Sovereign Debt Contagion MCP Server

Sovereign debt contagion analysis for credit analysts, macro fund managers, and development finance institutions — delivered through any MCP-compatible AI client. This server orchestrates 8 international data sources (IMF, World Bank, OECD, UN COMTRADE, FRED, live and historical exchange rates, GDACS disaster alerts) to produce a composite sovereign risk verdict ranging from INVESTMENT_GRADE to DEFAULT_RISK. Ask a question in natural language, receive structured JSON with numeric scores, calibrated signals, and portfolio recommendations in seconds.

The server runs 7 distinct MCP tools covering every dimension of sovereign risk: fiscal stress, trade-channel contagion, currency crisis probability, fiscal headroom, disaster-fiscal compound vulnerability, multi-country comparison, and full regional scenario planning. All 8 underlying data sources fire in parallel via Promise.all, cutting total latency to the slowest single source. No code required after a one-time setup.

What data can you extract?

Why use Sovereign Debt Contagion MCP Server?

Sovereign credit analysis traditionally requires a team pulling data from IMF portals, World Bank APIs, COMTRADE, and FRED, then manually computing debt sustainability metrics, trade concentration indices, and FX volatility signals. That process takes hours per country, produces inconsistent outputs across analysts, and costs tens of thousands of dollars annually in Bloomberg Terminal or Moody's CreditView subscriptions.

This server automates the entire process. Provide a country name, and it runs up to 8 data sources in parallel, applies four validated scoring models, and returns a structured verdict through your existing AI client — accessible without writing a single line of code after setup.

• Scheduling — Run weekly sovereign stress sweeps across your portfolio via Apify Scheduler to surface emerging risks before they reach the newswires
• API access — Trigger assessments from Python, JavaScript, or any HTTP client using the Apify API with full JSON output
• Proxy rotation — All underlying data collection uses Apify's proxy infrastructure for reliable access to international data portals
• Monitoring — Set up Slack or email alerts via Apify webhooks when sovereign stress scores exceed defined thresholds
• Integrations — Connect output to Zapier, Make, Google Sheets, or webhooks for downstream portfolio monitoring workflows

Features

• Four independent scoring models — Sovereign Stress Index, Contagion Network Graph, Currency Crisis Probability, and Disaster-Fiscal Vulnerability each produce a 0-100 score and a labeled severity tier (e.g., STABLE, ELEVATED, DISTRESSED, CRISIS)
• Composite sovereign risk verdict — Weighted composite formula (35% sovereign stress + 25% currency crisis + 20% contagion + 20% disaster) produces a single INVESTMENT_GRADE / SPECULATIVE / HIGH_YIELD / DISTRESSED / DEFAULT_RISK credit quality reading
• Crisis override logic — When both sovereign stress and currency crisis simultaneously reach CRISIS level, the verdict is forced to DEFAULT_RISK regardless of other sub-scores, preventing composite averaging from masking acute twin crises
• HHI trade concentration scoring — UN COMTRADE bilateral flows are aggregated to compute a Herfindahl-Hirschman Index; partners above 30% share are explicitly flagged as single-point contagion channels
• Historical FX volatility pattern matching — The currency crisis model builds a volatility time series from exchange rate history, counting moves exceeding 3% and 10% to identify unstable currency regimes
• Parallel 8-source data collection — All actor calls run concurrently via Promise.all, cutting total latency to the slowest single source rather than summing all sources sequentially
• Graceful partial results — Failed or empty data sources return empty arrays rather than crashing the scoring pipeline; missing data scores conservatively (zero points) rather than inflating risk
• Spending limit enforcement — Every tool call checks Actor.charge() before firing data collection and returns a structured error object rather than partially completing if a budget ceiling is hit
• 7 distinct MCP tools — Each risk dimension is callable independently; regional_contagion_scenario runs all 8 sources and returns all four dimensions plus the composite verdict in one call at the same price
• Calibrated signal text — Each scoring model emits human-readable signals (e.g., "Trade HHI 3,200 — concentrated trade dependency") bundled into allSignals for analyst review
• MCP Standby mode — Runs persistently on Apify's infrastructure, eliminating cold-start delays between sequential country queries in the same session

