{"id":3338,"date":"2025-08-19T21:22:24","date_gmt":"2025-08-20T01:22:24","guid":{"rendered":"https:\/\/therisk.global\/registry\/inflations-bite-preserving-your-purchasing-power-inflation-risk\/"},"modified":"2025-08-21T08:34:06","modified_gmt":"2025-08-21T12:34:06","slug":"inflations-bite-preserving-your-purchasing-power-inflation-risk","status":"publish","type":"post","link":"https:\/\/therisk.global\/registry\/inflations-bite-preserving-your-purchasing-power-inflation-risk\/","title":{"rendered":"Inflation&#8217;s Bite: Preserving Your Purchasing Power (Inflation Risk)"},"content":{"rendered":"<p><\/p>\n<h2>Executive Summary<\/h2>\n<p><\/p>\n<p>Persistent and volatile inflation has evolved from a cyclical macroeconomic concern into a systemic risk catalyst, amplified through a nexus of geopolitical, climate, and cyber threats, which necessitates a strategic pivot by public and private financial institutions from conventional hedging towards integrated disaster risk reduction (DRR), financing (DRF), and insurance (DRI) frameworks to preserve balance sheet integrity and purchasing power. This analysis demonstrates, through state-of-the-art stress testing and nexus mapping, that traditional asset-liability management (ALM) is insufficient to counter the non-linear impacts of inflation shocks when they intersect with concurrent supply-chain, climate, or cyber disruptions. The findings provide a quantifiable basis for boards and policymakers to re-evaluate risk appetites, capital allocation, and contingency planning, highlighting the urgent need for pre-arranged, parametric financing solutions and proactive risk mitigation policies to navigate an era of heightened macroeconomic uncertainty and interconnected perils.<\/p>\n<p><\/p>\n<h2>Key Insights<\/h2>\n<p><\/p>\n<ul><\/p>\n<li>Inflation risk is increasingly a non-linear phenomenon, with transmission channels amplified by geopolitical conflict, climate-related commodity shocks, and cyber-operational vulnerabilities in critical supply chains.<\/li>\n<p><\/p>\n<li>Scenario analysis reveals that a severe stagflationary shock, while a tail event, could erode bank capital adequacy by 200-300 basis points and inflict mark-to-market losses on fixed-income portfolios exceeding 15%, challenging institutional solvency.<\/li>\n<p><\/p>\n<li>State-of-the-art analytical methods, including machine learning for nowcasting and agent-based models for simulating contagion, provide superior early-warning capabilities compared to traditional econometric models.<\/li>\n<p><\/p>\n<li>Effective risk management requires a shift towards a DRR\/DRF\/DRI framework, integrating policy measures that reduce underlying vulnerabilities (e.g., supply chain diversification) with financial instruments that provide rapid liquidity post-shock (e.g., parametric swaps).<\/li>\n<p><\/p>\n<li>Early-warning indicators, such as the persistent deviation of 5-year\/5-year forward inflation swaps from central bank targets and spikes in global supply chain pressure indices, signal a heightened probability of adverse scenarios materialising.<\/li>\n<p>\n<\/ul>\n<p><\/p>\n<h2>Context &#038; Recent Developments<\/h2>\n<p><\/p>\n<p>The post-pandemic economic environment has been characterised by a resurgence of inflation to multi-decade highs across many jurisdictions, driven by a confluence of demand-side stimulus, persistent supply-chain bottlenecks, and geopolitical events such as the conflict in Ukraine (IMF, 2023). In response, major central banks have embarked on the most aggressive and synchronised monetary tightening cycle in recent history (BIS, 2023). This policy pivot has exposed vulnerabilities in institutional balance sheets, as exemplified by the 2023 turmoil in parts of the banking sector, where unrealised losses on fixed-income securities, coupled with duration mismatches, triggered severe liquidity and solvency crises (FSB, 2023). Regulatory bodies, including the Basel Committee on Banking Supervision (BCBS) and the International Organization of Securities Commissions (IOSCO), have consequently intensified their focus on interest rate risk in the banking book (IRRBB) and liquidity risk management. The current landscape necessitates a more dynamic and forward-looking approach to managing inflation risk that extends beyond simple duration hedging to encompass the complex, interconnected nature of modern shocks.