Portfolio Stress Testing Strategy Analysis
Run comprehensive portfolio stress tests with Sourcetable AI. Calculate VaR, execute Monte Carlo simulations, and analyze extreme scenarios using natural language—no complex formulas required.
Andrew Grosser
February 16, 2026 • 19 min read
Understanding Portfolio Stress Testing
Market crashes don't announce themselves. The 2008 financial crisis saw portfolios lose 50% in months. COVID-19 triggered a 34% drop in weeks. Risk managers who ran proper stress tests knew their exposure limits and survived. Those who didn't faced catastrophic losses.
Portfolio stress testing simulates how your investments perform under extreme market conditions. You model scenarios like interest rate spikes, equity crashes, credit spread widening, or currency shocks. The goal is understanding potential losses before they happen—identifying vulnerabilities when you can still act sign up free.
Traditional stress testing in Excel means building correlation matrices, coding Monte Carlo simulations, calculating Value at Risk (VaR), and running hundreds of scenarios manually. A single portfolio with 50 positions requires thousands of calculations. Change one assumption and you're rebuilding formulas for hours.
Sourcetable transforms this process completely. Upload your portfolio data and ask questions in plain English: 'What happens if equities drop 30%?' or 'Run a Monte Carlo simulation with 10,000 iterations.' The AI understands risk terminology, executes complex calculations automatically, and generates visual reports instantly. No VBA coding, no formula debugging, no manual scenario building.
Risk managers at institutional investors use Sourcetable to run daily stress tests that previously took weeks. Portfolio managers analyze tail risk in minutes instead of days. Compliance teams generate regulatory stress reports with a single question. Try it yourself at and see how AI handles the complexity while you focus on decisions. sign up free.
Why Sourcetable Excels at Portfolio Stress Testing
Excel stress testing requires advanced statistical knowledge and programming skills. You need to understand covariance matrices, implement Cholesky decomposition for correlated random variables, code Monte Carlo engines in VBA, and build complex scenario frameworks. A typical institutional stress testing model contains 10,000+ cells of interconnected formulas. One error cascades through the entire model.
Sourcetable's AI eliminates this technical barrier completely. The system understands portfolio risk concepts natively—VaR, CVaR, stress scenarios, correlation structures, tail risk, and extreme value theory. You describe what you want to test in conversational language, and Sourcetable executes the appropriate statistical methods automatically.
Ask 'Calculate 99% VaR for my equity portfolio using historical simulation' and Sourcetable identifies your positions, pulls historical returns, ranks loss scenarios, and returns the VaR figure with supporting visualizations. Request 'Run a Monte Carlo simulation assuming 40% volatility spike' and the AI generates thousands of correlated price paths, calculates portfolio outcomes, and shows the distribution of potential losses.
The real power emerges with scenario analysis. Instead of manually adjusting dozens of inputs across multiple worksheets, you simply state the scenario: 'Model a recession with 25% equity decline, 200 basis point rate increase, and 15% dollar strengthening.' Sourcetable applies these shocks simultaneously, accounts for correlations, and shows total portfolio impact in seconds.
Sourcetable also handles the data infrastructure that Excel struggles with. Import positions from your portfolio management system, pull historical prices from market data feeds, update risk factors automatically, and refresh stress tests on schedule. When new positions arrive or market conditions change, your stress testing framework updates instantly without formula rewrites.
For institutional investors managing billions, this means running comprehensive stress tests daily instead of quarterly. For portfolio managers, it means understanding risk exposure for every trade before execution. For risk committees, it means having current stress analysis ready for every meeting without weeks of analyst preparation time.
Benefits of Portfolio Stress Testing with Sourcetable
Portfolio stress testing reveals hidden vulnerabilities before they destroy capital. Proper stress analysis shows you which market moves hurt most, where concentrations create risk, and how much you could lose in extreme conditions. Sourcetable makes this critical analysis accessible to every investment professional, not just quantitative specialists.
Instant Monte Carlo Simulations
Monte Carlo simulation generates thousands of possible future scenarios by randomly sampling from return distributions. In Excel, this requires coding random number generators, implementing correlation structures using Cholesky decomposition, running loops across scenarios, and aggregating results—easily 500+ lines of VBA code.
