Asset Risk Valuation

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.

In a NAV (Net Asset Value) credit facility, the methodology/procedure for valuing assets is at the core of the deal. The valuation of assets in a NAV facility determines the amount that can be borrowed under the facility and when prepayments of the loan must be made, usually through the use of a maximum LTV (Loan-to-Value) ratio. The higher the valuation of the assets, the more the borrower can borrow under the facility. A secured lender's worst fear is that the loan will default and the collateral will not pay back the loan. For this reason, NAV lenders focus on the valuation of a fund's assets and the LTV ratio (which allows for breathing room in case the assets sell for less than their assessed value). The question lenders often confront, however, is "What the heck are these assets worth"? The answer depends on the fund's investments/strategy: ➡ Private Equity Funds: These funds, which own equity in private companies, use the Discounted Cash Flow (DCF) method, where future cash flows are discounted to current value using a rate tied to the time value of money and the risk of the investment. The LTV ratio for these funds tends to be low, usually 5% to 20%, because these investments are illiquid and bespoke. ➡ Private Credit Funds: These funds, which make or purchase loans, often value assets using a mix of the DCF method and comparisons to the valuation of similar loans sold in the market. Because the cash flows are tied to contractual obligations in the underlying loan agreements and there is often an active secondary market for loans, the valuation is more reliable and the LTV range is higher, usually 30% to 70%. ➡ Secondaries: These funds buy equity interests in other funds in the secondary market. The valuation of these investments is often a combination of the market approach (either examining the price of similar recent transactions or using a price to earnings multiple) and the DCF approach. Secondaries funds typically have an LTV ranging from 25% to 60%, reflecting the higher level of confidence in the valuation. The valuation procedure in the credit agreement varies based on the strategy of the fund borrower. A fund borrower usually supplies the initial valuation and provides regular updates on the value, usually on a monthly, quarterly or semi-annual basis. The credit agreement may include the methodologies and assumptions to be used in valuing the assets. The credit agreement may also provide a procedure for disputing an asset valuation, which is often triggered when the facility's LTV gets close to the covenanted LTV level. There may be limits to how often a valuation can be challenged and provisions as to which party has to pay for the valuation. These protocols are subject to negotiation but also vary depending on the fund strategy, with a challenge right being more common in a private equity buyout fund and less typical in a secondaries fund or a private credit fund.

Day 5 of 7: Unlocking Quant Knowledge – Exploring Advanced VaR Metrics (CVaR, IVaR, MVaR, Relative VaR) These metrics build on VaR, offering more insight into portfolio risks at a confidence level like 95% over a time horizon (e.g., one month). Let’s break them down: Conditional Value at Risk (CVaR): Also called Expected Shortfall (ES), CVaR measures the average loss in scenarios where losses exceed VaR. It provides a deeper understanding of tail risk by quantifying the severity of extreme losses rather than just their likelihood. Imagine planning a picnic and calculating the average damage (wet food, ruined decorations) if the 5% chance of a storm hits—CVaR goes beyond the VaR threshold to assess the expected loss in extreme cases. For a $1 million tech portfolio (40% Tesla, 35% Apple, 25% Microsoft) in Feb 2025 with a 95% VaR of $50,000 over one month, CVaR might be $75,000, meaning that in the worst 5% of cases, the expected loss would average $75,000—highlighting extreme risks. Incremental Value at Risk (IVaR): IVaR measures the impact of adding a new asset to the portfolio on overall VaR. It helps assess whether an addition increases or diversifies risk. It’s like adding a new dish to your picnic menu and checking how it affects your budget risk if prices spike. If adding $200,000 in Nvidia to the portfolio increases its 95% VaR from $50,000 to $55,000, then the IVaR of Nvidia is $5,000—helping you decide if the additional exposure is justified given potential returns. Marginal Value at Risk (MVaR): MVaR estimates the rate of change in portfolio VaR when slightly increasing a position in an asset. It identifies which assets contribute the most to overall risk. It’s like slightly increasing the picnic dessert budget and checking how it impacts your overall cost risk. For the portfolio, if increasing Tesla’s position by 1% raises the 95% VaR by $1,000, that’s the MVaR of Tesla—helping fine-tune allocation by identifying which assets are driving risk. Relative Value at Risk (Relative VaR): Relative VaR compares portfolio VaR against a benchmark, such as an index, to assess how much additional risk is being taken. It’s useful for understanding risk-adjusted performance. It’s like comparing your picnic rain risk to the city average. If your portfolio’s 95% VaR is $50,000, but the S&P 500’s is $30,000, your Relative VaR shows you’re taking $20,000 more risk than the market—helping assess exposure relative to industry standards. Real-World Applications Risk analysts apply these for regulatory reports, and hedge funds use them to fine-tune strategies during volatile markets. Fun Fact These concepts are employed beyond finance as well, notably in supply chain optimization, insurance modeling, and renewable energy planning—any domain where managing extreme (tail) risks is critical. #QuantFinance #RiskManagement #ValueAtRisk #EquityPortfolio #FinancialModeling #Finance #Investment #MarketRisk #RiskAnalysis