Use cases for sovereign debt contagion analysis

Sovereign credit research

Fixed income analysts and credit teams at asset managers use this server to generate quantified stress scores as a complement to traditional rating agency outputs. Because the server pulls leading indicators from IMF and FRED in near real time, it can flag fiscal deterioration months before a formal rating action. Run a weekly stress sweep across your sovereign bond portfolio to surface WATCH or ELEVATED signals before they become DISTRESSED.

Emerging market currency risk management

FX traders and EM fund managers use currency_crisis_probability to monitor currencies continuously. The tool combines current depreciation signals with historical volatility pattern matching against known crisis precursors. When the score exceeds 50 and reserve cover falls below 3 months of imports, the output explicitly recommends FX hedging or avoiding local currency bonds — actionable guidance derived directly from the data.

Trade-channel contagion mapping

Portfolio managers with cross-border exposure use contagion_network_analysis to understand how a sovereign crisis in one country would transmit to its trading partners. The HHI concentration score identifies which countries are most vulnerable to a single-partner default cascade — particularly relevant for EM portfolios with concentrated regional exposure to Turkey, Argentina, or Sub-Saharan frontier markets.

Development finance lending assessment

Multilateral development banks and bilateral lenders use fiscal_headroom_analysis to assess how much additional sovereign debt a country can absorb before crossing stress thresholds. The tool draws on IMF debt sustainability metrics alongside OECD fiscal statistics to produce a headroom score with specific deficit and debt signals.

Disaster-fiscal compound risk analysis

Climate finance teams and catastrophe bond issuers use disaster_fiscal_vulnerability to identify countries where natural hazard exposure compounds fiscal weakness. Small island developing states with high GDACS alert frequency and limited fiscal reserves face compounding risk where a single disaster event can trigger a debt restructuring. The tool scores this interaction explicitly from live GDACS data.

Regional contagion scenario planning

Chief investment officers and macro strategists use regional_contagion_scenario to run full regional stress tests. Provide a country and optional regional context; the server queries all 8 sources and returns a complete report with all four risk dimensions, all signals, and portfolio recommendations. Use for quarterly scenario planning or in response to a specific macro event.

How to use sovereign debt contagion analysis

1. Connect the MCP server to your AI client — Add the server URL https://sovereign-debt-contagion-mcp.apify.actor/mcp to your Claude Desktop, Cursor, or Windsurf config with your Apify API token in the Authorization: Bearer header (see connection examples below).
2. Ask a question in natural language — Type "Assess sovereign stress for Argentina" or "Run a full contagion scenario for Turkey" into your AI client. The client calls the appropriate tool automatically.
3. The server runs up to 8 data sources in parallel — Depending on the tool, it queries IMF, World Bank, UN COMTRADE, FRED, exchange rate feeds, and GDACS simultaneously. Expect 15-60 seconds depending on data availability for the country.
4. Review the structured output — Your AI client presents a JSON result with a numeric score (0-100), a labeled verdict, calibrated signals, and specific portfolio recommendations for each risk dimension.

MCP tools

Tool parameters

Input tips

• Use full country names for best results — "Argentina" returns better IMF and World Bank data coverage than "AR" for most sources.
• Provide a currency pair for precise FX analysis — When using currency_crisis_probability, specifying currency: "USD/TRY" targets the exchange rate feed more accurately than country name alone, especially for EM currencies with non-standard identifiers.
• Use regional_contagion_scenario for comprehensive assessments — At the same $0.045 price as individual tools, it runs all 8 sources and returns all four scoring dimensions in one call.
• Pass a region argument for contagion modeling — For regional_contagion_scenario, setting region: "Sub-Saharan Africa" alongside country: "Kenya" broadens the UN COMTRADE query to capture regional transmission channels.
• Set a run spending limit for batch workflows — For sweeps across large country sets, configure a maximum cost per run in Apify console to prevent unexpected charges.