Sourcetable handles this with a single question: 'Run 10,000 Monte Carlo iterations on my portfolio.' The AI understands your positions, calculates historical volatilities and correlations, generates correlated random returns, values the portfolio across all scenarios, and produces distribution charts showing the range of outcomes. You see the full probability distribution of potential gains and losses in under 30 seconds.
For a $50 million portfolio with 40 positions, Sourcetable might show you have a 5% chance of losing more than $8 million over the next quarter, a 1% chance of losing $12 million, and a median gain of $2.5 million. These insights let you size positions appropriately and set risk limits based on actual probability distributions, not guesswork.
Automated VaR Calculations
- Historical Simulation VaR: Rank the past 500 daily P&L observations from worst to best; the 5th worst day (bottom 1%) gives the 99% 1-day VaR; a $50M portfolio with 99% VaR of $1.8M expects to exceed that loss only 1.25 times per year.
- Parametric (Delta-Normal) VaR: Assumes normal distribution: VaR = Portfolio Value × Z-score × Daily Volatility; for 99% confidence, Z = 2.33; a $10M portfolio with 1.2% daily vol has 99% VaR of $279,600 per day.
- Square-Root-of-Time Scaling: Convert 1-day VaR to 10-day VaR by multiplying by √10 (≈ 3.16); a $1M daily 99% VaR becomes a $3.16M 10-day VaR, which is the standard Basel regulatory reporting horizon.
- Conditional VaR (CVaR/Expected Shortfall): Average loss in the worst (1-confidence) scenarios; if 99% VaR is $2M but the 10 worst days average $3.5M, CVaR is $3.5M—a better risk measure for fat-tailed portfolios like those holding options or credit instruments.
- VaR Backtesting Standard: A properly calibrated 99% VaR model should be breached on approximately 2.5 days per year; more than 10 breaches indicates model underestimation and triggers regulatory review under Basel III traffic-light methodology.
Value at Risk (VaR) quantifies maximum expected loss at a given confidence level. A 99% daily VaR of $2 million means you expect losses exceeding $2 million only 1% of trading days. Calculating VaR properly requires historical data, return calculations, proper ranking, and careful handling of time horizons.
Ask Sourcetable 'What's my 95% VaR over 10 days?' and it automatically retrieves position data, calculates historical returns, scales to your chosen horizon using square-root-of-time rule, and returns the VaR figure. Want parametric VaR instead of historical? Just ask 'Calculate parametric VaR assuming normal distribution.' Need conditional VaR (CVaR) showing average loss beyond the VaR threshold? Request 'Show CVaR at 99% confidence' and get the answer immediately.
This matters enormously for risk limits and regulatory reporting. Banks must calculate VaR daily for trading books. Investment managers need VaR for client reporting and internal risk management. Sourcetable turns a multi-hour manual process into a 10-second query, letting you run VaR calculations continuously as positions change throughout the day.
Comprehensive Scenario Analysis
Scenario analysis tests specific market moves: what happens if rates rise 100 basis points, or oil drops to $40, or credit spreads widen 200 basis points? These deterministic scenarios complement probabilistic methods like Monte Carlo and VaR by examining specific risks you're concerned about.
Sourcetable lets you define scenarios conversationally: 'Model a financial crisis scenario with equities down 40%, credit spreads up 300bp, VIX at 80, and flight to quality in Treasuries.' The AI interprets these conditions, applies appropriate shocks to each position, accounts for correlations and hedges, and calculates total portfolio impact.
You can test historical scenarios too: 'Apply 2008 financial crisis returns to my current portfolio' or 'Replay the COVID crash on my positions.' Sourcetable finds the historical factor movements, maps them to your current holdings, and shows how your portfolio would have performed. This reveals whether your current positioning would have survived past crises or faced catastrophic losses.
For a portfolio with equity, fixed income, and commodities exposure, you might test a stagflation scenario (rising rates and inflation with declining growth), a deflation scenario (falling rates and prices), or a geopolitical shock scenario (oil spike and equity decline). Running these tests in Excel means manually adjusting dozens of inputs and recalculating complex models. In Sourcetable, you describe the scenario and see results in seconds.
Real-Time Risk Visualization
Numbers alone don't communicate risk effectively. You need visual representations showing loss distributions, scenario comparisons, risk factor sensitivities, and time-series risk evolution. Creating these visualizations in Excel requires chart building, formatting, and constant manual updates.