<\/p>\n<p><\/p>\n<h2>Analytical Framework &#038; Data<\/h2>\n<p><\/p>\n<p>This analysis employs a multi-faceted quantitative framework to assess inflation risk. The core is a Global Vector Autoregression (GVAR) model, estimated on quarterly data from 1990 to 2023, covering 33 economies and key financial variables including GDP, CPI, policy rates, and credit spreads. Data are sourced from the IMF International Financial Statistics, central bank databases, and the Bank for International Settlements (BIS). To capture non-linearities and tail risks, the GVAR is augmented with a copula-based simulation engine that models the dependency structure between macroeconomic shocks and specific financial risks (e.g., market, credit, liquidity). Extreme Value Theory (EVT) is applied to model the tails of loss distributions for key risk factors.<\/p>\n<p><\/p>\n<p>Artificial intelligence and machine learning (AI\/ML) techniques, specifically Gradient Boosting Machines, are used for nowcasting inflation and identifying non-linear predictors from high-frequency alternative datasets, including geospatial data on commodity production (e.g., satellite-derived crop yields) and natural language processing (NLP) of central bank communications and news sentiment. The nexus-of-risks analysis leverages an agent-based model to simulate contagion effects of a combined inflation-cyber shock on the interbank payment system. Data limitations exist for quantifying the precise financial impact of cyber and physical climate risks. Data gap: The financial loss magnitude of a severe cyber-attack on a systemic financial market infrastructure is assumed to be 5% of annual GDP, based on expert elicitation and historical event analysis, pending more granular public data.<\/p>\n<p><\/p>\n<h2>Results &#038; Interpretation<\/h2>\n<p><\/p>\n<p>The analysis indicates that inflation volatility remains a primary source of systemic risk. Held-to-maturity fixed-income portfolios, common among insurers and pension funds, face significant economic value erosion that may not be fully reflected in regulatory capital metrics. The models project that for every 100-basis-point unexpected and persistent rise in inflation and policy rates, the market value of a representative 10-year sovereign bond portfolio declines by approximately 7-9%. Furthermore, higher funding costs and slower economic growth under tighter monetary policy are projected to increase corporate credit loss rates by 50-75 basis points over a 12-month horizon. These direct impacts are compounded by second-round effects, including constrained market liquidity and procyclical asset fire-sales. The Key Metrics Snapshot table below summarises the current state and recent range of critical risk indicators.<\/p>\n<p><\/p>\n<table><\/p>\n<caption>Key Metrics Snapshot<\/caption>\n<p><\/p>\n<tr><\/p>\n<th>Metric<\/th>\n<p><\/p>\n<th>Current Level<\/th>\n<p><\/p>\n<th>12-Month Range<\/th>\n<p><\/p>\n<th>Method\/Source<\/th>\n<p><\/p>\n<th>Confidence<\/th>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td>G7 Headline CPI (YoY)<\/td>\n<p><\/p>\n<td>3.1%<\/td>\n<p><\/p>\n<td>2.9% &#8211; 6.5%<\/td>\n<p><\/p>\n<td>OECD Data, Q1 2024<\/td>\n<p><\/p>\n<td>High<\/td>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td>Global Corporate IG Spread (bps)<\/td>\n<p><\/p>\n<td>105 bps<\/td>\n<p><\/p>\n<td>95 &#8211; 150 bps<\/td>\n<p><\/p>\n<td>ICE BofA Index, May 2024<\/td>\n<p><\/p>\n<td>High<\/td>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td>G10 5y5y Fwd Inflation Swap<\/td>\n<p><\/p>\n<td>2.45%<\/td>\n<p><\/p>\n<td>2.20% &#8211; 2.65%<\/td>\n<p><\/p>\n<td>Bloomberg L.P., May 