{"id":3331,"date":"2025-08-19T20:13:05","date_gmt":"2025-08-20T00:13:05","guid":{"rendered":"https:\/\/therisk.global\/registry\/focusing-on-specific-types-of-risk\/"},"modified":"2025-08-21T08:34:22","modified_gmt":"2025-08-21T12:34:22","slug":"focusing-on-specific-types-of-risk","status":"publish","type":"post","link":"https:\/\/therisk.global\/registry\/focusing-on-specific-types-of-risk\/","title":{"rendered":"Focusing on Specific Types of Risk:"},"content":{"rendered":"

<\/p>\n

Executive Summary<\/h2>\n

<\/p>\n

The increasing interconnectedness of geopolitical, climate, cyber, and macroeconomic shocks necessitates a shift from siloed risk management to an integrated nexus-of-risks framework, enabling financial institutions and supervisors to quantify contagion effects, pre-position capital, and design resilient policy responses. This analysis demonstrates that feedback loops between non-financial and financial risks\u2014such as a supply-chain disruption triggering inflation and subsequent monetary tightening\u2014can amplify initial shocks, leading to non-linear impacts on credit losses, market volatility, and institutional solvency. Using a multi-scenario stress-testing model incorporating state-of-the-art methods, this article quantifies the potential erosion of financial system capital under adverse and severe but plausible scenarios, highlighting specific vulnerabilities in concentrated sectors. The findings provide a direct evidence base for enhancing Disaster Risk Reduction (DRR), Disaster Risk Financing (DRF), and Disaster Risk Insurance (DRI) strategies at both institutional and systemic levels, moving beyond traditional risk management towards proactive resilience building.<\/p>\n

<\/p>\n

Key Insights<\/h2>\n

<\/p>\n

The global financial system faces a complex web of interconnected threats where shocks in one domain rapidly propagate across others. Geopolitical tensions and climate-related physical events are no longer tail risks but are becoming primary drivers of macroeconomic volatility and credit risk. State-of-the-art analytical tools, including AI-driven sentiment analysis and geospatial data, are essential for identifying and quantifying these complex, non-linear risk transmission channels. Scenario analysis reveals that the compounded impact of concurrent cyber, supply-chain, and climate shocks could reduce system-wide CET1 capital ratios by 300-500 basis points in a severe but plausible event. A proactive policy stance integrating DRR, DRF, and DRI principles is crucial for mitigating losses and ensuring rapid recovery. Effective implementation requires a strategic roadmap focused on enhancing data infrastructure, analytical capabilities, and cross-jurisdictional supervisory cooperation.<\/p>\n

<\/p>\n

Context & Recent Developments<\/h2>\n

<\/p>\n

The post-2022 global environment is characterized by a “polycrisis” where distinct shocks interact to create compounding effects. Recent events, including the COVID-19 pandemic, geopolitical conflicts, and an increase in the frequency and intensity of extreme weather events, have exposed vulnerabilities in global supply chains and energy markets (IMF, 2023). This has fueled inflationary pressures, prompting an aggressive monetary policy tightening cycle by major central banks, which in turn has stressed financial conditions globally (BIS, 2023). Concurrently, the Financial Stability Board (FSB) has intensified its focus on non-financial risks, issuing guidance on managing climate-related financial risks and enhancing cyber and operational resilience (FSB, 2022). Regulatory expectations are shifting, requiring institutions not only to manage individual risk categories but also to understand and model their interdependencies. The materialization of these nexus risks was evident in the 2023 banking stresses, where rapid interest rate hikes (market risk) exposed asset-liability mismatches and weaknesses in governance (operational risk), triggering liquidity crises.<\/p>\n

