Risk Assessment In Investment Portfolios

👉 Are we using the wrong tools to assess climate risk? A new expert-led assessment, drawing on the judgment of 60+ climate scientists, says that #climatechange introduces forms of risk that exceed the design assumptions of existing economic and financial frameworks. Here’s what that means in practice ⬇️ 🔹 Climate damages are structural, they reshape economies: where people live, what can be produced, how infrastructure functions, and which regions remain viable. 🔹 Extremes drive real-world risk: what actually destabilises societies and markets are heatwaves, floods, droughts, grid failures, food shocks. It’s the tails of the distribution that matter. 🔹 GDP misses mortality, inequality, displacement, ecosystem loss, and can even rise after disasters due to reconstruction. This creates a dangerous illusion of resilience. 🔹 Repeated shocks erode recovery capacity and propagate across supply chains, finance, migration, and geopolitics. 🔹 Beyond ~2°C, uncertainty widens sharply. Confidence in precise damage estimates falls even as consequences grow. 🔹 Tipping points expose the limits of economic modelling: At higher warming levels, model outputs can appear precise while resting on assumptions that no longer hold. At the same time, many models also underestimate positive tipping points in clean energy and innovation. The goal is to build resilience under deep uncertainty. For treasuries, central banks, regulators, and long-horizon investors, this means recalibrating governance toward: ➡️ precaution ➡️ robustness ➡️ transparency Because avoiding irreversible outcomes is always cheaper than trying to price them after the fact. read the report "Recalibrating Climate Risk" here 👇 https://lnkd.in/dx8wmRZ4 Green Futures Solutions (University of Exeter) Carbon Tracker @aurora trust

𝗪𝗵𝘆 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝘀𝘁𝘀 𝘀𝘆𝘀𝘁𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗿𝗶𝘀𝗸𝘀 A new report (👉https://lnkd.in/eMsCKQuh) exposes a fundamental gap between what climate scientists expect and what economic models predict. 𝗧𝗵𝗲 𝗰𝗼𝗿𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: 68 climate scientists from 12 countries were surveyed about economic damage estimates. Their insights differ radically from standard models: 🔴 At 3°C warming, experts estimate median GDP damage at ~35%. The Nordhaus DICE model predicts only ~3% 🔴 36% of scientists place the "collapse threshold" 𝘣𝘦𝘭𝘰𝘸 4°C, while many scenarios model up to 4°C and beyond 🔴 250 million people displaced by climate disasters in the past decade, impacts barely visible in GDP figures 𝗪𝗵𝘆 𝘄𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝘄𝗿𝗼𝗻𝗴: We focus on global averages, but people experience 𝘭𝘰𝘤𝘢𝘭 𝘦𝘹𝘵𝘳𝘦𝘮𝘦𝘴: the 2021 Texas storm caused $195 billion damage while barely registering in global temperature statistics. GDP often 𝘳𝘪𝘴𝘦𝘴 after disasters (reconstruction spending) while real wealth declines – the "disaster industrial complex" accounts for 1/3 of US economic activity at 1.4°C warming Models assume smooth damage curves but ignore tipping points, cascades, and system failures 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: This gap determines how pension funds assess risks and how central banks conduct stress tests. The NGFS recently raised damage estimates from 7-14% to 30% GDP loss at 3°C, but climate scientists say even this underestimates. 𝗧𝗵𝗲 𝘂𝗻𝗱𝗲𝗿𝗹𝘆𝗶𝗻𝗴 𝗰𝗮𝘂𝘀𝗲: Research ( 👉 https://lnkd.in/eVsBapbT) shows "disciplinary asymmetries": economists seek optimization within existing systems; natural scientists see limits and tipping points. Where economists use GDP as proxy, scientists see missed impacts on health, ecosystems, and inequality. As a consequence, environmental scientist see degrowth as an option, while economist favour market based solutions 👇 . 𝗪𝗵𝗮𝘁 𝗻𝗼𝘄: The report calls for "recalibration toward precaution, robustness, and transparency": ✓ Report ranges instead of point estimates ✓ Acknowledge where models fail (especially above 2-3°C) ✓ Integrate metrics beyond GDP: mortality, inequality, ecosystem degradation ✓ Model cascades and second-order effects The crucial insight: climate change introduces risks exceeding existing economic frameworks. The response is not waiting for perfect models, but recognizing that avoiding irreversible outcomes is cheaper than pricing them after the fact. For long-term investors: climate risk cannot be fully diversified away. It's a systemic risk requiring fundamentally different strategies. #climaterisk #climateeconomics #systemchange #financialrisk #sustainablefinance

