Name: Geopolitical Risk
Author: HKUDS

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Geopolitical Risk Analysis

Overview

Quantify geopolitical risk signals, identify crisis precursors, and build event-driven strategies that convert narratives such as "war / conflict / sanctions / supply disruption" into actionable multi-asset allocation decisions.

Core Analytical Framework

1. Risk Layering Model

Layer 1: Structural risk (long-lasting, slow-moving)
  └── Great-power rivalry, alliance structures, nuclear deterrence balance

Layer 2: Situational risk (cyclical escalation, monthly / quarterly scale)
  └── Military exercises, election cycles, sanctions escalation, diplomatic friction

Layer 3: Event risk (sudden shocks, daily / hourly scale)
  └── Military action, assassination, sanctions announcements, nuclear tests

2. Five Dimensions of Risk Assessment

Dimension	Description	Quantitative Proxy
Intensity	Severity of conflict / sanctions	GPR Index percentile
Persistence	Expected duration of the crisis	Futures curve contango / backwardation
Transmission	Spillover into supply chains / finance	CDS spread widening, VIX jump magnitude
Predictability	Whether the event is already priced in	Option implied volatility skew
Reversibility	Whether the situation can be resolved through negotiation	Speed of reversal in news sentiment

# Proxy indicators
- Brent-WTI spread widening (signal of regional supply stress)
- Persian Gulf tanker insurance rates (Lloyd's H&M quotes)
- UAE dirham NDF (depreciates under stress)
- Israeli shekel volatility
- Relative strength of VanEck Oil Services ETF (OIH) vs XLE

# Proxy indicators
- Abnormal weakness in the Philadelphia Semiconductor Index (SOX)
- TSM ADR (TSM) premium / discount in the U.S. market
- Taiwan CDS spreads
- TWD NDF depreciation under stress
- KOSPI, given Korea's semiconductor linkage
- U.S.-listed Chinese ADRs / Hong Kong Hang Seng Tech Index

3-6 months: inventory drawdown, sharp price spikes
6-18 months: partial substitution by Samsung / Intel IDM advanced capacity
2-4 years: ramp-up from TSMC Arizona and Kumamoto Japan
5+ years: Mainland China's independent advanced process catch-up, with major uncertainty

# Proxy indicators
- Daily changes in the Baltic Dry Index (BDI)
- SCFI Shanghai Containerized Freight Index
- Share prices of Maersk and other container shipping companies
- Share of AIS-tracked vessels rerouting via the Cape of Good Hope (>30% is high alert)
- European TTF natural gas prices, given Red Sea LNG exposure

# Proxy indicators
- European TTF natural gas futures
- Ukrainian sovereign CDS spreads
- RUB/USD exchange rate under sanctions pressure
- Chicago wheat futures (ZW)
- European power prices, e.g. Germany EEX Baseload
- Russian ETF trading status (RSX liquidated; use substitutes)

Sanctions announcement
  ├── Financial sanctions → SWIFT cutoff → cross-border settlement disruption → emerging-market debt crisis
  ├── Energy sanctions → European gas spike → industrial energy costs → eurozone recession
  ├── Export controls → Russia semiconductor / military shortages → weaker war sustainability
  └── Grain blockade → Middle East / Africa food stress → political instability → migration pressure

# Proxy indicators
- Chinese rare-earth futures prices (permanent magnets / praseodymium-neodymium oxide)
- Philippine peso volatility
- Vietnam industrial park REITs / ETFs
- MP Materials (MP) share price as a substitute rare-earth beneficiary
- Share prices of Chinese shipping companies

# Proxy indicators
- KRW/USD volatility spike
- KOSPI decline
- South Korean CDS spreads
- JPY safe-haven inflows (JPY/USD strength)
- ADR prices of Samsung / SK Hynix

GPR: overall geopolitical risk
GPRT: geopolitical threats (forward-looking)
GPRA: geopolitical acts (events already realized)
GPR_country: country-level sub-index

import pandas as pd
import requests

def load_gpr_index():
    """Load the official GPR Index data.

    Returns:
        pd.DataFrame: Monthly GPR data with columns such as GPR, GPRT, and GPRA.
    """
    url = "https://www.matteoiacoviello.com/gpr_files/data_gpr_export.xls"
    df = pd.read_excel(url, index_col=0, parse_dates=True)
    return df

def gpr_signal(df, window=12, threshold=1.5):
    """Generate abnormal GPR signals.