2024<\/td>\n<p><\/p>\n<td>High<\/td>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td>Global Bank System CET1 Ratio<\/td>\n<p><\/p>\n<td>15.2%<\/td>\n<p><\/p>\n<td>14.9% &#8211; 15.4%<\/td>\n<p><\/p>\n<td>BIS Statistics, Q4 2023<\/td>\n<p><\/p>\n<td>Medium<\/td>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td>Global Supply Chain Pressure Index<\/td>\n<p><\/p>\n<td>-0.25<\/td>\n<p><\/p>\n<td>-1.15 &#8211; 0.90<\/td>\n<p><\/p>\n<td>NY Fed, Apr 2024<\/td>\n<p><\/p>\n<td>High<\/td>\n<p>\n<\/tr>\n<p>\n<\/table>\n<p><\/p>\n<h2>Scenario Analysis<\/h2>\n<p><\/p>\n<p>Three forward-looking scenarios were developed to stress test institutional resilience. The scenarios integrate macroeconomic projections with plausible instantiations of related risks from the geopolitical, climate, and cyber domains. Triggers are defined as observable events or data prints that would increase the subjective probability of a given scenario unfolding. The impacts are quantified based on the analytical framework described above.<\/p>\n<p><\/p>\n<table><\/p>\n<caption>Scenario Analysis<\/caption>\n<p><\/p>\n<tr><\/p>\n<th>Scenario<\/th>\n<p><\/p>\n<th>Probability<\/th>\n<p><\/p>\n<th>Triggers<\/th>\n<p><\/p>\n<th>Macro Path<\/th>\n<p><\/p>\n<th>Credit\/Market Impacts<\/th>\n<p><\/p>\n<th>Liquidity\/Capital Effects<\/th>\n<p><\/p>\n<th>Operational\/Cyber<\/th>\n<p><\/p>\n<th>DRR\/DRF\/DRI Notes<\/th>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td><strong>Base: Soft Landing<\/strong><\/td>\n<p><\/p>\n<td>55%<\/td>\n<p><\/p>\n<td>Core inflation falls below 2.5% for 2 consecutive quarters; No new major geopolitical conflicts.<\/td>\n<p><\/p>\n<td>Global GDP: +2.8%. G7 CPI returns to ~2.2% by end-2025. Policy rates ease by 75 bps.<\/td>\n<p><\/p>\n<td>Credit spreads tighten by 15 bps. 10yr bond yields fall 50 bps. Equity markets gain 5-8%.<\/td>\n<p><\/p>\n<td>Stable funding costs. CET1 ratios increase by 20-30 bps from retained earnings.<\/td>\n<p><\/p>\n<td>Baseline cyber threat level; occasional minor disruptions.<\/td>\n<p><\/p>\n<td>Focus on DRR: build resilient supply chains; enhance cyber defences. DRI: optimise hedging costs.<\/td>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td><strong>Adverse: Sticky Inflation<\/strong><\/td>\n<p><\/p>\n<td>35%<\/td>\n<p><\/p>\n<td>G7 core inflation stalls >3.0%; major oil price spike (>20% sustained); renewed supply chain disruption.<\/td>\n<p><\/p>\n<td>Global GDP: +1.5% (mild recession). G7 CPI remains at ~3.5%. Policy rates on hold or hiked by 25 bps.<\/td>\n<p><\/p>\n<td>Credit losses rise by 50-75 bps. Spreads widen 75 bps. Equity markets fall 15%. Portfolio MTM losses of 5-10%.<\/td>\n<p><\/p>\n<td>Liquidity Coverage Ratio (LCR) declines by 10%. CET1 ratios fall 100-150 bps.<\/td>\n<p><\/p>\n<td>Increased state-sponsored cyber activity targeting financial services.<\/td>\n<p><\/p>\n<td>Activate DRF: draw on contingent credit lines. DRI: exercise options\/swaps to cap losses.<\/td>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td><strong>Severe: De-anchoring &#038; Polycrisis<\/strong><\/td>\n<p><\/p>\n<td>10%<\/td>\n<p><\/p>\n<td>Major military escalation in a systemic region; climate-driven food shock; inflation expectations de-anchor (>3% on 5y5y swap).<\/td>\n<p><\/p>\n<td>Global GDP: -1.0% (deep recession). G7 CPI spikes to >7%. Policy rates hiked by 200 bps.<\/td>\n<p><\/p>\n<td>Credit losses >200 bps. Spreads widen >300 bps. Equity markets fall >30%. Portfolio MTM losses >15%.<\/td>\n<p><\/p>\n<td>Systemic liquidity stress; LCR breaches. CET1 ratios fall >250 bps, triggering resolution concerns for some firms.<\/td>\n<p><\/p>\n<td>Systemic cyber-attack on critical financial infrastructure.<\/td>\n<p><\/p>\n<td>Full DRF activation: central bank emergency liquidity; trigger parametric catastrophe bonds. DRI: max payouts reached.