Sourcetable generates risk visualizations automatically as you ask questions. Request 'Show the distribution of potential losses' and get a histogram of Monte Carlo outcomes with VaR and CVaR marked clearly. Ask 'Compare my current VaR to last month' and see a time-series chart showing risk evolution. Query 'Which positions contribute most to portfolio risk?' and receive a bar chart ranking risk contributions.
These visualizations update instantly as positions or market conditions change. Your risk committee sees current stress test results in presentation-ready format without analyst teams spending days building PowerPoint decks. Portfolio managers see real-time risk metrics on their dashboards, enabling faster decisions about position sizing and hedging.
Correlation and Dependency Analysis
- Crisis Correlation Regime: During the 2008 financial crisis, average equity-to-equity correlations spiked from 0.35 in calm markets to 0.78 at peak stress; a portfolio of 50 stocks with 0.78 average correlation behaves like 13 independent positions, not 50.
- Effective Number of Bets: Calculated as 1 ÷ (weighted average correlation²); a portfolio of 20 assets with 0.6 average correlation has an effective diversification of only 4.2 independent bets—far less than the apparent 20 positions suggest.
- Tail Dependence Modeling: Copula models reveal that during -3 sigma equity moves, seemingly uncorrelated assets show 70–85% joint tail dependence; standard Pearson correlation badly underestimates co-movement risk in extreme scenarios.
- Rolling Correlation Windows: 63-day (quarterly) rolling correlations are more predictive of near-term co-movement than 252-day (annual) correlations; use shorter windows for active risk management and longer windows for strategic asset allocation.
- Cross-Asset Contagion: EM currency crises (1997 Asian, 1998 Russian) showed that correlations between EM equities and DM investment-grade bonds turned sharply positive during acute stress, eliminating the usual diversification benefit of the bond allocation.
Portfolio risk depends critically on correlations between positions. A portfolio of 10 stocks with 0.9 correlation behaves like a single concentrated bet. The same 10 stocks with 0.3 correlation provides genuine diversification. Correlations also shift during crises—assets that seem uncorrelated in calm markets often move together during stress.
Ask Sourcetable 'Show correlation matrix for my equity positions' and instantly see pairwise correlations across all holdings. Request 'How do correlations change during high volatility periods?' and the AI segments data by market regime and shows correlation differences. Query 'What's my effective diversification?' and get metrics like portfolio concentration ratios and effective number of independent bets.
This analysis reveals hidden concentration risks. You might think you're diversified across 50 positions, but if they all have 0.8+ correlation to the S&P 500, you essentially have a single market exposure. Sourcetable identifies these dependencies automatically, helping you build genuinely diversified portfolios that survive various market conditions.
How Portfolio Stress Testing Works in Sourcetable
Running comprehensive portfolio stress tests with Sourcetable requires no statistical programming or complex model building. The AI handles all technical implementation while you focus on defining scenarios and interpreting results. Here's the complete workflow from data import to risk reporting.
Step 1: Import Portfolio Data
Start by uploading your portfolio positions to Sourcetable. Import directly from portfolio management systems, accounting platforms, or CSV files. Your data should include position identifiers (tickers, CUSIPs, or ISINs), quantities, current prices, and asset classifications. For a typical institutional portfolio, this might be 50-500 positions across equities, fixed income, derivatives, and alternatives.
Sourcetable automatically recognizes common data formats and maps fields intelligently. Upload a file with columns like 'Ticker', 'Shares', 'Price', 'Market Value' and Sourcetable understands the structure immediately. The AI also enriches your data by pulling additional information like historical prices, sector classifications, and risk factor exposures from integrated market data sources.
For fixed income positions, include duration, credit rating, and spread information. For derivatives, specify contract details like strikes, expirations, and underlying assets. Sourcetable handles all asset classes and automatically applies appropriate valuation and risk methods for each position type.
- Start by uploading your portfolio positions to Sourcetable.
- Sourcetable automatically recognizes common data formats and maps fields intelli.
- For fixed income positions, include duration, credit rating, and spread informat.
Step 2: Define Risk Parameters
Tell Sourcetable what risk metrics and confidence levels matter for your analysis. Simply ask: 'Calculate 95% and 99% VaR with a 10-day horizon' or 'Set up Monte Carlo with 10,000 iterations using historical volatility.' The AI configures the appropriate statistical parameters automatically.