Climate-related disasters may cause $12.5 TN in losses by 2050. How are investors preparing? This powerful new methodology from Institutional Investors Group on Climate Change (IIGCC) offers a way forward and includes a data tool as well. What to know: -The new Physical Climate Risk Appraisal Methodology (PCRAM 2.0) was designed for real-asset developers, managers, and capital providers. -It is applicable to both public and private sector assets and is geography agnostic. -The methodology combines insights from climate science, engineering, and finance to support a user to incorporate PCRs into asset appraisal. -PCRAM 2.0 is relevant to investment decision-makers, offering practical applications for both institutional investors and businesses to consider as they navigate uncertainty. Benefits for Investors: 1. Standardisation: Provides a consistent process for evaluating and managing investments in climate-resilient Real Estate and Infrastructure. 2. Risk and Opportunity: Focuses on resilience benefits like predictable cash flows, enhanced credit quality, and efficient long-term cost management. 3. Efficient Resource Management: Encourages a holistic approach to risk management, ensuring effective resource allocation for building resilient assets. 4. Building Investor Knowledge: Helps institutional investors navigate uncertainty Explore the methods, the data tracker, and share your thoughts here: https://lnkd.in/eKMdBSwj #climaterisk #climatefinance #investors #physicalrisk

Tail risk-aware investors: 1. Don’t blindly rely on full-sample correlations for portfolio construction 2. Give scenario analysis a meaningful role in asset allocation decisions 3. Use these downside scenarios to estimate the investors’ risk tolerance 4. Use portfolio optimization tools that account directly for left-tail risks 5. Beware of “diversification free lunches” in privately held asset classes 6. Evaluate interest rate risk and its impact on stock-bond diversification 7. Seek asset classes that provide upside “unification”/anti-diversification 8. Consider active risk management strategies: ▪️ Hedges with put options and proxies ▪️ Strategies that embed short positions ▪️ Momentum-based factors or strategies ▪️ Actively-managed absolute return alts ▪️ Managed volatility overlays/strategies ▪️ Strategic or tactical cash allocations [From the book Beyond Diversification. This is not investment advice.]

It is perhaps surprising that even today, MACRO RISK accounts for over 50% of total portfolio risk for a representative US equity portfolio 👉 As the chart shows, the macro risk % of total risk has moved sharply higher in times of macro volatility The recent peak was 2022 (macro risk was 65% of total risk) and since late 2023 macro risk has been declining as idiosyncratic forces (#nvidia #ai #mag7) started to rise and as the #fed finished hiking rates 🚨 But macro risk still accounts for over 50% of the total risk of a portfolio made up of the #GoldmanSachs Very Important Positions ETF (#GVIP) constituents 👉 Macro still matters It is possible to break down this Macro Risk into its pieces and run a full attribution. The risk model underneath this chart is very similar to traditional equity factor risk models. In fact, it is entirely interoperable with these types of models. The difference is that macro factors (macro factor returns) are used. These are de-correlated and then related to portfolio (or indeed single stock) returns. A wide range of macro factors are used (including daily real GDP estimates). A variance -covariance matrix is used to generate Total Risk. The result is the "macro DNA" of your porfolio. 👉 And the message here is that macro is still impacting daily returns and overall risk. It is interesting to look at return attribution (not shown) - where you can see what part of returns was explained by macro factors ("non-specific") and what portion came from other sources ("specific risk") A greater focus on macro factor risk seems to be an emerging trend in among equity investors and equity long/short funds in particular over the last few years. Many funds have started to implement macro risk solutions. 👉 It would be very interesting to get any thoughts from equity investors on whether they feel understanding their macro risk is a challenge, how this is being handled and how best practice in this area is evolving