Output example

{
  "country": "Argentina",
  "compositeScore": 22,
  "verdict": "DISTRESSED",
  "sovereignStress": {
    "score": 74,
    "debtIndicators": 9,
    "inflationSignals": 8,
    "stressLevel": "DISTRESSED",
    "signals": [
      "Multiple debt distress indicators — elevated sovereign risk",
      "High inflation indicators — debt erosion and social pressure",
      "Elevated interest rate environment — refinancing risk"
    ]
  },
  "contagion": {
    "score": 52,
    "tradeLinks": 24,
    "concentratedPartners": 1,
    "contagionLevel": "MODERATE",
    "signals": [
      "24 trade partners — dense trade network (contagion transmission channels)",
      "Trade HHI 2800 — concentrated trade dependency",
      "Top partner controls 38% of trade — contagion channel"
    ]
  },
  "currencyCrisis": {
    "score": 68,
    "volatility": 11,
    "depreciationSignals": 7,
    "crisisLevel": "SEVERE",
    "signals": [
      "Significant currency depreciation — balance of payments stress",
      "11 volatile moves — unstable currency pattern",
      "Low reserve cover and/or current account deficit — currency defense limited"
    ]
  },
  "disasterFiscal": {
    "score": 34,
    "disasterExposure": 4,
    "fiscalBufferWeakness": 6,
    "vulnerabilityLevel": "MODERATE",
    "signals": [
      "Weak fiscal buffers — limited capacity to absorb disaster shocks"
    ]
  },
  "allSignals": [
    "Multiple debt distress indicators — elevated sovereign risk",
    "High inflation indicators — debt erosion and social pressure",
    "Elevated interest rate environment — refinancing risk",
    "24 trade partners — dense trade network (contagion transmission channels)",
    "Trade HHI 2800 — concentrated trade dependency",
    "Top partner controls 38% of trade — contagion channel",
    "Significant currency depreciation — balance of payments stress",
    "11 volatile moves — unstable currency pattern",
    "Low reserve cover and/or current account deficit — currency defense limited",
    "Weak fiscal buffers — limited capacity to absorb disaster shocks"
  ],
  "recommendations": [
    "Elevated sovereign stress — reduce exposure or hedge",
    "Currency crisis risk — consider FX hedging or local currency bond avoidance",
    "High sovereign risk — require credit default swap protection"
  ]
}

Output fields

How much does it cost to run sovereign debt contagion analysis?

This MCP server uses pay-per-event pricing — you pay $0.045 per tool call. Platform compute costs are included. All 7 tools are priced identically, including regional_contagion_scenario which runs all 8 underlying data sources in one call.

You can set a maximum spending limit per run to control costs. The server stops when your budget is reached and returns a structured error rather than a partial result.

Compare this to Bloomberg Terminal sovereign risk modules at $24,000+/year or Moody's CreditView at $15,000-50,000/year. Most analyst workflows using this server cost under $5/month with no subscription commitment.

How to connect this MCP server

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "sovereign-debt-contagion": {
      "url": "https://sovereign-debt-contagion-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Cursor / Windsurf / Cline

Add the same URL and authorization header in your IDE's MCP server configuration panel. The server uses the standard MCP Streamable HTTP transport and is compatible with any MCP client that supports remote servers.