    Args:
        df: DataFrame containing GPR data
        window: Rolling mean window in months
        threshold: Z-score trigger threshold in standard deviations

    Returns:
        pd.Series: Boolean signal where True means high-risk state
    """
    gpr = df["GPR"]
    rolling_mean = gpr.rolling(window).mean()
    rolling_std = gpr.rolling(window).std()
    z_score = (gpr - rolling_mean) / rolling_std
    return z_score > threshold

def oil_war_premium(spot_price, mean_5y_price, supply_disruption_prob,
                    disruption_magnitude_pct):
    """Estimate the war-risk premium embedded in crude oil.

    Method:
        A simplified model based on expected supply-disruption value.

    Args:
        spot_price: Current spot price in USD/bbl
        mean_5y_price: Five-year average price as the "no-risk" baseline
        supply_disruption_prob: Probability of supply disruption in [0, 1]
        disruption_magnitude_pct: Price impact of disruption in [0, 1]

    Returns:
        float: Estimated war premium in USD/bbl
    """
    expected_disruption_premium = (
        mean_5y_price * disruption_magnitude_pct * supply_disruption_prob
    )
    observed_premium = spot_price - mean_5y_price
    return max(0, min(observed_premium, expected_disruption_premium))

def gold_geopolitical_premium(gold_price, real_yield_10y, usd_index):
    """Decompose the geopolitical premium component in gold prices.

    Args:
        gold_price: Spot gold price in USD/oz
        real_yield_10y: 10-year real yield in percent
        usd_index: DXY index

    Returns:
        float: Geopolitical premium as the residual component
    """
    import numpy as np
    # Gold fundamentals: real rates (negative) + USD (negative)
    # Linear approximation:
    # Gold ≈ α - β1*RealYield - β2*DXY + ε (geopolitical premium)
    # β1 ≈ 800, β2 ≈ 15 are rough historical estimates that should be updated
    fundamental_value = 2000 - 800 * real_yield_10y - 15 * (usd_index - 100)
    return gold_price - fundamental_value

def update_disruption_probability(prior_prob, new_event_severity, base_rate=0.05):
    """Update supply-chain disruption probability using a new event.

    This is a simplified Bayesian update that adjusts the prior
    using the severity of the new event.

    Args:
        prior_prob: Prior disruption probability
        new_event_severity: Event severity in [0, 1]
            0.0 = diplomatic friction
            0.3 = military standoff
            0.6 = local conflict
            1.0 = full-scale war
        base_rate: Historical annualized baseline disruption rate

    Returns:
        float: Updated disruption probability
    """
    # Likelihood ratio: how much more likely the event is before a real disruption
    # than in a non-disruption state
    likelihood_ratio = 1 + 9 * new_event_severity  # 1x ~ 10x
    posterior = (prior_prob * likelihood_ratio) / (
        prior_prob * likelihood_ratio + (1 - prior_prob)
    )
    return posterior

Sanction Type	Intensity Score	Typical Asset Shock	Expected Duration
Targeted sanctions on people / entities	1-2	<0.5%	Short-lived
Sector-level export controls	3-4	1-3%	Several months
SWIFT cutoff	7-8	5-15%	Long-lasting
Full-scale economic sanctions	9-10	10-30%	Structural
Oil embargo	8-9	Crude +10-30%	Medium-term

Asset	Hormuz	Russia-Ukraine	Red Sea	Notes
Brent crude	+++ shock	++ persistent	+ mild	Primary geopolitical-risk asset
WTI crude	++ shock	++ persistent	+ mild	Widens against Brent
Europe TTF gas	++	+++	+	Cost of replacing Russian gas
LNG futures	+++	++	++	Red Sea disruption matters for Asian LNG
Relevant ETFs	XLE, OIH, UNG

Gold (GLD/GC): geopolitical shock → immediate rally, but persistence depends on real-rate direction
Silver (SLV/SI): industrial exposure dilutes safe-haven behavior and raises volatility
Palladium / platinum: Russia is a major producer, so sanctions hit supply directly

Asset	Russia-Ukraine Conflict	South China Sea Blockade	Driver
Wheat (ZW)	+++	+	Russia + Ukraine account for about 30% of exports
Corn (ZC)	++	+	Ukraine is a major exporter
Sunflower oil	+++	-	Ukraine accounts for roughly 50% globally
Soybeans (ZS)	+	+	China import demand