𝐓𝐡𝐢𝐧𝐠𝐬 𝐭𝐨 𝐊𝐞𝐞𝐩 𝐢𝐧 𝐌𝐢𝐧𝐝 𝐃𝐮𝐫𝐢𝐧𝐠 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: 1. 𝐀𝐬𝐬𝐞𝐭 𝐀𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧 Is the portfolio diversified across asset classes (equity, debt, gold, etc.)? Proper allocation reduces risk and improves stability. 2. 𝐑𝐢𝐬𝐤 𝐯𝐬. 𝐑𝐞𝐭𝐮𝐫𝐧 Look beyond just returns. Assess risk-adjusted returns using Sharpe Ratio, Treynor Ratio, and Jensen’s Alpha. 3. 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 𝐁𝐞𝐭𝐚 Understand the portfolio’s sensitivity to market movements. High beta = higher volatility. 4. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠 Compare returns against relevant benchmarks (like Nifty 50, Sensex, etc.). Outperformance or underperformance gives valuable insights. 5. 𝐆𝐨𝐚𝐥 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 Does the portfolio align with the investor’s financial goals, time horizon, and risk appetite? A high-return portfolio isn’t useful if it doesn’t meet the purpose. 6. 𝐑𝐞𝐛𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠 𝐅𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 Check if the portfolio is rebalanced regularly to maintain desired allocation. Market movements can distort the original strategy. 7. 𝐄𝐱𝐩𝐞𝐧𝐬𝐞 𝐑𝐚𝐭𝐢𝐨𝐬 𝐚𝐧𝐝 𝐂𝐨𝐬𝐭𝐬 High expense ratios or hidden charges can eat into your returns. Analyze net returns after costs. 8. 𝐓𝐚𝐱 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 Understand the tax implications of short-term and long-term capital gains. Opt for instruments that are more tax-efficient, where possible. 9. 𝐋𝐢𝐪𝐮𝐢𝐝𝐢𝐭𝐲 𝐨𝐟 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭𝐬 Can the investments be liquidated quickly in case of emergencies? Illiquid assets may pose a problem during urgent needs. 10. 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐨𝐟 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 Is the portfolio consistently delivering returns over time? Avoid portfolios that rely on one-off gains. Follow: Priyanshu Pandey #PortfolioAnalysis #InvestmentTips #FinancialPlanning #AssetAllocation #RiskManagement #PersonalFinance #WealthManagement