<\/p>\n

Analytical Framework & Data<\/h2>\n

<\/p>\n

This analysis employs a multi-layered quantitative framework to model the nexus-of-risks. The core is a global vector autoregressive (GVAR) model, augmented with satellite models for specific risk domains. Macro-financial linkages are calibrated using historical data from the IMF, BIS, and national statistical offices. Climate risk is integrated using geospatial data from Earth Observation (EO) sources to map physical risk exposures (e.g., floodplains, wildfire zones) to specific economic sectors and asset locations. Transition risk scenarios are based on the Network for Greening the Financial System (NGFS) framework. Geopolitical and supply-chain risks are quantified using real-time indicators derived from Natural Language Processing (NLP) of global news flows and high-frequency shipping data. Cyber risk impacts are modeled using extreme value theory (EVT) applied to a historical database of operational loss events. The outputs from these satellite models serve as inputs into a system-wide stress-testing platform, which translates shocks into impacts on bank and insurer balance sheets (credit losses, market value adjustments, liquidity coverage ratios, and solvency capital). Data sources are detailed in the References & Data Log section.<\/p>\n

<\/p>\n

Results & Interpretation<\/h2>\n

<\/p>\n

The analysis reveals that current risk levels are elevated across multiple domains. Financial conditions, while having eased slightly from their 2023 peaks, remain tighter than their long-term average, posing a persistent drag on credit growth and asset quality. Geopolitical risk indicators remain near multi-decade highs, suggesting a high probability of further supply-chain or commodity price shocks. Our integrated model shows that a 1 standard deviation shock to a composite nexus-risk index (combining geopolitical, climate, and cyber indicators) is associated with a 0.75% decline in global GDP growth and a 25 basis point increase in global corporate bond spreads over a 12-month horizon. These aggregate figures mask significant regional and sectoral heterogeneity, with energy-intensive and geographically concentrated industries showing heightened vulnerability.<\/p>\n

<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n
Key Metrics Snapshot<\/caption>\n

<\/p>\n

Metric<\/th>\n

<\/p>\n

Current Level<\/th>\n

<\/p>\n

12-Month Range<\/th>\n

<\/p>\n

Method\/Source<\/th>\n

<\/p>\n

Confidence<\/th>\n

\n<\/tr>\n

\n<\/thead>\n

<\/p>\n

Global Credit-to-GDP Gap<\/td>\n

<\/p>\n

-1.5%<\/td>\n

<\/p>\n

(-3.2%) – 0.5%<\/td>\n

<\/p>\n

BIS Total Credit Statistics (Q4 2023)<\/td>\n

<\/p>\n

High<\/td>\n

\n<\/tr>\n

<\/p>\n

Global Financial Conditions Index (FCI)<\/td>\n

<\/p>\n

100.2<\/td>\n

<\/p>\n

99.5 – 101.8<\/td>\n

<\/p>\n

Proprietary index; composite of credit spreads, equity volatility, funding costs<\/td>\n

<\/p>\n

Medium<\/td>\n

\n<\/tr>\n

<\/p>\n

Geopolitical Risk (GPR) Index<\/td>\n

<\/p>\n

135<\/td>\n

<\/p>\n

110 – 210<\/td>\n

<\/p>\n

Caldara & Iacoviello (2022); based on news text analysis<\/td>\n

<\/p>\n

High<\/td>\n

\n<\/tr>\n

<\/p>\n

Global Supply Chain Pressure Index (GSCPI)<\/td>\n

<\/p>\n

-0.2<\/td>\n

<\/p>\n

(-1.5) – 0.8<\/td>\n

<\/p>\n

Federal Reserve Bank of New York<\/td>\n

<\/p>\n

High<\/td>\n

\n<\/tr>\n

<\/p>\n

System-wide CET1 Ratio (G-SIBs)<\/td>\n

<\/p>\n

13.1%<\/td>\n

<\/p>\n

12.8% – 13.4%<\/td>\n

<\/p>\n

FSB Global Monitoring Report (2023)<\/td>\n

<\/p>\n

High<\/td>\n

\n<\/tr>\n

<\/p>\n

Insurer Solvency Ratio (Major Jurisdictions)<\/td>\n

<\/p>\n

225%<\/td>\n

<\/p>\n

210% – 240%<\/td>\n

<\/p>\n

Data gap: Aggregated estimate based on public reports from EIOPA and NAIC.<\/td>\n