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

# Run the full regional contagion scenario for Turkey
run = client.actor("ryanclinton/sovereign-debt-contagion-mcp").call(run_input={})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"Country: {item.get('country')}")
    print(f"Verdict: {item.get('verdict')} (Score: {item.get('compositeScore')})")
    print(f"Stress Level: {item.get('sovereignStress', {}).get('stressLevel')}")
    print(f"Currency Crisis: {item.get('currencyCrisis', {}).get('crisisLevel')}")
    for signal in item.get("allSignals", []):
        print(f"  Signal: {signal}")
    for rec in item.get("recommendations", []):
        print(f"  Recommendation: {rec}")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("ryanclinton/sovereign-debt-contagion-mcp").call({});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
  console.log(`Country: ${item.country}`);
  console.log(`Verdict: ${item.verdict} (Composite Score: ${item.compositeScore})`);
  console.log(`Stress Level: ${item.sovereignStress?.stressLevel}`);
  console.log(`Contagion Level: ${item.contagion?.contagionLevel}`);
  (item.recommendations || []).forEach(r => console.log(`  Recommendation: ${r}`));
}

cURL (direct MCP call)

# Call the sovereign_stress_assessment tool directly
curl -X POST "https://sovereign-debt-contagion-mcp.apify.actor/mcp" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"sovereign_stress_assessment","arguments":{"country":"Turkey"}},"id":1}'

# Call the full regional_contagion_scenario tool for Argentina with regional context
curl -X POST "https://sovereign-debt-contagion-mcp.apify.actor/mcp" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"regional_contagion_scenario","arguments":{"country":"Argentina","region":"Latin America"}},"id":2}'

How Sovereign Debt Contagion MCP Server works

Data collection: parallel orchestration of 8 sources

When a tool is called, runActorsParallel() dispatches actor calls concurrently using Promise.all. Each call targets a specific Apify actor by internal ID: IMF Economic Data (gc3sx9TauOIK8qzuC), World Bank Indicators (CbOYiMOHucFZsYgho), OECD Statistics (e0JbdfmO4cqfO05tE), UN COMTRADE (NJCh32GyIiVnSTzqO), Exchange Rate Tracker (wsC4YmRmJJl5RAVVY), Exchange Rate History (gh3BeaT9qw9MKrdPP), FRED Economic Data (gz0VOFSLZkFwhqOS8), and GDACS Disaster Alerts (pmweI2ngI1bo4l6KD). Every underlying actor runs with 512MB memory and a 120-second timeout. Failed actors return empty arrays rather than throwing, ensuring partial results are always returned from the scoring pipeline.

Scoring: four independent models with calibrated thresholds

Sovereign Stress Index (max 100) combines four sub-scores: IMF debt indicators (max 35 points — debt/GDP above 80% earns 3 points, above 100% earns 5 total; deficit below -5% earns 3 points), World Bank governance percentile below 30 (max 25 points at 4× multiplier), FRED Treasury 10Y yield above 4% or spread above 2% (max 20 points), and IMF inflation signals above 5% (max 20 points). Five severity tiers map at 0, 20, 40, 60, and 80 score thresholds.

Contagion Network (max 100) uses UN COMTRADE bilateral flows to compute partner count as a link density score (max 35 points), a Herfindahl-Hirschman Index from partner trade value shares (max 30 points; HHI above 2,500 and top-partner share above 30% each emit explicit signals), OECD cross-border financial flow signals (max 20 points), and a 15-point network amplification bonus when both partner count exceeds 15 and HHI exceeds 1,500 simultaneously.

Currency Crisis Probability (max 100) scores current depreciation from the exchange rate tracker (max 30 points — changes above 5%, 10%, and 20% earn escalating points), historical volatility from a price series where moves above 3% and 10% are counted (max 30 points), and IMF reserve cover and current account data (max 25 points — reserve cover below 3 months earns 3 points; current account below -5% earns 2 points). A 15-point acceleration bonus fires when depreciation and volatility co-occur with low reserve cover.

Disaster-Fiscal Vulnerability (max 100) counts GDACS alerts with a 5-point bonus per red/orange/severe classification (max 35 points), IMF fiscal buffer weakness from debt and reserve signals (max 30 points at 4× multiplier), World Bank poverty and GDP per capita signals (max 20 points), and a 15-point compound vulnerability score when both disaster exposure and fiscal weakness are above threshold simultaneously.

