Risk Dashboard

30 March 2026  |  365 securities  |  189 longs, 176 shorts  |  261 weeks history

4.00%
Network Density
ELEVATED
30w change: -0.46pp
30
Clusters Identified
Largest: LATAM Utilities / Core (62 names)
Equity: 16.0%
Dominant Factor
% of total portfolio variance explained
1. Network Density
How to read this chart

Network density = % of all security pairs with |correlation| > 0.6 over a rolling 26-week window. When density rises, more securities move together -- diversification fails and systemic risk increases.

Regimes: GREEN (LOW <2.5%) = good diversification. YELLOW (CAUTIOUS 2.5-4%) = correlations clustering. ORANGE (ELEVATED 4-5.5%) = hedges may fail. RED (CRISIS >5.5%) = extreme herding.

Current: 4.00% -- ELEVATED regime. 30w change: -0.46pp.
2. Correlations
Rolling Correlations -- Overall Book

26-week rolling average correlations: Long-Long (LL), Short-Short (SS), Long-Short Net (LS). Rising LL = longs moving together (diversification loss). Rising LS Net = hedges becoming less effective.

Intra-Region Statistics
How to read

Summary of pairwise correlations within each geographic region. High Mean Corr = stocks in that region move together (concentration risk). % Pairs > 0.6 is the density metric per region.

RegionNMean CorrMedian Corr% Pairs > 0.6
LATAM1580.35150.331610.6023
EMEA1770.16380.14581.4902
Cross Over300.30870.27589.8851
Cross-Region Correlation Matrix
How to read

Average pairwise correlation between securities in different regions. Diagonal = intra-region. Off-diagonal values approaching the diagonal indicate geographic diversification is breaking down.

Intra-Sector Statistics
How to read

Pairwise correlation stats within each sector. High Mean Corr = concentrated sector risk. Sectors with high % Pairs > 0.6 need extra attention -- hedges within that sector may not work.

SectorNMean CorrMedian Corr% Pairs > 0.6
Communication Services220.17530.16581.2987
Consumer Discretionary400.20400.18823.8462
Consumer Staples420.17040.15850.5807
Energy210.23770.21512.8571
Financials760.24560.21865.4035
Health Care100.13830.09672.2222
Hedges70.46900.491419.0476
Industrials380.21190.18903.8407
Information Technology110.20790.19081.8182
Materials570.31300.31459.0852
Real Estate200.22100.19855.2632
Utilities210.38680.346931.4286
Cross-Sector Correlation Matrix
How to read

Average correlation between sectors. Identifies hidden sector bets -- if two sectors are highly correlated, holding both does not give the diversification you expect.

Top Correlated Pairs by Region
How to read

Highest pairwise correlations within each region (structural, ~2.5y window). High correlation between two positions = they move together. Use to identify concentration risk and hedge candidates.

Name AName BCorr
RENT3 BZ EquityRENT4 BZ Equity0.9907
AXIA3 BZ EquityAXIA7 BZ Equity0.9849
IRS US EquityPICS US Equity0.9795
PETR4 BZ EquityPICS US Equity0.9746
BAP US EquityPICS US Equity0.9616
CAAP US EquityRENT4 BZ Equity0.9555
CEPU US EquityPICS US Equity0.9454
PICS US EquityVALE3 BZ Equity0.9282
GGBR4 BZ EquityPICS US Equity0.9271
PICS US EquityXP US Equity0.9196
IGTI11 BZ EquityMULT3 BZ Equity0.9183
PICS US EquityPOMO4 BZ Equity0.9107
EQTL3 BZ EquityRENT4 BZ Equity0.9010
PICS US EquityYDUQ3 BZ Equity0.8979
CEPU US EquityPAM US Equity0.8950
Name AName BCorr
IMP SJ EquityNPH SJ Equity0.8941
PEO PW EquitySPL PW Equity0.8551
AKBNK TI EquityYKBNK TI Equity0.8523
ANG SJ EquityHAR SJ Equity0.7867
ABG SJ EquityFSR SJ Equity0.7664
TFG SJ EquityTRU SJ Equity0.7561
CPI SJ EquityFSR SJ Equity0.7493
PEO PW EquityPZU PW Equity0.7447
BVT SJ EquityTRU SJ Equity0.7415
EDV LN EquityHAR SJ Equity0.7406
ANG SJ EquityEDV LN Equity0.7373
PPH SJ EquityTRU SJ Equity0.7339
PPH SJ EquityTFG SJ Equity0.7317
FSR SJ EquityNED SJ Equity0.7316
ALPHA GA EquityEUROB GA Equity0.7272
Name AName BCorr
CS CN EquityTECK US Equity0.7749
AAL LN EquityTECK US Equity0.7268
GLEN LN EquityTECK US Equity0.7167
FCX US EquityGLEN LN Equity0.7086
FCX US EquityTECK US Equity0.6848
CS CN EquityFCX US Equity0.6841
B US EquityGLD US Equity0.6722
AAL LN EquityGLEN LN Equity0.6687
CS CN EquityGLEN LN Equity0.6643
AAL LN EquityCS CN Equity0.6420
AAL LN EquityFCX US Equity0.6020
CS CN EquityFM CN Equity0.5868
FCX US EquityFM CN Equity0.5848
FM CN EquityTECK US Equity0.5705
FM CN EquityGLEN LN Equity0.5423
Name AName BCorr
B US EquityPICS US Equity0.9964
FCX US EquityPICS US Equity0.9964
RENT3 BZ EquityRENT4 BZ Equity0.9907
AXIA3 BZ EquityAXIA7 BZ Equity0.9849
AAL LN EquityPICS US Equity0.9806
IRS US EquityPICS US Equity0.9795
FM CN EquityPICS US Equity0.9772
KGH PW EquityPICS US Equity0.9758
PETR4 BZ EquityPICS US Equity0.9746
EDV LN EquityPICS US Equity0.9695
BAP US EquityPICS US Equity0.9616
CAAP US EquityRENT4 BZ Equity0.9555
CEPU US EquityPICS US Equity0.9454
NPN SJ EquityPICS US Equity0.9434
GLEN LN EquityPICS US Equity0.9361
PICS US EquityTRU SJ Equity0.9323
PICS US EquityVALE3 BZ Equity0.9282
GGBR4 BZ EquityPICS US Equity0.9271
PICS US EquityXP US Equity0.9196
IGTI11 BZ EquityMULT3 BZ Equity0.9183
Name AName BCorr
BOVA11 BZ EquityEWZ US Equity0.9696
QQQ US EquitySPY US Equity0.9568
BOVA11 BZ EquityITUB US Equity0.8999
EWZ US EquityITUB US Equity0.8944
MBK PW EquityMIL PW Equity0.8912
AUAU3 BZ EquityVOD SJ Equity0.8904
BHP LN EquityRIO US Equity0.8827
AUAU3 BZ EquityBHP LN Equity0.8806
ALOS3 BZ EquityBOVA11 BZ Equity0.8778
GDX US EquityNEM US Equity0.8659
BOVA11 BZ EquityENGI11 BZ Equity0.8586
ALOS3 BZ EquityMOTV3 BZ Equity0.8571
AUAU3 BZ EquityMTM SJ Equity0.8556
ALOS3 BZ EquityEWZ US Equity0.8521
TIMS3 BZ EquityVIVT3 BZ Equity0.8521
EWZ US EquitySANB11 BZ Equity0.8466
BOVA11 BZ EquitySANB11 BZ Equity0.8456
EWW US EquityGFNORTEO MM Equity0.8412
BOVA11 BZ EquityMOTV3 BZ Equity0.8392
ENGI11 BZ EquityMOTV3 BZ Equity0.8381
3. Clusters
Cluster Map (PCA Space)