Estimated impact under a Taiwan Strait crisis:
- Mild military tension (drills): SOX -5% to -10%
- Blockade drill (1 month): SOX -15% to -25%
- Actual military conflict: SOX -40% to -60% (no true historical analogue)

Beneficiaries through substitution:
- Intel (INTC): IDM model with U.S.-based capacity
- GlobalFoundries (GFS): U.S. / Europe / Singapore capacity
- Samsung, though Korea itself is also a geopolitical risk zone

Key ETFs and stocks:
- BDRY: bulk-shipping freight ETF tracking BDI, highly sensitive to Red Sea / Hormuz shocks
- ZIM: Israeli container shipper, directly exposed to Red Sea risk
- FRO (Frontline): tanker beneficiary of Hormuz risk
- STNG (Scorpio Tankers): benefits from rerouting around the Red Sea
- MAERSK.B: container-shipping leader that benefits from freight spikes during crises

U.S. defense ETFs: ITA (iShares), XAR (SPDR)

Single-stock beneficiaries of geopolitical risk:
- LMT (Lockheed Martin): F-35, missile systems
- RTX (Raytheon): air-defense systems such as Patriot
- NOC (Northrop Grumman): B-21 bomber, nuclear systems
- BA (Boeing): military exposure, though commercial aviation can be hurt by geopolitics

Historical pattern:
Higher geopolitical risk → faster defense budget approvals → effect shows up with a 6-12 month lag

Capital flows during crises:
Risk currencies (AUD/NZD/MXN/KRW/BRL) → outflows
Safe-haven currencies (JPY/CHF/USD) ← inflows

JPY:
- Net-creditor-nation status + repatriation effect
- Historical crisis moves: +1% to +3% vs USD

CHF:
- Neutral country + European financial center
- Major crises: +2% to +5% vs EUR

USD:
- Global reserve currency and final safe haven during crises
- But if the U.S. homeland is directly attacked, USD can weaken instead

Note: High-carry funding currencies such as TRY and ARS tend to suffer the most when global risk aversion rises

SIGNAL_LEVELS = {
    "GREEN": {
        "desc": "Normal geopolitical risk level",
        "gpr_percentile": (0, 50),
        "action": "Standard allocation, no special hedge required"
    },
    "YELLOW": {
        "desc": "Risk rising, watch for escalation",
        "gpr_percentile": (50, 75),
        "action": "Small long-gold position, reduce high-risk asset exposure by 10%"
    },
    "ORANGE": {
        "desc": "High-risk state, potential shock approaching",
        "gpr_percentile": (75, 90),
        "action": "Add safe-haven assets, buy OTM protective options, bullish on oil"
    },
    "RED": {
        "desc": "Extreme risk, crisis may break out",
        "gpr_percentile": (90, 100),
        "action": "Maximize defensive positioning, hold cash / gold / Treasuries, short high-risk assets"
    }
}

Diplomatic:
  [ ] Embassy closures / downgrades
  [ ] Diplomat expulsions
  [ ] UN emergency meeting called
  [ ] Escalation in joint statements by multiple countries

Military:
  [ ] Large-scale exercises (>50,000 personnel)
  [ ] Carrier strike group forward deployment
  [ ] Higher readiness announcements
  [ ] Missile / nuclear system release orders

Financial:
  [ ] Target-country CDS spread breaks historical highs
  [ ] Exchange rate devaluation >3% in one week
  [ ] Sharp decline in FX reserves
  [ ] Accelerating capital flight

def crisis_vol_strategy(underlying, option_chain):
    """Volatility trading framework during crises.

    Crisis outbreaks usually cause:
    1. A short-term VIX spike (long VIX futures / options)
    2. Inversion in the IV term structure (front month > back month)
    3. Steeper put skew

    Args:
        underlying: Underlying asset ticker
        option_chain: Option chain data

    Returns:
        dict: Recommended strategies and sizing guidance
    """
    strategies = {
        "long_vix_futures": {
            "instrument": "Front-month VX futures",
            "trigger": "VIX < 20 and GPR > 75th percentile",
            "target": "VIX spikes to 35-50",
            "stop": "VIX falls 15% below entry"
        },
        "backspread": {
            "instrument": f"Buy OTM Put + sell ATM Put on {underlying}",
            "trigger": "Implied volatility is at a historical low",
            "profit_zone": "Large drop > 10%"
        },
        "calendar_spread": {
            "instrument": "Sell near-month ATM + buy far-month ATM",
            "trigger": "Exit when term-structure inversion becomes excessive",
            "profit_zone": "Volatility mean reversion"
        }
    }
    return strategies