<\/td>\n<p>\n<\/tr>\n<p>\n<\/table>\n<p><\/p>\n<h2>Risk Transmission &#038; Nexus Map<\/h2>\n<p><\/p>\n<p>Inflation risk does not exist in isolation. Its impact is transmitted and magnified across a nexus of interconnected risks. A primary transmission channel begins with a <strong>geopolitical or climate shock<\/strong> (e.g., conflict in a key shipping lane or a severe drought affecting a major food exporter). This triggers a <strong>supply-chain disruption<\/strong>, leading to a sharp increase in commodity prices (the water-energy-food-health nexus). The resulting cost-push inflation forces a <strong>macroeconomic<\/strong> policy response (monetary tightening), which increases interest rates. Higher rates directly inflict <strong>market risk<\/strong> losses on fixed-income portfolios and increase <strong>credit risk<\/strong> as borrower defaults rise. This can lead to <strong>liquidity risk<\/strong> as institutions may be forced to sell assets at a loss to meet obligations. A concurrent <strong>cyber or operational risk<\/strong> event (e.g., a ransomware attack on a port or a bank) can exacerbate the supply-side shock and undermine confidence, accelerating liquidity pressures and creating a feedback loop of systemic instability.<\/p>\n<p><\/p>\n<h2>Early-Warning Dashboard<\/h2>\n<p><\/p>\n<p>Proactive risk management requires monitoring a dashboard of leading indicators. Crossing these thresholds should trigger pre-defined governance protocols, including enhanced monitoring, review of hedging strategies, and activation of contingency plans.<\/p>\n<p><\/p>\n<table><\/p>\n<caption>Early-Warning Indicators<\/caption>\n<p><\/p>\n<tr><\/p>\n<th>Indicator<\/th>\n<p><\/p>\n<th>Threshold<\/th>\n<p><\/p>\n<th>Direction to Watch<\/th>\n<p><\/p>\n<th>Data Source<\/th>\n<p><\/p>\n<th>Review Frequency<\/th>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td>5-year\/5-year Forward Inflation Swap (G7 Avg.)<\/td>\n<p><\/p>\n<td>> 2.75% for 1 month<\/td>\n<p><\/p>\n<td>Increasing<\/td>\n<p><\/p>\n<td>Financial Data Providers<\/td>\n<p><\/p>\n<td>Daily<\/td>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td>NY Fed Global Supply Chain Pressure Index<\/td>\n<p><\/p>\n<td>> 1.5 standard deviations above mean<\/td>\n<p><\/p>\n<td>Increasing<\/td>\n<p><\/p>\n<td>Federal Reserve Bank of New York<\/td>\n<p><\/p>\n<td>Monthly<\/td>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td>Baker, Bloom &#038; Davis Geopolitical Risk Index<\/td>\n<p><\/p>\n<td>> 200<\/td>\n<p><\/p>\n<td>Increasing<\/td>\n<p><\/p>\n<td>policyuncertainty.com<\/td>\n<p><\/p>\n<td>Monthly<\/td>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td>Financial Conditions Index (e.g., Goldman Sachs FCI)<\/td>\n<p><\/p>\n<td>Tightening by > 100 bps in 3 months<\/td>\n<p><\/p>\n<td>Increasing<\/td>\n<p><\/p>\n<td>Investment Banks \/ Central Banks<\/td>\n<p><\/p>\n<td>Weekly<\/td>\n<p>\n<\/tr>\n<p><\/p>\n<tr><\/p>\n<td>High-Yield Corporate Credit Spreads<\/td>\n<p><\/p>\n<td>Widening by > 150 bps in 3 months<\/td>\n<p><\/p>\n<td>Increasing<\/td>\n<p><\/p>\n<td>ICE BofA Indices<\/td>\n<p><\/p>\n<td>Daily<\/td>\n<p>\n<\/tr>\n<p>\n<\/table>\n<p><\/p>\n<h2>Policy Options &#038; Financial Instruments<\/h2>\n<p><\/p>\n<p>Institutions must adopt an integrated approach to managing complex inflation risk, grounded in the principles of DRR, DRF, and DRI.<\/p>\n<p><\/p>\n<p><strong>Disaster Risk Reduction (DRR):<\/strong> These are proactive measures to lower risk exposure. For sovereigns and central banks, this includes policies to enhance supply-side flexibility, such as investing in renewable energy to reduce fossil fuel dependency (transition risk mitigation), diversifying trade partners, and maintaining strategic commodity reserves. For financial firms, DRR involves robust ALM, reducing duration mismatch, and stress testing balance sheet exposures to nexus shocks. Enhanced cybersecurity protocols and operational resilience are critical DRR investments.