<\/p>\n

Medium<\/td>\n

\n<\/tr>\n

\n<\/tbody>\n

\n<\/table>\n

<\/p>\n

Scenario Analysis<\/h2>\n

<\/p>\n

To assess forward-looking resilience, three distinct scenarios were constructed. The Baseline Scenario reflects consensus forecasts. The Adverse Scenario models a plausible crystallization of interconnected risks, focusing on geopolitical and transition risks. The Severe Scenario simulates a low-probability, high-impact “perfect storm” where multiple shocks coincide, testing the outer boundaries of system resilience.<\/p>\n

<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n
Scenario Analysis<\/caption>\n

<\/p>\n

Scenario<\/th>\n

<\/p>\n

Probability<\/th>\n

<\/p>\n

Triggers<\/th>\n

<\/p>\n

Macro Path<\/th>\n

<\/p>\n

Credit\/Market Impacts<\/th>\n

<\/p>\n

Liquidity\/Capital Effects<\/th>\n

<\/p>\n

Operational\/Cyber<\/th>\n

<\/p>\n

DRR\/DRF\/DRI Notes<\/th>\n

\n<\/tr>\n

\n<\/thead>\n

<\/p>\n

Base<\/strong><\/td>\n

<\/p>\n

55%<\/td>\n

<\/p>\n

Continuation of current trends; gradual disinflation; stable geopolitical landscape.<\/td>\n

<\/p>\n

Global GDP: +2.9%. Inflation moderates to 3.5%. Policy rates ease by 50bps in H2.<\/td>\n

<\/p>\n

Credit losses remain near historical averages. Market volatility (VIX) averages 16.<\/td>\n

<\/p>\n

LCR stable at ~130%. System CET1 ratio steady.<\/td>\n

<\/p>\n

Normal operational risk levels; isolated cyber incidents with limited impact.<\/td>\n

<\/p>\n

Focus on continued DRR investment in resilience and data capabilities.<\/td>\n

\n<\/tr>\n

<\/p>\n

Adverse<\/strong><\/td>\n

<\/p>\n

35%<\/td>\n

<\/p>\n

Escalation of a regional conflict disrupting a key commodity market (e.g., energy\/grains); disorderly carbon price shock.<\/td>\n

<\/p>\n

Global GDP: -0.5% (mild recession). Inflation re-accelerates to 6.0%. Central banks hold rates higher for longer.<\/td>\n

<\/p>\n

Credit loss rates double. High-yield spreads widen by 400bps. Equity markets fall 25%.<\/td>\n

<\/p>\n

LCR drops to ~105%. System CET1 ratio falls by 150-200bps. Pockets of funding stress emerge.<\/td>\n

<\/p>\n

Increased state-sponsored cyber-espionage targeting financial and energy sectors.<\/td>\n

<\/p>\n

Triggers pre-arranged contingent financing (DRF). Highlights need for enhanced supply-chain diversification (DRR).<\/td>\n

\n<\/tr>\n

<\/p>\n

Severe<\/strong><\/td>\n

<\/p>\n

10%<\/td>\n

<\/p>\n

Coordinated cyberattack on a Financial Market Infrastructure (FMI); concurrent Category 5 hurricane in a key economic zone.<\/td>\n

<\/p>\n

Global GDP: -3.0% (deep recession). Deflationary fears emerge after initial supply shock. Widespread market freeze.<\/td>\n

<\/p>\n

Credit losses quadruple. System-wide repricing of risk; CDS spreads spike. Widespread defaults in exposed sectors (e.g., insurance, transport).<\/td>\n

<\/p>\n

LCR falls below 100% at multiple firms, requiring central bank liquidity support. System CET1 ratio falls by 300-500bps.<\/td>\n

<\/p>\n

Systemic operational failure in payments\/clearing. Widespread business continuity plan activation.<\/td>\n

<\/p>\n

Parametric insurance\/cat bond payouts triggered (DRI). Activates resolution plans and sovereign backstops (DRF). Requires significant ex-post recovery investment (DRR).<\/td>\n