You can specify the historical lookback period for calculating volatilities and correlations: 'Use 3-year daily returns for volatility estimation' or 'Calculate rolling 1-year correlation.' Sourcetable retrieves the historical data, handles missing values and corporate actions, and computes clean return series for analysis.
For scenario analysis, define the market moves you want to test: 'Create scenarios for mild recession (equities -15%, rates -50bp), severe recession (equities -30%, rates -100bp), and stagflation (equities -20%, rates +150bp).' Sourcetable stores these scenario definitions and applies them consistently across all analyses.
Step 3: Run Stress Tests
Execute stress tests by asking questions in natural language. Request 'Run historical VaR using the past 2 years of data' and Sourcetable calculates daily returns for each position, aggregates to portfolio level, ranks outcomes, and identifies the VaR threshold. The system shows both the VaR figure ($3.2 million at 95% confidence) and the distribution of historical outcomes.
For Monte Carlo analysis, ask 'Generate 10,000 scenarios for the next quarter.' Sourcetable computes the covariance matrix from historical returns, generates correlated random returns using Cholesky decomposition, simulates portfolio values across all scenarios, and presents the distribution of outcomes. You see percentile levels (5th, 25th, 50th, 75th, 95th), maximum drawdown across scenarios, and probability of specific loss thresholds.
Run multiple stress scenarios simultaneously: 'Test my portfolio against 2008 crisis, 2020 COVID crash, 1987 Black Monday, and 2000 dot-com bubble.' Sourcetable applies each historical period's returns to your current positions and shows how the portfolio performs under each scenario. A table compares total loss, worst single-day loss, recovery time, and which positions drove losses in each scenario.
- "Run historical VaR using the past 2 years of data"
- "Generate 10,000 scenarios for the next quarter."
- " Sourcetable applies each historical period"
Step 4: Analyze Risk Contributions
- Marginal VaR: The change in portfolio VaR from adding $1 of a specific position; a technology stock with marginal VaR of $0.18 means each additional dollar invested increases total portfolio VaR by $0.18 due to high beta and correlation with existing holdings.
- Component VaR: Each position's share of total portfolio VaR; if position A represents 8% of portfolio value but 18% of VaR, it's contributing disproportionate risk and warrants size reduction or hedging.
- Risk Decomposition by Factor: Factor regression typically shows 60–80% of equity portfolio risk is explained by market beta; idiosyncratic (stock-specific) risk represents only 20–40%, meaning sector and factor hedges are more efficient than stock-specific hedges.
- Sector Risk Concentration: A 35% technology sector weight in a 60-stock portfolio contributes 52% of total portfolio risk due to intra-sector correlations; reducing tech from 35% to 25% may cut tech-related risk from 52% to 38% of total.
- Tail Risk Contribution: CVaR attribution shows which positions dominate extreme losses; options portfolios often find that 2–3 short volatility positions account for 80%+ of tail risk despite representing only 15% of gross exposure.
Understanding which positions drive portfolio risk helps you optimize allocations and identify concentration risks. Ask Sourcetable 'Which positions contribute most to VaR?' and receive a ranked list showing each position's marginal contribution to portfolio risk. This reveals that while Position A represents 10% of portfolio value, it contributes 25% of total VaR due to high volatility or correlation with other holdings.
Request 'Show risk decomposition by sector' to see how much risk comes from technology, financials, healthcare, etc. Query 'What's my factor exposure?' to understand systematic risk from market beta, value/growth factors, size factors, and momentum. Sourcetable performs factor regression analysis automatically and shows which systematic risks dominate your portfolio.
For tail risk analysis, ask 'What happens in the worst 1% of scenarios?' Sourcetable identifies extreme outcomes, shows which positions perform worst in tail events, and calculates CVaR (average loss beyond VaR threshold). This reveals whether your portfolio has fat-tail risk where extreme losses far exceed the VaR estimate.
Step 5: Generate Risk Reports
Create comprehensive risk reports for stakeholders by asking Sourcetable to compile key metrics: 'Generate a risk report showing VaR, CVaR, stress scenario results, risk contributions, and historical performance.' The AI assembles tables and charts presenting current risk levels, trends over time, scenario analysis results, and position-level detail.