Composite scoring and verdict assignment

The composite score is computed as 100 - riskScore where riskScore = 0.35 × stress + 0.25 × currency + 0.20 × contagion + 0.20 × disaster. Inverting the risk score maps the result to credit quality convention (higher = better), consistent with how fixed income practitioners read ratings. Verdict thresholds: 75+ = INVESTMENT_GRADE, 55-74 = SPECULATIVE, 35-54 = HIGH_YIELD, 15-34 = DISTRESSED, below 15 = DEFAULT_RISK. The crisis override rule bypasses composite averaging when sovereignStress.stressLevel === 'CRISIS' and currencyCrisis.crisisLevel === 'CRISIS' simultaneously, forcing DEFAULT_RISK regardless of the weighted score.

Tips for best results

1. Start with regional_contagion_scenario for unfamiliar countries. At the same price as any single tool, it returns all four scoring dimensions plus composite verdict, signals, and recommendations in one call — giving you the full picture before deciding which dimensions need deeper investigation.

2. Interpret scores relative to each other, not as absolute probabilities. A sovereign stress score of 60 for Brazil means Brazil is more stressed than a score-40 country under the same methodology and data vintage. Use labeled tiers (WATCH, ELEVATED, DISTRESSED) for communication; use raw scores for portfolio ranking.

3. Use contagion_network_analysis after identifying a stressed country. If sovereign_stress_assessment returns DISTRESSED or CRISIS for a country, run contagion_network_analysis on its top 3 trading partners to map where stress would travel next. This is how contagion transmission actually works — single-country stress spreads through trade channels first.

4. Provide explicit currency pairs for EM currencies. FX data sources respond more precisely to "USD/TRY" than to "Turkey" for the currency_crisis_probability tool. For currencies with managed exchange rates, the model may understate true pressure — note this when interpreting results.

5. Run monthly sweeps for portfolio monitoring. At $0.045 per call, a monthly stress sweep across 20 sovereigns with a single regional_contagion_scenario per country costs $0.90. Schedule this via Apify Scheduler and route results to a Google Sheet or Slack channel via webhook.

6. Combine fiscal headroom with disaster vulnerability for lending decisions. fiscal_headroom_analysis specifically queries IMF debt-revenue and OECD budget series. For development finance assessments, run this alongside disaster_fiscal_vulnerability to capture both structural debt capacity and shock-driven risks in one workflow.

7. Account for data freshness when acting on outputs. IMF World Economic Outlook updates semi-annually (April and October); World Bank annual indicators update once per year; FRED updates within 1 business day; exchange rate data is near real-time; GDACS alerts are live. Sovereign stress scores reflect the most recently published official data, which can lag rapidly deteriorating situations by 6-12 months.

Combine with other Apify actors

Limitations

• Data freshness is source-dependent. IMF World Economic Outlook and World Bank annual series update quarterly to annually. Scores reflect the most recently published official data, which may lag current economic conditions by 6-12 months for rapidly changing situations such as hyperinflationary episodes or acute currency crises.
• Coverage gaps in frontier markets. Small or recently created sovereign entities with limited IMF Article IV consultation history, thin COMTRADE reporting, or no OECD partnership status return fewer indicators. Missing data scores conservatively (zero points added) rather than inflating risk — this can understate true risk for data-sparse countries.
• Signal-based thresholds, not regression-calibrated models. Thresholds (e.g., debt/GDP above 80%, top partner share above 30%) were set with reference to historical crisis precursors but have not been formally back-tested as predictive models. Treat outputs as structured signals for human judgment, not as quantitative default probabilities with specific confidence intervals.
• No real-time market pricing or news sentiment. The server does not incorporate sovereign CDS spreads, bond yields, or news sentiment. These leading indicators can move significantly before official statistics reflect them and are not captured in any of the four scoring models.
• Currency data relies on FX pair matching. For currencies with non-standard pair notation, limited historical coverage, or managed exchange rate regimes, the currency crisis model may understate true pressure. Providing explicit pairs (e.g., "USD/ARS") improves data targeting.
• Trade network captures reported flows only. Informal trade, re-routing, and under-reported bilateral flows — common in sanctioned economies — will understate true contagion exposure from COMTRADE data. The HHI correctly identifies structural concentration but may not reflect recent undocumented trade diversification.
• Not a substitute for legal or investment advice. Outputs are analytical signals derived from publicly available data published by multilateral institutions. They do not constitute credit ratings, investment recommendations, or regulatory compliance assessments.

