifts. International equity sleeves sometimes add less diversification than expected when global markets sync during risk off episodes.
How many assets can I compare?
Select two to twenty ETF tickers from the catalog. Results show the full symmetrical matrix, three highest pairs, three lowest pairs, and diversification score. Use lowest correlation pairs to prioritize new sleeves that reduce portfolio level co movement. Remove redundant tickers that duplicate existing correlation exposure before adding new fund lines. Compare diversification score before and after each candidate addition to quantify marginal benefit. Institutional allocators often cap any single pairwise correlation above 0.75 when approving new fund mandates. Remove lowest correlation pair candidates first when building satellite sleeve around US total market core. Diversification score above zero point four suggests meaningful sleeve independence worth maintaining in rebalance policy. Revisit correlation selections after major Fed policy shifts because bond equity relationships regime shift over decades. Remove lowest correlation pair candidates first when building satellite sleeve around US total market core. Diversification score above zero point four suggests meaningful sleeve independence worth maintaining in rebalance policy.
휴대폰이나 태블릿에서 자산 상관관계 매트릭스을(를) 사용할 수 있나요?
예. 이 도구는 데스크톱과 동일한 공식으로 모바일 브라우저에서 완전히 실행됩니다. 선택적 localStorage는 브라우저 설정에서 활성화된 경우 기기에 입력값을 기억할 수 있습니다.
자산 상관관계 매트릭스을(를) 사용할 때 내 데이터는 어디에 저장되나요?
당사 서버 어디에도 없습니다. 계산은 브라우저에서 로컬로 실행됩니다. 선택적 localStorage는 기기에서만 양식 필드를 저장하며 네트워크를 통해 포트폴리오 번호를 전송하지 않습니다.
세금 또는 법적 결정에 자산 상관관계 매트릭스에 의존해야 하나요?
아니요. 이 도구는 교육용 수학만 제공합니다. 세법, 계좌 규칙 및 개인 상황은 다양합니다. 중대한 결과를 초래하는 거래 전에 자격을 갖춘 세무 또는 법률 전문가와 상담하세요.
관련 도구
Size allocation weights with Risk Parity Allocator, measure overall portfolio heat with Portfolio Temperature, and backtest multi asset blends with Lost Decade Backtester on portfolios.tools when building diversified ETF portfolios from correlation insights. Risk Parity Allocator sizes weights after correlation matrix confirms sleeves diversify rather than duplicate. Risk Parity Allocator sizes weights after correlation matrix confirms sleeves diversify rather than duplicate.