Each dot = one security projected onto PC1 vs PC2. Colors = cluster assignments (hierarchical clustering on PC1 residuals). Names close together share similar risk profiles. Click a point or use the dropdown below to explore any cluster.

Cluster-to-Cluster Correlation

Average correlation between cluster members. Clusters with high inter-correlation may represent the same underlying risk factor. Labels show auto-generated cluster names.

4. Brazil Deep Dive
Brazil Internal Clusters

Brazil names analyzed separately (PC1 explains ~51% of Brazil variance). Internal clustering reveals sub-groups not visible in the global analysis. Click a point or use the dropdown to inspect each Brazil cluster.

5. Factor Analysis
Risk Decomposition

How much of the portfolio's total variance is explained by each macro factor group. Residual = idiosyncratic risk not captured by factors -- high residual indicates strong stock-level diversification.

Current: Equity: 16.0% is the dominant macro driver. Residual: 82.7%.
Factor Group Legend
GroupFactorsBloomberg Tickers
EquityDM Equity + EM EquitySPX Index, MXEF Index
RatesUS RatesUSGG10YR Index
FXUSDDXY Curncy
CommoditiesOil + Gold + CopperCO1 Comdty, XAU Curncy, HGA Comdty
VolatilityVolatilityVIX Index
Portfolio Factor Exposure

Net portfolio beta to each factor (weight-adjusted). Positive = long exposure. Negative USD = portfolio benefits from dollar weakness.

Rolling Factor Attribution

52-week rolling R2 decomposition showing which factors drive portfolio returns over time. Black dashed line = total R2. Caveat: assumes static portfolio weights.

Methodology

Data

Weekly price data for 365 securities over 261 weeks (2021-03-09 to 2026-03-02). Source: Bloomberg via Risk Tool spreadsheet. Returns: log returns for prices, simple diff for yield levels (USGG10YR).

Network Density

Rolling 26-week window. Count pairs with |corr| > 0.6, divide by total pairs N*(N-1)/2. Regimes: LOW (<2.5%), CAUTIOUS (2.5-4%), ELEVATED (4-5.5%), CRISIS (>5.5%).

Correlations

Gross = raw returns. Net = short positions sign-flipped (P&L perspective). Rolling: 26w. Structural: full ~2.5y sample. Regions: LATAM, EMEA, Cross Over (DM countries).

Clustering

PCA on full correlation matrix. PC1 removed before clustering (avoids mega-cluster from market beta). Hierarchical clustering (Ward's method) on PC1 residuals, K selected by silhouette score. Brazil: separate PCA (PC1=51%), 10 internal clusters. Naming: dominant region + sector + style (High Beta if vol>45%, Low Vol if <25%, else Core).

Factor Analysis

8 reduced factors (VIF all <5): USGG10YR, DXY, CO1, XAU, MXEF, SPX, HGA, VIX. Multivariate OLS per stock. Portfolio exposure = weighted betas. Min 80 obs required.

Risk decomposition: contrib_f = beta_f * cov(X_f, y) / var(y). Grouped: Equity (SPX+MXEF), Rates, FX, Commodities (CO1+XAU+HGA), Vol. Residual = 1 - sum.