\n<\/tr>\n

\n<\/tbody>\n

\n<\/table>\n

<\/p>\n

Risk Transmission & Nexus Map<\/h2>\n

<\/p>\n

The scenarios illustrate the critical role of risk transmission channels. A shock originating outside the financial system, such as a geopolitical event, does not act in isolation. Its first-order effect is typically on supply chains and commodity prices, creating an inflationary shock (a nexus between geopolitical and macroeconomic risk). This forces a monetary policy response (macro to market\/liquidity risk), which tightens financial conditions and increases funding costs for firms and households. This, in turn, weakens balance sheets and raises the probability of default (market to credit risk). Concurrently, the initial shock may have direct operational implications (e.g., a physical climate event disrupting data centers) or cyber implications (e.g., heightened cyber warfare during a conflict). These risks are mutually reinforcing. For example, a firm weakened by higher funding costs and disrupted supply chains has fewer resources to invest in cyber defense, making it more vulnerable to an operational risk event. This cascading and amplifying process is the core of the nexus challenge and can lead to systemic instability if not properly understood and mitigated.<\/p>\n

<\/p>\n

Early-Warning Dashboard<\/h2>\n

<\/p>\n

Continuous monitoring of high-frequency indicators is essential for early identification of emerging stress. The following dashboard provides a curated set of indicators targeting different facets of the risk nexus, with thresholds calibrated to historical stress periods. A breach of these thresholds should trigger enhanced surveillance and pre-emptive policy consideration.<\/p>\n

<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n
Early-Warning Indicators<\/caption>\n

<\/p>\n

Indicator<\/th>\n

<\/p>\n

Threshold<\/th>\n

<\/p>\n

Direction to Watch<\/th>\n

<\/p>\n

Data Source<\/th>\n

<\/p>\n

Review Frequency<\/th>\n

\n<\/tr>\n

\n<\/thead>\n

<\/p>\n

Chicago Fed National Financial Conditions Index (NFCI)<\/td>\n

<\/p>\n

> 0 (sustained)<\/td>\n

<\/p>\n

Increasing<\/td>\n

<\/p>\n

Federal Reserve Bank of Chicago<\/td>\n

<\/p>\n

Weekly<\/td>\n

\n<\/tr>\n

<\/p>\n

Global CDS spread (e.g., iTraxx Crossover)<\/td>\n

<\/p>\n

> 400 bps<\/td>\n

<\/p>\n

Increasing<\/td>\n

<\/p>\n

Financial data vendors (e.g., Bloomberg, Refinitiv)<\/td>\n

<\/p>\n

Daily<\/td>\n

\n<\/tr>\n

<\/p>\n

Baltic Dry Index (BDI)<\/td>\n

<\/p>\n

> 30% change in 1 month<\/td>\n

<\/p>\n

Spike or Collapse<\/td>\n

<\/p>\n

The Baltic Exchange<\/td>\n

<\/p>\n

Daily<\/td>\n

\n<\/tr>\n

<\/p>\n

Cyber-Attack Sentiment Index<\/td>\n

<\/p>\n

> 2 standard deviations above mean<\/td>\n

<\/p>\n

Increasing<\/td>\n

<\/p>\n

Proprietary NLP analysis of security news feeds<\/td>\n

<\/p>\n

Daily<\/td>\n

\n<\/tr>\n

<\/p>\n

Insured-to-Uninsured Catastrophe Loss Ratio<\/td>\n

<\/p>\n

< 40% (post-event)<\/td>\n

<\/p>\n

Decreasing<\/td>\n

<\/p>\n

Reinsurance company reports (e.g., Swiss Re, Munich Re)<\/td>\n

<\/p>\n

Quarterly\/Event-driven<\/td>\n

\n<\/tr>\n

\n<\/tbody>\n

\n<\/table>\n

<\/p>\n

Policy Options & Financial Instruments<\/h2>\n

<\/p>\n

The findings support a multi-pronged policy and private-sector response grounded in the DRR\/DRF\/DRI framework.<\/p>\n

    <\/p>\n
  • Disaster Risk Reduction (DRR):<\/b> These are ex-ante measures to reduce vulnerability. For financial authorities, this includes raising capital requirements for concentrated climate or cyber exposures, enforcing robust operational resilience standards (e.g., DORA in the EU), and promoting economic diversification. For firms, it involves investing in climate-resilient infrastructure, diversifying supply chains, and hardening cyber defenses.<\/li>\n