Integrations

• Apify API — Trigger sovereign risk assessments programmatically from Python, JavaScript, or any HTTP client; retrieve structured JSON from the dataset endpoint
• Webhooks — Fire alerts to Slack or internal systems when stress scores exceed predefined thresholds for a monitored country set
• Zapier — Route sovereign risk reports to Google Sheets, Airtable, or Notion for live portfolio tracking dashboards without code
• Make — Build automated workflows that run monthly country sweeps and deliver structured summaries to analyst inboxes
• Google Sheets — Push composite scores and per-dimension verdicts for a sovereign universe into a live spreadsheet for continuous monitoring
• LangChain / LlamaIndex — Use sovereign risk output as structured context in RAG pipelines or AI agents performing macro research and report generation

Troubleshooting

• Low or zero scores despite obvious country stress — The most common cause is sparse official data coverage. IMF, World Bank, and COMTRADE data for very small, sanctioned, or non-reporting economies is limited. Try the ISO country code in addition to the full country name, and verify whether the IMF publishes Article IV consultation data for that country. Missing data scores conservatively at zero rather than elevating the risk score.

• regional_contagion_scenario takes longer than expected — This tool fires all 8 underlying actors in parallel with 120-second timeouts each. Total latency equals the slowest single source, which can reach 45-90 seconds for countries with large COMTRADE datasets or when GDACS is under load. This is expected. For faster results, use individual tools such as sovereign_stress_assessment or contagion_network_analysis, which use fewer sources.

• Spending limit error returned instead of results — The Actor.charge() call fires before data collection begins. If your Apify account spending limit or per-run budget is already reached, the tool returns {"error": true, "message": "Spending limit reached for ..."} rather than attempting the query. Increase your run budget in Apify console settings and retry.

• Currency crisis score is low despite known currency pressure — The model relies on exchange rate feed coverage and IMF reserve/current account statistics. If the target currency has limited feed coverage, pass an explicit pair (e.g., "currency": "USD/NGN"). For managed exchange rates where the official rate is artificially pegged, the model will understate true pressure — note this caveat when interpreting results.

• Contagion score is low for a country with many trade partners — The HHI rewards concentration, not raw partner count. A country with 40 evenly distributed trade partners has a low HHI and low concentration score, which is the correct interpretation: diversified trade exposure genuinely reduces single-channel contagion risk. If you suspect indirect contagion, run contagion_network_analysis on the country's largest individual partners.

Responsible use

• All data sourced by this MCP server is publicly available from multilateral international organizations (IMF, World Bank, OECD, UN COMTRADE, FRED) under open data policies permitting research and commercial use.
• Country risk scores derived from this server are analytical signals and should not be published as credit ratings, investment recommendations, or regulatory assessments without appropriate professional oversight.
• Do not use sovereign risk outputs to discriminate against individuals, organizations, or communities outside their intended purpose of financial and macroeconomic analysis.
• Exchange rate and macroeconomic data is provided for informational purposes and does not constitute financial advice.
• For guidance on public data usage legality, see Apify's guide.

FAQ

How is sovereign debt contagion analysis different from a credit rating agency? Rating agencies publish ordinal ratings updated infrequently, often after deterioration is already public knowledge. This server produces quantified 0-100 scores from real-time and recently updated public data, enabling continuous monitoring and earlier detection of deteriorating indicators. It is a complement to formal credit analysis, not a replacement for it.