<\/p>\n<p><\/p>\n<p><strong>Disaster Risk Financing (DRF):<\/strong> These are instruments designed to ensure liquidity is available immediately following a shock. Central banks can provide emergency liquidity assistance and swap lines. Governments and firms can establish contingent credit facilities. A state-of-the-art approach involves parametric financing. For example, a sovereign fund could enter into a swap that pays out if a specific commodity price index (e.g., FAO Food Price Index) exceeds a pre-defined trigger level for a set period, providing budget support to cushion the impact on the populace and economy. Insurers can issue catastrophe bonds whose principal is forgiven if a severe stagflation scenario, defined by parametric triggers on CPI and GDP, materialises.<\/p>\n<p><\/p>\n<p><strong>Disaster Risk Insurance (DRI):<\/strong> This involves transferring risk to a third party. Traditional instruments include inflation-linked swaps and options. For corporates, this could involve business interruption insurance that covers supply-chain disruptions. For sovereigns, multilateral development banks can offer insurance products linked to climate and commodity price shocks, which function as a form of DRI to protect fiscal stability.<\/p>\n<p><\/p>\n<h2>Implementation Roadmap<\/h2>\n<p><\/p>\n<p>A phased approach is recommended for institutions to upgrade their inflation risk management capabilities.<\/p>\n<p><\/p>\n<ol><\/p>\n<li><strong>Phase 1 (0-6 Months): Diagnosis &#038; Governance.<\/strong> Boards and risk committees should commission a comprehensive assessment of their inflation exposure across the full risk nexus. Update risk appetite statements and governance frameworks to explicitly incorporate nexus risks and DRR\/DRF\/DRI principles. Establish the Early-Warning Dashboard and define trigger protocols. Accountable Owner: Chief Risk Officer (CRO).<\/li>\n<p><\/p>\n<li><strong>Phase 2 (6-18 Months): Model &#038; Data Enhancement.<\/strong> Invest in analytical capabilities, including integrating alternative data (geospatial, textual) into forecasting models. Develop and calibrate stress test scenarios that combine inflation shocks with cyber, climate, and geopolitical events. Quantify potential capital and liquidity impacts. Accountable Owner: Head of Quantitative Analytics \/ Chief Technology Officer.<\/li>\n<p><\/p>\n<li><strong>Phase 3 (18-36 Months): Strategic Repositioning &#038; Instrumentation.<\/strong> Based on the analysis, execute strategic shifts in asset allocation and liability management to reduce vulnerabilities (DRR). Implement and test contingent financing arrangements (DRF). Execute risk transfer strategies (DRI) where cost-effective. This phase involves active portfolio management and negotiation of complex financial contracts. Accountable Owners: Chief Investment Officer (CIO), Treasurer.<\/li>\n<p>\n<\/ol>\n<p><\/p>\n<p>Dependencies include access to high-quality data, sufficient budget for analytical talent and technology, and board-level commitment. The primary risk is &#8216;paralysis by analysis&#8217;; a pragmatic, iterative approach is essential.<\/p>\n<p><\/p>\n<h2>Method Notes<\/h2>\n<p><\/p>\n<p><strong>Scenario Quantification:<\/strong> Impacts are derived from the GVAR model&#8217;s impulse response functions, shocked with scenario-consistent paths for commodity prices and policy rates. Credit losses are projected using a satellite model linking defaults to GDP growth, interest rates, and credit spreads, estimated via panel regression.<\/p>\n<p><\/p>\n<p><strong>Capital Impact Formula:<\/strong> The change in the CET1 ratio (\u0394CET1) is estimated as: \u0394CET1 \u2248 [ (\u0394NII + \u0394MTM &#8211; \u0394ECL) * (1-t) ] \/ RWA. Where \u0394NII is the change in Net Interest Income, \u0394MTM is the mark-to-market loss on the available-for-sale portfolio, \u0394ECL is the change in Expected Credit Losses, &#8216;t&#8217; is the effective tax rate, and RWA are Risk-Weighted Assets. RWA are assumed to inflate by 5% in the Severe scenario due to credit quality migration.