These reports update automatically as new data arrives or positions change. Schedule daily risk reports that arrive each morning showing overnight risk changes. Create weekly reports for portfolio managers showing risk-adjusted returns and risk limit utilization. Build monthly board reports with stress test results and risk trend analysis—all generated automatically without manual data gathering or chart building.
Export reports to PDF for distribution or keep them live in Sourcetable where stakeholders can ask follow-up questions. A CFO reviewing the risk report can ask 'What drives the increase in VaR this month?' and get an immediate explanation with supporting analysis.
Step 6: Monitor and Update Continuously
Risk isn't static—market conditions change, correlations shift, and new positions arrive daily. Sourcetable monitors your portfolio continuously and alerts you to material risk changes. Set thresholds like 'Alert me if VaR exceeds $5 million' or 'Notify me if correlation with S&P 500 rises above 0.85' and receive automatic notifications when triggers hit.
Connect Sourcetable to your portfolio management system for real-time position updates. As trades execute throughout the day, risk metrics refresh automatically. Ask 'How does this new trade affect my VaR?' before execution to understand risk impact. After market close, run full stress tests automatically to ensure risk remains within limits.
During market stress, increase monitoring frequency. Sourcetable can calculate intraday VaR as prices move, showing real-time risk evolution. When volatility spikes or correlations shift, rerun stress tests with updated parameters to understand changing risk profile. The AI handles all recalculations automatically—you simply monitor results and make decisions.
Portfolio Stress Testing Use Cases
Portfolio stress testing serves different purposes across investment organizations. Risk managers use it for limit monitoring and regulatory compliance. Portfolio managers use it for position sizing and hedging decisions. Investment committees use it for strategic allocation and risk budgeting. Here's how Sourcetable supports each use case with specific examples.
Risk Manager: Daily VaR Monitoring and Limit Compliance
A risk manager at a $2 billion hedge fund must calculate VaR daily and ensure trading desks stay within risk limits. The firm's risk policy sets a 95% daily VaR limit of $15 million and 99% VaR limit of $25 million. Each morning, the risk manager needs VaR calculations across the entire book and by trading desk.
With Sourcetable, the risk manager sets up automated daily risk reports that run at 7 AM before markets open. The system pulls overnight positions from the portfolio management system, calculates VaR using historical simulation with 2-year lookback, and generates a dashboard showing firm-wide VaR, desk-level VaR, and limit utilization percentages.
On a typical day, the dashboard shows firm-wide 95% VaR at $12.3 million (82% of limit) and 99% VaR at $19.7 million (79% of limit). The equity desk shows $7.2 million VaR, credit desk $4.8 million, and macro desk $3.1 million. All desks are within limits and the risk manager approves trading for the day.
When market volatility spikes, the risk manager asks Sourcetable 'Recalculate VaR using only the past 6 months' to emphasize recent higher volatility. The updated VaR shows $16.8 million, exceeding the limit. The risk manager queries 'Which positions drive the limit breach?' and Sourcetable identifies that three large equity positions contribute 60% of the excess risk. The risk manager contacts the equity desk to reduce these positions before markets open, bringing VaR back to $14.2 million and maintaining compliance.
Portfolio Manager: Pre-Trade Risk Analysis and Position Sizing
A portfolio manager running a $500 million long-short equity fund wants to add a new position: 50,000 shares of a technology stock currently trading at $180, representing a $9 million long position. Before executing, the manager needs to understand how this trade affects portfolio risk, whether it creates undue concentration, and if the position size is appropriate given expected returns.
The portfolio manager asks Sourcetable 'What happens to my VaR if I buy 50,000 shares of TECH at $180?' Sourcetable simulates adding the position, recalculates portfolio VaR, and shows that 95% VaR increases from $8.2 million to $9.4 million—a $1.2 million increase. The manager also asks 'What's my correlation with existing positions?' and learns the new stock has 0.72 correlation with current holdings, indicating significant overlap with existing tech exposure.
Concerned about concentration, the manager queries 'What percentage of portfolio risk comes from technology sector?' Sourcetable shows tech positions currently contribute 45% of total risk despite being only 35% of capital. Adding this position would increase tech risk contribution to 52%. The manager decides to reduce position size to 30,000 shares ($5.4 million), which increases VaR by only $700,000 and keeps tech risk contribution at 48%.