Crisis type        | Gold | Oil | Defense | JPY | Treasuries | EM
Energy conflict    | ++   | +++ | ++      | +   | +          | ---
Nuclear escalation | +++  | +   | +       | +++ | +++        | ---
Sanctions / trade  | +    | +   | +       | +   | +          | --
Food crisis        | +    | 0   | 0       | 0   | +          | -- (importers)
Sea blockade       | +    | ++  | +       | +   | +          | -

Event	S&P 500 Max Drawdown	Days to Recover Prior High	Max Oil Rally	Max Gold Rally
9/11 attacks (2001)	-11.6%	31 days	-35% (demand collapse)	+5%
Iraq War (2003)	-3%	<30 days	+40% (within 1 year)	+15%
Russia-Georgia War (2008)	<-5%	<30 days	Overlapped with financial crisis	+10%
Crimea (2014)	-1%	7 days	-5%	+3%
Full invasion of Ukraine (2022)	-3% briefly	<20 days	+40% (within 3 months)	+5%

1. The initial equity shock from geopolitical events usually recovers within 30 days unless recession hits simultaneously
2. Energy / commodities effects last longer because supply-side changes are structural
3. Go long the most damaged assets once the crisis de-escalates and mean reversion starts
4. Sell safe-haven assets that exploded during the crisis, especially gold after tension fades

REVERSION_SIGNALS = [
    "Ceasefire agreement signed / negotiations announced",
    "Energy / grain exports resume, confirmed by shipping data",
    "Target-country CDS spreads retrace >20% from the peak",
    "GPR Index falls >30% from the peak",
    "VIX drops below 20 after peaking",
    "Safe-haven currencies such as JPY / CHF begin weakening"
]

# Official download, free, monthly updates
GPR_DATA_URL = "https://www.matteoiacoviello.com/gpr_files/data_gpr_export.xls"

# High-frequency daily GPR based on Twitter / news
# Access request required: https://www.policyuncertainty.com/gpr_daily.html

# Related paper:
# Caldara & Iacoviello (2022), "Measuring Geopolitical Risk"
# American Economic Review, 112(4): 1194-1225

# GDELT 2.0 provides global news-event data updated every 15 minutes
# Includes the CAMEO event code system for military / diplomatic / conflict classification

def query_gdelt_events(country_code, event_type, start_date, end_date):
    """Query GDELT geopolitical event data.

    GDELT BigQuery table: gdelt-bq.gdeltv2.events
    CAMEO root codes: 14=protest, 18=assault, 19=fight, 20=mass violence

    Args:
        country_code: FIPS country code, e.g. 'CH' for China, 'RS' for Russia
        event_type: CAMEO root code
        start_date: Start date in YYYY-MM-DD
        end_date: End date in YYYY-MM-DD

    Returns:
        pd.DataFrame: Event records
    """
    from google.cloud import bigquery
    client = bigquery.Client()

    query = f"""
    SELECT SQLDATE, Actor1CountryCode, Actor2CountryCode,
           EventCode, GoldsteinScale, NumMentions, AvgTone
    FROM `gdelt-bq.gdeltv2.events`
    WHERE (Actor1CountryCode = '{country_code}'
           OR Actor2CountryCode = '{country_code}')
      AND EventRootCode = '{event_type}'
      AND SQLDATE BETWEEN '{start_date.replace('-','')}'
                      AND '{end_date.replace('-','')}'
    ORDER BY SQLDATE DESC
    """
    return client.query(query).to_dataframe()

# Armed Conflict Location & Event Data Project
# https://acleddata.com/
# Covers 100+ countries and is free for approved academic access

ACLED_API_BASE = "https://api.acleddata.com/acled/read"

def fetch_acled_events(country, start_date, end_date, api_key):
    """Fetch ACLED armed-conflict event data.