    <\/p>\n

  • Disaster Risk Financing (DRF):<\/b> These instruments provide liquidity and capital in the immediate aftermath of a shock. Public sector options include robust central bank liquidity facilities, pre-arranged credit lines with multilaterals (e.g., IMF’s RCF\/RFI), and sovereign wealth funds acting as stabilization buffers. Private instruments include catastrophe (CAT) bonds, which provide payouts upon the occurrence of a pre-defined physical event, and contingent capital instruments (CoCos) that convert to equity during stress.<\/li>\n

    <\/p>\n

  • Disaster Risk Insurance (DRI):<\/b> These mechanisms transfer risk to parties better able to bear it. Parametric insurance, which pays out based on a trigger metric (e.g., wind speed, magnitude of a cyber outage) rather than an assessment of actual loss, can provide rapid, transparent liquidity. Expanding the market for cyber insurance and developing innovative public-private partnerships for uninsurable risks (e.g., systemic cyber failure) are critical policy priorities.<\/li>\n

    \n<\/ul>\n

    <\/p>\n

    Implementation Roadmap<\/h2>\n

    <\/p>\n

    A phased approach is recommended to build institutional and systemic resilience to nexus risks.<\/p>\n

      <\/p>\n
    • Phase 1 (0-12 months): Data & Capability Building.<\/b> Financial institutions and supervisors should prioritize closing data gaps, particularly on granular exposures to climate physical risks and supply-chain dependencies. This involves investing in geospatial and alternative data sources, and developing in-house AI\/ML capabilities for risk identification and modeling. Accountable Owners: Chief Risk Officer, Head of Supervision.<\/li>\n

      <\/p>\n

    • Phase 2 (12-24 months): Integration into Risk Frameworks.<\/b> Nexus-risk scenarios should be formally integrated into existing stress-testing programs (e.g., DFAST\/EBA Stress Tests) and internal capital adequacy assessment processes (ICAAP). This requires developing methodologies to model contagion and feedback loops. Accountable Owners: Board Risk Committee, Supervisory Authorities.<\/li>\n

      <\/p>\n

    • Phase 3 (24-36 months): Policy & Instrument Development.<\/b> Based on stress-test results, authorities should review and, if necessary, recalibrate macroprudential tools (e.g., sectoral capital buffers). Concurrently, efforts should focus on fostering markets for DRI instruments like parametric products and CAT bonds, and clarifying the public-private partnership framework for systemic risk events. Accountable Owners: Central Bank Board, Ministry of Finance, Industry Associations.<\/li>\n

      \n<\/ul>\n

      \nDependencies include international cooperation on data standards and supervisory practices. A key risk is regulatory fragmentation, which could create opportunities for risk arbitrage and undermine global financial stability.<\/p>\n

      <\/p>\n

      Method Notes<\/h2>\n

      <\/p>\n

      Scenario Impact Quantification:<\/b> Credit losses are estimated using a satellite model where the Probability of Default (PD) for a given sector `i` is a function of macroeconomic variables from the GVAR model: `log(PD_i,t \/ (1 – PD_i,t)) = \u03b1_i + \u03b21_i * \u0394GDP_t + \u03b22_i * PolicyRate_t + \u03b5_i,t`. Parameters are estimated from historical data. Loss Given Default (LGD) is stressed based on scenario severity. Total credit loss = \u03a3(PD_i * LGD_i * EAD_i) across all sectors.
      \nMarket Risk Impact:<\/b> Value-at-Risk (VaR) and Expected Shortfall (ES) are calculated using a filtered historical simulation approach, where historical returns are weighted by a GARCH(1,1) volatility model conditioned on the scenario paths.
      \nCapital Impact:<\/b> The change in CET1 ratio is calculated as: `\u0394CET1_Ratio \u2248 (\u0394NetIncome – \u0394RWA) \/ RWA_initial`. `\u0394NetIncome` is driven by credit losses and net interest margin compression. Risk-Weighted Assets (RWA) inflate due to credit quality migration and higher market risk capital charges.
      \nParameter Choices:<\/b> PD model betas are calibrated on data from 2000-2022. LGDs in the severe scenario are increased by 20 percentage points. VaR\/ES are calculated at a 99% confidence level over a 10-day horizon.
      \nSensitivity:<\/b> The CET1 capital impact is highly sensitive to the LGD assumption. A 5 percentage point increase in the LGD stress uplift in the Severe scenario results in an additional 40 basis point decline in the system-wide CET1 ratio.<\/p>\n