A key question every organization should ask: What is your intellectual property worth, and what would it cost if you lost it? Whether through ransomware, theft, or data loss, the financial and operational impact of a cyber incident defines your value at risk (VaR). To manage and reduce this risk effectively: 1. Assess What’s at Stake – Identify your most valuable digital assets and determine their worth. If they were stolen or encrypted by ransomware, what would the financial and reputational damage be? 2. Reduce the Likelihood of Harm – Implement security measures in phases: -Crawl: Establish basic protections like backups, access controls, and endpoint security. -Walk: Strengthen detection and response capabilities with continuous monitoring. -Run: Build resilience through advanced threat modeling, zero-trust security, and incident response plans. 3. Plan for the Future: Cyber threats evolve, so security should too. Ask yourself: Three years from now, what’s my cybersecurity headline? Will my value at risk have increased or decreased? What proactive steps today will make the biggest difference over time? By systematically reducing your value at risk, organizations can protect their most critical assets and build long-term resilience against evolving cyber threats.

When do you switch from earnings-based to asset-based valuation methods? Most valuation starts with earnings. Multiples, cash flows, DCF models. But sometimes, the income statement is not the best lens. Here is when you step back and let the balance sheet take over: 1. When the business is no longer a going concern - If operations are winding down or liquidity is under stress, future earnings lose relevance. - In distressed cases, liquidation value or net asset value becomes the core of the valuation. 2. When the business is asset-rich but income-poor - A company might own land, real estate, or investments that do not show up in earnings. - If the market is undervaluing those assets, a book-value-based approach helps uncover hidden value. 3. When historical earnings are volatile or unreliable - If cash flows are inconsistent, driven by one-offs, or subject to manipulation, you cannot rely on multiples. - Asset-based valuation provides a floor when the income stream cannot be trusted. 4. When the business is in early-stage or pre-revenue phase - Startups or R&D-heavy businesses often have limited or negative earnings. - In such cases, the value is in the assets like patents, IP, capitalized costs, not the income statement. 5. When the assets are more valuable than the operations - Sometimes the operating business is loss-making, but the underlying assets like brands, land, inventory can be monetized at a premium. - Here, asset-based valuation gives you the realizable value, not the accounting one. Earnings-based methods work when future cash flows are predictable. Asset-based methods take over when earnings lose their signaling power. Follow Pratik S for Investment Banking Careers and Education

Value at Risk (VaR) is a widely used risk management metric that quantifies the potential loss in the value of a portfolio of assets or investments over a specified time horizon and at a given confidence level. In simpler terms, VaR provides an estimate of the maximum amount of money an investment or portfolio is likely to lose within a certain time frame with a certain level of confidence. For example, a 95% VaR of $100,000 over one week would mean that there is a 5% chance of the portfolio losing more than $100,000 in the next week. There are different models to calculate VaR, and the choice of model depends on the characteristics of the portfolio and the assumptions made about the underlying assets. Some common VaR models include: 👉🏼 Historical VaR: This method uses historical price data to estimate the potential losses. It simply looks at past price movements and calculates VaR based on the historical volatility. For example, if the historical volatility of a portfolio is 10%, a 95% VaR would be the loss that is exceeded with a 5% probability based on past price movements. 👉🏼 Parametric VaR: This method assumes that asset returns follow a specific distribution, often the normal (Gaussian) distribution, and uses statistical properties of the distribution to estimate VaR. It requires estimating the mean and standard deviation of returns to calculate VaR. 👉🏼 Monte Carlo VaR: This method uses simulations to model the potential distribution of asset returns. It involves generating a large number of random scenarios for asset prices and calculating the portfolio value for each scenario. The VaR is then estimated based on the distribution of the simulated portfolio values. 👉🏼 Conditional VaR (CVaR) or Expected Shortfall: CVaR is an extension of VaR and represents the expected loss beyond the VaR level. It provides a measure of the average loss in the tail of the distribution. Instead of focusing on the worst outcome given a confidence level, it considers the average loss for those outcomes that exceed the VaR threshold. 👉🏼 Historical Simulation: This approach uses past returns and ranks them from worst to best. The VaR is then calculated based on the historical observations corresponding to the chosen confidence level. 👉🏼 GARCH (Generalized Autoregressive Conditional Heteroskedasticity) Models: GARCH models are used to estimate the volatility of asset returns over time. Once the volatility is estimated, it can be used to calculate VaR. Each VaR model has its assumptions and limitations. The choice of model should be based on the characteristics of the portfolio and the data available. Moreover, VaR is just one tool in risk management, and it should be used in conjunction with other risk measures and stress tests to get a comprehensive understanding of the portfolio's risk profile. Anup Singh Picture Courtesy - Investopedia #var #marketrisk #riskmanagement #riskmodeling #riskassessment #riskanalysis #stresstesting LinkedIn