    Args:
        country: Country name in English
        start_date: Start date in YYYY-MM-DD
        end_date: End date in YYYY-MM-DD
        api_key: ACLED API key

    Returns:
        pd.DataFrame: Conflict event data
    """
    import requests
    import pandas as pd

    params = {
        "key": api_key,
        "email": "[email protected]",
        "country": country,
        "event_date": f"{start_date}|{end_date}",
        "event_date_where": "BETWEEN",
        "export_type": "json"
    }
    resp = requests.get(ACLED_API_BASE, params=params)
    return pd.DataFrame(resp.json()["data"])

# Option A: Use the Jina Reader API integrated in the project through read_url
def analyze_geopolitical_news(query: str) -> dict:
    """Read news through Jina and analyze geopolitical-risk sentiment.

    Use together with the agent's read_url tool.

    Args:
        query: Search keywords

    Returns:
        dict: Sentiment-analysis result
    """
    # Recommended news sources:
    news_sources = [
        "https://www.reuters.com/world/",
        "https://www.bloomberg.com/politics",
        "https://www.ft.com/world",
        "https://www.foreignpolicy.com/"
    ]
    # Use read_url to fetch content, then pass it to the LLM to extract risk events

# Option B: Event Registry API (paid, structured news)
# https://eventregistry.org/
# Supports filtering by country / topic / time and returns standardized events

# Option C: VADER / FinBERT sentiment analysis
# Score geopolitical news sentiment and build high-frequency signals

DATA_SOURCES = {
    "oil_tanker_tracking": {
        "desc": "Crude oil / LNG vessel AIS tracking",
        "source": "MarineTraffic API (paid) / VesselFinder (limited free)",
        "use_case": "Real-time monitoring of traffic through Hormuz / the Red Sea"
    },
    "un_vote_data": {
        "desc": "UN General Assembly / Security Council voting records",
        "source": "UN Data API (free)",
        "use_case": "Track changes in great-power alignment"
    },
    "arms_transfer": {
        "desc": "Arms transfers and military aid data",
        "source": "SIPRI Arms Transfers Database (free)",
        "use_case": "Estimate conflict-escalation probability"
    },
    "nuclear_risk": {
        "desc": "Real-time nuclear-risk assessment",
        "source": "Bulletin of the Atomic Scientists Doomsday Clock",
        "use_case": "Tail-risk monitoring"
    },
    "commodity_futures": {
        "desc": "Commodity futures prices, including geopolitical premium",
        "source": "Integrated in this project: Tushare commodity futures / OKX crypto",
        "use_case": "Estimate war premium"
    }
}

Run at the start of each month:
1. Download the latest GPR Index data
2. Calculate CDS spread changes for each hotspot country
3. Analyze tanker insurance rates
4. Summarize counts of high-intensity GDELT conflict events
5. Output a composite risk score (0-100) plus allocation guidance

Trigger:
Major geopolitical event breaks out, such as a missile strike or sanctions announcement

Execution flow:
1. Identify the event type and intensity (0-10)
2. Map the affected asset classes
3. Estimate the short-term price shock range
4. Identify hedging instruments (options / futures / ETFs)
5. Set stop-loss rules and position size

# Geopolitical scenario stress tests for a portfolio
SCENARIOS = {
    "hormuz_blockade_30d": {
        "oil_price_shock": +40,
        "gold_shock": +8,
        "equity_shock": -12,
        "usd_shock": +3,
        "description": "30-day Strait of Hormuz blockade scenario"
    },
    "taiwan_conflict_mild": {
        "semioconductor_shock": -25,
        "gold_shock": +5,
        "equity_shock": -15,
        "jpy_shock": +8,
        "description": "Mild Taiwan Strait military conflict scenario"
    },
    "russia_gas_cutoff": {
        "eu_natgas_shock": +80,
        "eu_equity_shock": -20,
        "eur_shock": -8,
        "gold_shock": +6,
        "description": "Russia fully cuts off gas to Europe"
    }
}

def portfolio_stress_test(portfolio_weights, scenarios=SCENARIOS):
    """Run geopolitical scenario stress tests on a portfolio.

    Args:
        portfolio_weights: dict mapping asset ticker to weight
        scenarios: Scenario-definition dictionary

    Returns:
        pd.DataFrame: Expected portfolio PnL under each scenario
    """
    results = {}
    for scenario_name, shocks in scenarios.items():
        portfolio_pnl = sum(
            portfolio_weights.get(asset, 0) * shock / 100
            for asset, shock in shocks.items()
            if asset != "description"
        )
        results[scenario_name] = {
            "portfolio_return": portfolio_pnl,
            "description": shocks["description"]
        }
    return results

# Strategy logic:
# When GPR > 75th percentile, hold 5% gold + 5% oil calls
# When GPR < 25th percentile, revert to standard allocation
# Historical backtests suggest a roughly 30-40% reduction in tail losses
# across major crises from 2001-2023

def gpr_dynamic_hedge_backtest(returns_data, gpr_data,
                                hedge_assets=["GLD", "USO"],
                                hedge_weight=0.05):
    """Backtest a GPR-driven dynamic hedge strategy.