Tail risk refers to the likelihood and impact of rare, extreme moves in investment returns typically those beyond three standard deviations from the mean events that standard normal-based models fail to capture Real-world return distributions exhibit excess kurtosis meaning extreme outcomes (both losses and gains) occur more often than a normal distribution would predict Practical Techniques to Model Tail Risk 1. Value at Risk (VaR) & Expected Shortfall (ES / CVaR) VaR computes the maximum expected loss at a given confidence level (e.g., 95% or 99%) over a certain horizon. It's simple but doesn't capture the magnitude of losses beyond that threshold Expected Shortfall (ES), aka Conditional VaR (CVaR) or Tail VaR, measures the average loss in the worst-case tail beyond the VaR threshold—offering a more comprehensive view of tail behavior ES is coherent and subadditive (unlike VaR), making it more suitable for portfolio risk management In practice, ES can be computed using closed-form formulas for certain distributions or via simulation (e.g., Monte Carlo) 2. Extreme Value Theory (EVT) / Peaks-Over-Threshold (POT) Focuses on modeling the tail distribution directly, rather than the entire return distribution. The POT method fits a Generalized Pareto Distribution (GPD) to the values that exceed a high threshold sidestepping parametric assumptions over the full range EVT approaches are highly practical in risk management used for forecasting VaR and ES more accurately, especially when data exhibit heavy tails Academic work shows combining GARCH filtering for volatility clustering with EVT on residuals improves tail risk estimates 3. GARCH and Time-Series Models Return volatility clusters over time. GARCH (and its variants) models this conditional heteroskedasticity: ARCH/GARCH models estimate time-varying volatility, improving tail risk estimates by accounting for changing market regimes These models are often paired with EVT for enhanced tail modeling: filter returns via GARCH, then apply EVT (like POT) to the standardized residuals 4. Stochastic‐Volatility and Jump Models (SVJ) These models capture both volatility dynamics and discontinuous jumps: SVJ models (e.g. Bates, Duffie–Pan–Singleton) blend stochastic volatility with jump components, enabling fat tails, skewness, volatility clustering, and large jumps all in one model They’re particularly useful for tail risk modeling in derivatives pricing and hedging applications thanks to their market realism 5. Copulas for Multivariate Tail Risk To model joint tail dependencies across assets: Copulas enable constructing joint distributions from individual marginals, capturing dependence structures including during extreme events Useful for portfolio-level tail risk, systemic risk, or stress testing scenarios where multiple assets may suffer extreme losses simultaneously 

Climate change has become a financial equation 🌍 Companies are beginning to quantify what inaction could cost, translating climate risk into direct revenue impacts. The data show that addressing climate impacts through mitigation and adaptation measures represents about 8% of FY24 revenues, while the cost of inaction reaches 15%. This means the financial exposure of not acting almost doubles the investment required to act. The chart shows how this varies across sectors. Energy, materials, and building industries face some of the highest projected costs of inaction, driven by physical and transition risks. In contrast, the real estate sector stands out with a cost of action near 96%, reflecting the capital needed to protect assets from floods, fires, and hurricanes. Financial asset owners and managers estimate the cost of inaction at 120% of FY24 revenues, the highest across sectors, signaling a growing understanding of portfolio-wide climate risk. These figures show that climate change is now treated as a balance sheet issue, not a sustainability add-on. They also reveal that value protection depends on early adaptation and strategic investment. The financial logic is clear. Acting today reduces the future cost of disruption, regulation, and loss of assets. The next step is to internalize these insights into decision-making, linking climate risk directly with business strategy. How prepared are companies to make that connection before the cost gap widens? Source: EY Global Climate Action Barometer 2025 #sustainability #esg

Value-at-Risk (VaR) and Expected Shortfall (ES) are two key measures used in risk management to quantify potential losses in investments or portfolios. Estimating such risk measures for static and dynamic portfolios involves simulating scenarios that represent realistic joint dynamics of their components. This requires both a realistic representation of the temporal dynamics of individual assets (temporal dependence) and an adequate representation of their co-movements (cross-asset dependence). A common approach in scenario simulation is to use parametric models, but these models often struggle with heterogeneous portfolios and intraday dynamics. As a result, Gaussian factor models are widely used to address the scalability constraints inherent in nonlinear models. However, they often fail to capture many stylized features of market data. Stylized facts in finance refer to empirical regularities observed in financial data across various markets and time periods. These facts are considered robust and have significant implications for financial modelling and risk management. Some of the stylized statistical properties of asset returns include absence of autocorrelations, heavy tails, gain/loss asymmetry, aggregational Gaussianity, intermittency, and volatility clustering. Generative Adversarial Networks (GANs) offer a promising alternative to both parametric models and Gaussian factor models, as they can learn complex patterns from data without relying on parametric assumptions. To correctly quantify tail risk, the authors of [1] proposed Tail-GAN, a novel data-driven approach for multi-asset market scenario simulation that focuses on generating tail risk scenarios for a user-specified class of trading strategies. Tail-GAN utilizes GAN architecture and exploits the joint elicitability property of VaR and ES (Expected Shortfall). The proposed TAil-GAN is capable of learning to simulate price scenarios that preserve tail risk features for benchmark trading strategies, including consistent statistics such as VaR and ES. #QuantFinance Their numerical experiments show that, in contrast to other data-driven scenario generators, the proposed Tail-GAN method used in scenario simulation correctly captures tail risk for both static and dynamic portfolios. The links to their preprint [1] and the #Python GitHub repo [2] are posted in the comments.

"𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗼𝗻𝗹𝘆 𝗹𝗼𝗼𝗸 𝗮𝘁 𝗿𝗲𝘁𝘂𝗿𝗻𝘀. 𝗕𝘂𝘁 𝗶𝘀 𝘁𝗵𝗮𝘁 𝗿𝗲𝗮𝗹𝗹𝘆 𝘁𝗵𝗲 𝘄𝗵𝗼𝗹𝗲 𝗽𝗶𝗰𝘁𝘂𝗿𝗲?" 🤔 Chasing only high returns is like focusing only on the speed of your car without checking fuel levels, engine health, or your final destination. 🚗💨 In long-term investing, wealth creation hinges on several key factors. Here are the seven most important factors: 𝟭. 𝗖𝗹𝗲𝗮𝗿 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗚𝗼𝗮𝗹𝘀 Setting specific financial goals (like buying a house, retirement, or children’s education) helps you plan and stay focused. Example: Knowing you need ₹1 crore for your child's education in 15 years helps you choose the right investments to meet this target. 𝟮. 𝗧𝗶𝗺𝗲 𝗛𝗼𝗿𝗶𝘇𝗼𝗻 The duration you plan to stay invested impacts your investment choices. Longer horizons can handle more risk for potentially higher returns. Example: If you have 20+ years until retirement, you can afford to invest heavily in equity, as you have time to ride out market volatility. 𝟯. 𝗔𝘀𝘀𝗲𝘁 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 Diversifying across asset classes (equity, debt, gold etc.) reduces risk and optimizes returns. Example: A mix of 60% equities, 30% debt, and 10% gold can help you diversify and stabilize your portfolio, catering to different market conditions. 𝟰. 𝗥𝗲𝗴𝘂𝗹𝗮𝗿 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁𝘀 Consistent investing, such as via SIPs (Systematic Investment Plans), leverages the power of compounding and reduces market timing risks. Example: Investing ₹10,000 monthly in an equity mutual fund over 20 years can grow significantly through the compounding effect. 𝟱. 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Understanding your risk tolerance and adjusting your investments accordingly protects you from making panic decisions during market downturns. Example: If you can't handle the volatility of equity, balancing with safer debt funds can help maintain peace of mind. 𝟲. 𝗣𝗮𝘁𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗗𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 Wealth creation is a long journey. Staying invested through market ups and downs is key to compounding returns. Example: Investors who stayed invested during market crashes and didn't panic sell (like in 2008 or 2020) benefited from subsequent market recoveries. 𝟳. 𝗥𝗲𝘁𝘂𝗿𝗻𝘀: 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 Chasing high returns can lead to risky decisions, but aiming for steady, consistent returns helps build wealth over time without unnecessary stress. Example: Aiming for consistent returns of 10-12% annually in a diversified portfolio can help you achieve your financial goals without any stress, even if it means avoiding trendy but volatile investments. Focusing on these seven pillars can set you on a path to long-term financial success. Instead of chasing quick gains, build a sustainable, well-rounded strategy that stands the test of time. Are you focusing on high returns or building a resilient investment strategy for the long haul? Take a moment to rethink your approach. 💭