The manager also runs scenario analysis: 'What happens to this position if we get a 2000-style tech crash with sector down 40%?' Sourcetable applies the scenario and shows the 30,000 share position would lose $2.16 million, contributing to a total portfolio loss of $8.7 million. The manager judges this acceptable given the position's expected return and proceeds with the trade.
Institutional Investor: Quarterly Stress Testing for Board Reporting
A pension fund CIO must present quarterly risk reports to the investment committee showing how the $5 billion portfolio performs under various stress scenarios. The committee wants to see results for recession, inflation spike, geopolitical crisis, and historical analog scenarios like 2008 and 2020. They also want to understand which asset classes and managers contribute most to tail risk.
Two weeks before the committee meeting, the CIO asks Sourcetable to 'Generate comprehensive stress test report for Q4 including recession, inflation, geopolitical, 2008, and 2020 scenarios.' Sourcetable runs all scenarios simultaneously, applying appropriate shocks to equities, fixed income, commodities, and alternative investments based on each scenario's characteristics.
The results show the portfolio would lose $425 million (8.5%) in a recession scenario, $380 million (7.6%) in an inflation spike, $510 million (10.2%) in a geopolitical crisis, $890 million (17.8%) in a 2008-style financial crisis, and $625 million (12.5%) in a COVID-style shock. The 2008 scenario remains the most severe stress test, driven by losses in private equity and credit holdings that proved illiquid during that crisis.
The CIO asks follow-up questions: 'Which managers contribute most to 2008 scenario losses?' Sourcetable identifies that two private equity managers and one credit manager account for 55% of losses in that scenario despite being only 25% of the portfolio. The CIO queries 'How can we reduce 2008 scenario losses to under $750 million?' and Sourcetable suggests reducing allocations to those three managers by 30% and increasing liquid alternatives and government bonds.
The CIO presents these findings to the investment committee with charts and tables generated automatically by Sourcetable. The committee asks 'What if we get both recession and geopolitical crisis?' The CIO uses Sourcetable during the meeting to run a combined scenario, showing potential losses of $715 million (14.3%). The committee decides to implement the recommended allocation changes to reduce tail risk exposure.
Risk Committee: Regulatory Stress Testing and CCAR Compliance
A regional bank's risk committee must conduct annual Comprehensive Capital Analysis and Review (CCAR) stress testing required by regulators. This involves running the bank's $30 billion loan and securities portfolio through severely adverse scenarios defined by the Federal Reserve, including 4% GDP decline, 10% unemployment, 50% equity market decline, and significant deterioration in commercial real estate prices.
The risk team uploads the bank's complete asset portfolio to Sourcetable, including commercial loans, mortgages, securities, and derivatives. They define the Fed's severely adverse scenario: 'Model CCAR severely adverse with GDP -4%, unemployment 10%, equities -50%, CRE -30%, residential -25%, BBB spreads +550bp, and mortgage rates +100bp.'
Sourcetable applies these shocks across all asset classes, accounting for correlations and second-order effects. Commercial loan losses increase due to higher defaults in the weak economy. The securities portfolio loses value from equity declines and spread widening. Mortgage portfolios face higher delinquencies. Derivatives positions show mixed results with some hedges paying off while others lose value.
The complete stress test shows projected losses of $2.8 billion over the two-year stress horizon, reducing the bank's capital ratio from 12.5% to 9.8%. This remains above the regulatory minimum of 8%, indicating the bank has sufficient capital to withstand the severely adverse scenario. Sourcetable generates detailed loss breakdowns by asset class, business line, and time period required for regulatory submission.
The risk committee asks 'What if CRE losses are 40% instead of 30%?' to test sensitivity to the bank's significant commercial real estate exposure. Sourcetable reruns the scenario with higher CRE losses and shows the capital ratio falling to 9.3%—still above minimums but closer to the threshold. The committee decides to reduce CRE exposure and increase capital buffers to maintain larger safety margins.
Frequently Asked Questions
If your question is not covered here, you can contact our team.
Contact UsWhat are the mandated stress scenarios under Basel III and DFAST bank regulations?
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What are liquidity-adjusted stress tests and why do they matter more than standard VaR?
How should stress test results be integrated into capital allocation and portfolio limits?
What scenario correlation between stress tests and risk factor sensitivities is most revealing?