    Args:
        returns_data: pd.DataFrame of daily asset returns
        gpr_data: pd.Series of monthly GPR Index values
        hedge_assets: List of hedge assets
        hedge_weight: Allocation weight per hedge asset

    Returns:
        pd.DataFrame: Return comparison before and after hedging
    """
    import pandas as pd

    # Map monthly GPR to daily frequency.
    gpr_daily = gpr_data.resample("D").ffill()
    gpr_threshold = gpr_daily.quantile(0.75)

    hedge_signal = gpr_daily > gpr_threshold

    base_return = returns_data.drop(columns=hedge_assets, errors="ignore").mean(axis=1)
    hedge_return = returns_data[hedge_assets].mean(axis=1) if hedge_assets else 0

    hedged_return = base_return.copy()
    hedged_return[hedge_signal] = (
        base_return[hedge_signal] * (1 - len(hedge_assets) * hedge_weight) +
        hedge_return[hedge_signal] * len(hedge_assets) * hedge_weight
    )

    return pd.DataFrame({
        "base": base_return,
        "hedged": hedged_return,
        "hedge_active": hedge_signal.astype(int)
    })

Academic papers:
- Caldara & Iacoviello (2022), "Measuring Geopolitical Risk", AER
- Apergis et al. (2021), "Geopolitical Risks and Asset Prices"
- Mueller & Rauh (2018), "The Hard Problem of Prediction for Conflict Prevention"

Data resources:
- GPR Index: https://www.matteoiacoviello.com/gpr.htm
- GDELT: https://www.gdeltproject.org/
- ACLED: https://acleddata.com/
- SIPRI: https://www.sipri.org/databases

Market-analysis tools:
- BDI (Baltic Dry Index): https://www.balticexchange.com/
- CDS spread data: Bloomberg / Refinitiv (paid) / FRED (partially free)
- Vessel AIS tracking: MarineTraffic.com

Geopolitical Risk | Skills Pool

Geopolitical Risk

Geopolitical Risk

Geopolitical Risk Analysis

Overview

Core Analytical Framework

1. Risk Layering Model

2. Five Dimensions of Risk Assessment

Monitoring the Six Major Global Geopolitical Hotspots

1. Strait of Hormuz — Oil Transport Chokepoint

2. Taiwan Strait — Core of the Semiconductor Supply Chain

3. Red Sea / Suez Canal — Europe-Asia Trade Artery

4. Russia-Ukraine Conflict — Energy and Food Security

5. South China Sea — Shipping Lanes and Rare-Earth Competition

6. Korean Peninsula — Regional Security Shock Source

Quantitative Framework for Geopolitical Risk

GPR Index (Caldara & Iacoviello)

Calculating War Risk Premiums

Supply-Chain Disruption Probability Assessment

Quantifying Sanctions Transmission Chains

Asset-Class Impact Mapping

Energy

Precious Metals (Safe Haven Function)

Agriculture

Semiconductors / Technology

Shipping / Logistics

Defense

FX (Safe-Haven Currencies)

Event-Driven Strategy Framework

Phase 1: Positioning Before the Crisis (Early-Warning Signal Detection)

Phase 2: Trading During the Crisis

Phase 3: Mean Reversion After the Crisis

Data Sources and APIs

1. GPR Index (Most Important Quantitative Dataset)

2. GDELT Global Event Database (Free)

3. ACLED Armed Conflict Location & Event Data

4. Real-Time News Sentiment Analysis

5. Other Practical Data Sources

Application Scenarios

Scenario 1: Geopolitical Risk Dashboard (Monthly Refresh)

Scenario 2: Rapid Event Shock Assessment

Scenario 3: Quarterly Risk Stress Testing

Scenario 4: Backtest of a GPR-Driven Dynamic Hedge

References and Further Reading

Llm Trading Agent Security

Energy Procurement

Council

Carrier Relationship Management

Market Research

Market Research