Climate Value-at-Risk (𝗖𝗹𝗶𝗺𝗮𝘁𝗲 𝗩𝗮𝗥) provides a forward-looking and return-based valuation assessment to measure climate-related risks and opportunities. Climate VaR is a quantitative assessment calculated at the company and security level. The aggregated company Climate #VaR is calculated as a percentage of market value (from -100% to +100%) for multiple climate scenarios and includes the valuation impacts arising from technology opportunities, policy risks and physical risks. #CVAR is widely used across the industry with many asset owners and investment managers including such measure in their regular reports. Although third party solutions are providing such measure, some firms rely on their internal modelling or a mixture of both third party solutions and internal work. The purpose of this compilation is to provide an overview of CVAR, its derivation, framework and methodology as well as application. The compilation includes several reports/working papers and articles addressing CVAR: 1. MSCI Climate Value-at-Risk (VaR) Methodology 2. Introduction to Climate Value-at-Risk: Methodologies and Tools to Evaluate the Financial Impact of Climate-Related Risks and Opportunities 3. Measuring climate-related and environmental risks for equities 4. Incorporating climate risk in to Strategic Asset Allocation 5. CLIMAFIN handbook: pricing forward-looking climate risks under uncertainty 6. Does Climate VaR add financial value : Some empirical evidence 7. Climate-related risks in financial assets 8. Climate value at risk of global financial assets 9. Climate Risks in Financial Assets 10. Climate Risk and Financial Stability: Evidence from Bank Lending 11. Integrating Climate Risk Into an Insurer’s Strategic Asset Allocation 12. CLIMATE RISK ASSESSMENT OF THE SOVEREIGN BOND PORTFOLIO OF EUROPEAN INSURERS 13. Climate value at risk and expected shortfall for Bitcoin market 14. Climate Risk Report #riskmanagement #riskmeasurement #riskassessment #valueatrisk #climaterisk #expectedshortfall #physicalrisk #transitionrisk #climatechange #financialstability #riskmetrics #systemicrisk #assetallocation #carbonemissions #ESG #riskappetite #riskframework #Basel #netzero #information #resources #knowledge #ES #banklending #transmissionchannel #Basel #BCBS #education #stresstesting #financialassets #riskreporting #environmentalrisk

Why CFA, FRM, Risk Professionals, and Banking Professionals Should Read This Guide on Value at Risk This article, "Value at Risk (VaR): Comprehensive Study Guide", is a must-read for CFA candidates, FRM aspirants, risk professionals, and banking professionals aiming to excel in risk management within capital markets. Here’s why it’s an essential resource: Foundational Knowledge: Gain a solid understanding of VaR as a core risk metric, crucial for analyzing market volatility and portfolio exposure in today's complex financial environment. Comprehensive Coverage: Explore VaR history, calculation methods (historical, parametric, Monte Carlo), conversions across horizons and confidence levels, limitations, and extensions like Expected Shortfall, providing a complete toolkit for risk analysis. Real-World Insights: Examine case studies from financial crises (LTCM, dot-com, 2008 GFC) and regulatory evolution (Basel Accords), highlighting how VaR has shaped banking practices and lessons from its failures. Risk Management Relevance: Learn practical applications in risk assessment, capital allocation, regulatory compliance, and performance evaluation, preparing you for roles in risk advisory, compliance, and banking operations. Practical Application: Bridge theory with CFA-style problems and solutions, from single-asset calculations to multi-asset portfolios, equipping you to apply VaR in real scenarios. Dive into this guide to transform theoretical risk concepts into actionable skills for a successful career in risk management and banking!