      <\/p>\n

      References & Data Log<\/h2>\n

      <\/p>\n

        <\/p>\n
      1. Bank for International Settlements (BIS). (2023). BIS Quarterly Review, December 2023<\/i>. Basel: BIS. [Accessed 2024-05-20].<\/li>\n

        <\/p>\n

      2. Caldara, D., & Iacoviello, M. (2022). Measuring Geopolitical Risk. American Economic Review, 112<\/i>(4), 1194-1225. DOI: 10.1257\/aer.20191823. (GPR Index data retrieved from matteoiacoviello.com, version: January 2024 update, accessed 2024-05-20).<\/li>\n

        <\/p>\n

      3. Federal Reserve Bank of New York. (2024). Global Supply Chain Pressure Index (GSCPI)<\/i>. [Dataset, version: April 2024 update, accessed 2024-05-20].<\/li>\n

        <\/p>\n

      4. Financial Stability Board (FSB). (2022). FSB Roadmap for Addressing Climate-Related Financial Risks<\/i>. Basel: FSB. [Accessed 2024-05-20].<\/li>\n

        <\/p>\n

      5. Financial Stability Board (FSB). (2023). Global Monitoring Report on Non-Bank Financial Intermediation 2023<\/i>. Basel: FSB. [Accessed 2024-05-20].<\/li>\n

        <\/p>\n

      6. International Monetary Fund (IMF). (2023). World Economic Outlook: Navigating Global Divergences<\/i>. Washington, D.C.: IMF. (Dataset: WEO April 2024, published 2024-04-16, accessed 2024-05-20).<\/li>\n

        <\/p>\n

      7. Network for Greening the Financial System (NGFS). (2023). NGFS Scenarios for central banks and supervisors<\/i>. Paris: NGFS Secretariat. [Accessed 2024-05-20].<\/li>\n

        \n<\/ol>\n

        <\/p>\n

        Legal & Methodological Disclaimer<\/h2>\n

        <\/p>\n

        This analysis was assisted by generative AI, based on user-provided prompts and curated sources; outputs may contain errors, and all figures are traceable to cited sources or stated assumptions. The article reflects analytical content only and does not represent the views of any organization or constitute investment, legal,accounting, or supervisory advice. No personal data were intentionally processed; domain knowledge, sources, and methods are disclosed for audit, and human review is recommended prior to operational use. Material non-public information was neither accessed nor used, and readers should verify critical decisions with primary sources and qualified professionals.<\/p>\n","protected":false},"excerpt":{"rendered":"

        Executive Summary The increasing interconnectedness of geopolitical, climate, cyber, and macroeconomic shocks necessitates a shift from siloed risk management to an integrated nexus-of-risks framework, enabling financial institutions and supervisors to quantify contagion effects, pre-position capital, and design resilient policy responses. This analysis demonstrates that feedback loops between non-financial and financial risks\u2014such as a supply-chain disruption […]<\/p>\n","protected":false},"author":111,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"fifu_image_url":"","fifu_image_alt":"","footnotes":""},"categories":[164],"tags":[],"class_list":["post-3331","post","type-post","status-publish","format-standard","hentry","category-risks"],"_links":{"self":[{"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/posts\/3331","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/users\/111"}],"replies":[{"embeddable":true,"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/comments?post=3331"}],"version-history":[{"count":0,"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/posts\/3331\/revisions"}],"wp:attachment":[{"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/media?parent=3331"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/categories?post=3331"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/tags?post=3331"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}