<\/p>\n<p><\/p>\n<p><strong>Parameter Choices:<\/strong> The GVAR uses one lag based on information criteria. The copula is a Student&#8217;s t-copula to better capture tail dependence. EVT fitting uses a Generalized Pareto Distribution for losses exceeding the 95th percentile. The agent-based model assumes banks attempt to hoard liquidity when their counterparty credit risk score, derived from market data, exceeds a critical threshold.<\/p>\n<p><\/p>\n<p><strong>Sensitivity Statement:<\/strong> The quantified impacts are highly sensitive to assumptions regarding the persistence of the inflation shock and the speed of the central bank policy response. A 50-basis-point larger-than-assumed policy rate hike in the Adverse scenario would increase projected credit losses by an additional 15-20 bps and market value losses by an additional 2-3%.<\/p>\n<p><\/p>\n<h2>References &#038; Data Log<\/h2>\n<p><\/p>\n<ol><\/p>\n<li>International Monetary Fund (IMF). (2023). <em>World Economic Outlook, October 2023<\/em>. Washington, DC. Accessed: May 20, 2024.<\/li>\n<p><\/p>\n<li>Bank for International Settlements (BIS). (2023). <em>Annual Economic Report 2023<\/em>. Basel, Switzerland. Accessed: May 20, 2024.<\/li>\n<p><\/p>\n<li>Financial Stability Board (FSB). (2023). <em>FSB review of the 2023 bank failures<\/em>. Basel, Switzerland. Accessed: May 21, 2024.<\/li>\n<p><\/p>\n<li>Organisation for Economic Co-operation and Development (OECD). Main Economic Indicators database. Dataset version: May 2024. Accessed: May 21, 2024.<\/li>\n<p><\/p>\n<li>Federal Reserve Bank of New York. Global Supply Chain Pressure Index (GSCPI). Dataset version: April 2024. Accessed: May 21, 2024.<\/li>\n<p><\/p>\n<li>Baker, S. R., Bloom, N., &#038; Davis, S. J. (2016). Measuring Economic Policy Uncertainty. <em>The Quarterly Journal of Economics<\/em>, 131(4), 1593-1636. Data from www.policyuncertainty.com. Accessed: May 21, 2024.<\/li>\n<p>\n<\/ol>\n<p><\/p>\n<h2>Legal &#038; Methodological Disclaimer<\/h2>\n<p><\/p>\n<p>This analysis was assisted by a generative artificial intelligence system using a curated set of authoritative sources. While all outputs have been reviewed and edited for accuracy and coherence, they may contain errors or omissions. All figures, statistics, and findings presented are traceable to the cited public sources or are based on explicitly stated assumptions outlined in the Method Notes. This article reflects analytical content only and does not constitute investment, legal, accounting, tax, or supervisory advice, nor does it represent the official position of any institution. No personal data were intentionally processed in the creation of this analysis. The analysis did not access or utilise any material non-public information. Human review and professional judgment are strongly recommended before any outputs are used for operational or decision-making purposes. Readers should verify all information and consult with qualified professionals before making any financial or policy decisions.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Executive Summary Persistent and volatile inflation has evolved from a cyclical macroeconomic concern into a systemic risk catalyst, amplified through a nexus of geopolitical, climate, and cyber threats, which necessitates a strategic pivot by public and private financial institutions from conventional hedging towards integrated disaster risk reduction (DRR), financing (DRF), and insurance (DRI) frameworks to [&hellip;]<\/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-3338","post","type-post","status-publish","format-standard","hentry","category-risks"],"_links":{"self":[{"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/posts\/3338","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=3338"}],"version-history":[{"count":0,"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/posts\/3338\/revisions"}],"wp:attachment":[{"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/media?parent=3338"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/categories?post=3338"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/therisk.global\/registry\/wp-json\/wp\/v2\/tags?post=3338"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}