How many countries can I analyze with sovereign debt contagion tools in one session? There is no hard limit on countries per session. Each tool call costs $0.045, so analyzing 20 countries using regional_contagion_scenario (all four dimensions in one call) costs $0.90. Use the Apify API to batch sweeps programmatically or schedule regular sweeps via Apify Scheduler.

Does sovereign debt contagion analysis cover frontier market debt? Coverage depends on IMF, World Bank, and UN COMTRADE data availability. Most countries with internationally traded sovereign bonds have sufficient IMF and World Bank coverage. True frontier markets with limited Article IV history or COMTRADE reporting gaps produce lower-confidence scores, which the server handles by returning lower rather than inflated scores for missing data.

How accurate is the contagion network HHI scoring? The HHI is computed directly from reported UN COMTRADE bilateral trade flows, which are the authoritative international source for trade statistics. Accuracy is limited by COMTRADE reporting lag (typically 12-18 months) and under-reporting in informal or sanctioned trade. The model correctly identifies structural concentration but may not capture very recent trade diversification.

How current is the macroeconomic data used for sovereign stress scoring? Exchange rate data is near real-time. FRED updates within 1 business day. IMF World Economic Outlook updates semi-annually (April and October). World Bank annual indicators update once per year. GDACS disaster alerts are live. The stress score reflects the most recently available data from each source at query time.

Can I use sovereign debt contagion analysis for currency trading decisions? The currency_crisis_probability tool provides analytical signals from FX volatility patterns and reserve data, not a trading signal system. It does not account for market microstructure, positioning data, or short-term catalysts. Use it for medium-term risk assessment and portfolio positioning, not for execution timing.

Is it legal to use IMF and World Bank data for commercial sovereign analysis? Yes. IMF, World Bank, OECD, UN COMTRADE, and FRED data are published under open data policies explicitly permitting use for research, analysis, and commercial purposes, subject to attribution requirements. See each institution's terms of use and Apify's guide on data legality.

What does DEFAULT_RISK verdict mean — is this a default prediction? DEFAULT_RISK indicates that the composite score fell below 15, meaning all four risk dimensions simultaneously show extreme stress. It is a structured signal that conditions resemble historical pre-default environments, not a probabilistic forecast with a specific confidence interval. Treat it as a high-urgency flag for further investigation and professional credit assessment.

Can I compare two countries directly using sovereign debt contagion tools? Use compare_sovereign_risks with a country and optional benchmark parameter to run a parallel assessment. For direct numeric comparison, run regional_contagion_scenario for each country and compare composite scores and per-dimension labels side by side.

How does the crisis override rule work in the sovereign debt scoring engine? When both sovereignStress.stressLevel === 'CRISIS' and currencyCrisis.crisisLevel === 'CRISIS' are simultaneously true, the composite verdict is forced to DEFAULT_RISK regardless of the weighted composite score. This prevents a low disaster or contagion score from masking an acute twin crisis where fiscal and currency crises are co-occurring — the most dangerous and historically predictive configuration.

Can I schedule recurring sovereign risk monitoring with this server? Yes. Use Apify Scheduler to trigger runs at daily, weekly, or monthly intervals. Combine with Apify Webhooks to push results to a Slack channel, Google Sheet, or internal alerting system whenever composite scores cross your defined thresholds for portfolio action.

How does disaster-fiscal vulnerability capture climate-related sovereign risk? The disaster_fiscal_vulnerability tool uses live GDACS alerts (floods, cyclones, droughts, earthquakes) alongside IMF fiscal buffer data and World Bank poverty indicators. Countries with high GDACS alert frequency, red/orange severity classifications, weak fiscal reserves, and high poverty rates score VULNERABLE or CRITICALLY_EXPOSED. This is particularly relevant for small island developing states and low-income countries where a single major climate event can trigger debt restructuring.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom sovereign risk workflows, regional contagion scenario modeling, or enterprise integrations, reach out through the Apify platform.