geopolitical-risk

# 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 | --- ## Monitoring the Six Major Global Geopolitical Hotspots ### 1. Strait of Hormuz — Oil Transport Chokepoint **Strategic significance** - Roughly 20% of global oil supply (about 17 million barrels/day) and 20% of LNG passes through it - Iran has the ability to disrupt the strait through mines, naval assets, and shore-based missiles - It is the only export route for Gulf states such as Saudi Arabia, the UAE, Kuwait, and Iraq **Risk triggers** - Escalation in U.S.-Iran tensions, such as failed nuclear talks or tighter sanctions - Tankers being seized or attacked - Iranian blockade drills during military exercises **Key monitoring indicators** ```python # 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 ``` **Asset impact direction** - Bullish: crude oil, LNG, shipping stocks (BDRY/FRO), defense stocks (LMT/RTX) - Bearish: airlines (DAL/UAL), petrochemical refiners, emerging-market importers such as INR and KRW --- ### 2. Taiwan Strait — Core of the Semiconductor Supply Chain **Strategic significance** - TSMC accounts for roughly 90% of global advanced-node capacity below 5nm - Taiwan produces about 65% of the world's semiconductors - It sits on the main southbound route linking Northeast Asia and Southeast Asia **Risk triggers** - Larger-scale Chinese military exercises, especially blockade drills - U.S. arms sales to Taiwan or high-level official visits - Major policy changes in cross-strait relations **Key monitoring indicators** ```python # 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 ``` **Asset impact direction** - Bullish: Intel / GlobalFoundries as substitute capacity providers, defense stocks, JPY as a haven - Bearish: Apple / NVIDIA / AMD / Qualcomm as TSMC clients, TSM ADR, Samsung Electronics - Extreme scenario: global semiconductor shortage leading to collapse across auto and consumer-electronics supply chains **Supply chain substitution timeline** ``` 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 ``` --- ### 3. Red Sea / Suez Canal — Europe-Asia Trade Artery **Strategic significance** - The Suez Canal carries about 12% of global trade volume and 30% of container shipping - The alternative route around the Cape of Good Hope adds 10-14 days and raises cost by 15-25% - Houthi forces in Yemen threaten the Bab el-Mandeb chokepoint **Risk triggers** (already validated by the 2024 Houthi attacks) - Intensified attacks on merchant vessels by Houthi forces - Israel-Gaza escalation spilling across the region - Political instability in Eritrea or Somalia **Key monitoring indicators** ```python # 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 ``` **Asset impact direction** - Bullish: shipping stocks (ZIM/MAERSK/COSCO), tankers rerouting around the Cape (FRO/STNG) - Bearish: European manufacturers facing supply-chain delays, inflation-sensitive sectors - Lag effect: higher freight rates → higher global CPI → tighter rate expectations --- ### 4. Russia-Ukraine Conflict — Energy and Food Security **Strategic significance** - Russia is the world's largest natural gas exporter and second-largest crude exporter - Ukraine is a major global grain exporter (wheat / corn / sunflower oil) - The war has already driven a permanent restructuring of Europe's energy mix **Ongoing risk points** - Escalation in nuclear rhetoric, a major tail-risk driver - Sanctions expanding to third parties, forcing countries like China and India to choose sides - Continued attacks on Ukrainian infrastructure such as the power grid and ports **Key monitoring indicators** ```python # 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 transmission-chain analysis** ``` 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 ``` --- ### 5. South China Sea — Shipping Lanes and Rare-Earth Competition **Strategic significance** - Around one-third of global trade value, roughly USD 3.4 trillion annually, passes through the South China Sea - China controls about 60% of global rare-earth supply, even more in refining - Territorial frictions between China and the Philippines / Vietnam persist **Risk triggers** - China declaring an Air Defense Identification Zone (ADIZ) - Clashes around flashpoints such as Sabina Shoal or Scarborough Shoal - Rare-earth export bans or quota cuts as a technology retaliation tool against the U.S. **Key monitoring indicators** ```python # 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 ``` **Asset impact direction** - Bullish: rare-earth miners such as MP Materials and Australia's Lynas, Japanese trading houses with inventories - Bearish: EV / permanent-magnet motor supply chains, Chinese ADRs --- ### 6. Korean Peninsula — Regional Security Shock Source **Strategic significance** - North Korea possesses nuclear weapons and ICBMs, making it a non-trivial tail risk - Strategic cooperation among China, Russia, and North Korea has deepened, including artillery supply during the Russia-Ukraine war - South Korea is a major global exporter of semiconductors, shipbuilding, and autos **Risk triggers** - Nuclear or missile tests, especially ICBM launches - North Korea announcing strategic changes such as "nuclear sharing" - Political crises in South Korea affecting U.S. force deployment **Key monitoring indicators** ```python # 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 ``` --- ## Quantitative Framework for Geopolitical Risk ### GPR Index (Caldara & Iacoviello) **Definition and source** - Built by Fed economists Dario Caldara and Matteo Iacoviello - Computed from war / terror / military-related word frequency in major newspapers globally - Monthly data back to 1900, covering global and country-specific series - Official data: https://www.matteoiacoviello.com/gpr.htm **Index taxonomy** ``` GPR: overall geopolitical risk GPRT: geopolitical threats (forward-looking) GPRA: geopolitical acts (events already realized) GPR_country: country-level sub-index ``` **Python example** ```python 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 ``` --- ### Calculating War Risk Premiums **Oil war premium** ```python 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)) ``` **Gold safe-haven premium** ```python 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 ``` --- ### Supply-Chain Disruption Probability Assessment **Bayesian update framework** ```python 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 ``` --- ### Quantifying Sanctions Transmission Chains **Sanctions intensity scorecard** | 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-Class Impact Mapping ### Energy | 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 | | | | ### Precious Metals (Safe Haven Function) ``` 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 ``` **Empirical patterns (2001-2024)** - A 1-standard-deviation rise in GPR implies about +1.2% expected gold return over a 1-month window - On day one of major shocks such as Pearl Harbor, 9/11, or Russia-Ukraine, gold rose roughly 3-8% - Within 60 days, around 50-70% of the geopolitical premium mean-reverts ### Agriculture | 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 | ### Semiconductors / Technology ``` 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 ``` ### Shipping / Logistics ``` 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 ``` ### Defense ``` 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 ``` ### FX (Safe-Haven Currencies) ``` 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