|
on Risk Management |
| By: | Fekria Belhouichet; Guglielmo Maria Caporale; Luis Alberiko Gil-Alana |
| Abstract: | This paper applies the R² connectedness method proposed by Balli et al. (2023) to analyse contemporaneous and lagged connectedness between returns on several asset classes (sector ETFs, Bitcoin, stock market indices, Brent crude oil) over the period 1 January 2023 – 22 September 2025, in the presence of heightened geopolitical risk. The results indicate that contemporaneous effects dominate over lagged ones. Specifically, the Nikkei 225, the STOXX 600, and Brent oil act as net risk receivers, while Bitcoin plays a limited role as a safe haven. Conversely, the S&P 500 index appears to be the main shock emitter, followed by the Defence (ITA) and Technology (XLK) ETFs, while the Energy (XLE) ETF seems to be particularly exposed to risk. These findings provide valuable information to policymakers responsible for financial stability and to investors seeking effective portfolio diversification and hedging strategies, especially during periods of market turbulence. |
| Keywords: | contemporaneous and lagged R2 connectedness, Thematic ETFs, Brent oil, S&P 500, STOXX 600, Nikkei 225, geopolitical risk |
| JEL: | C32 G11 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12225 |
| By: | Elisa Alos; Frido Rolloos; Kenichiro Shiraya |
| Abstract: | The covariance between the return of an asset and its realized volatility can be approximated as the difference between two specific implied volatilities. In this paper it is proved that in the small time-to-maturity limit the approximation error tends to zero. In addition a direct relation between the short time-to-maturity covariance and slope of the at-the-money implied volatility is established. The limit theorems are valid for stochastic volatility models with Hurst parameter $H \in(0, 1)$. An application of the results is to accurately approximate the Hurst parameter using only a discrete set of implied volatilities. Numerical examples under the rough Bergomi model are presented. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.26310 |
| By: | Peng Liu; Alexander Schied |
| Abstract: | In this paper, we investigate the Lambda Value-at-Risk ($\Lambda$VaR) under ambiguity, where the ambiguity is represented by a family of probability measures. We establish that for increasing Lambda functions, the robust (i.e., worst-case) $\Lambda$VaR under such an ambiguity set is equivalent to $\Lambda$VaR computed with respect to a capacity, a novel extension in the literature. This framework unifies and extends both traditional $\Lambda$VaR and Choquet quantiles (Value-at-Risk under ambiguity). We analyze the fundamental properties of this extended risk measure and establish a novel equivalent representation for $\Lambda$VaR under capacities with monotone Lambda functions in terms of families of downsets. Moreover, explicit formulas are derived for robust $\Lambda$VaR when ambiguity sets are characterized by $\phi$-divergence and the likelihood ratio constraints, respectively. We further explore the applications in risk sharing among multiple agents. We demonstrate that the family of risk measures induced by families of downsets is closed under inf-convolution. In particular, we prove that the inf-convolution of $\Lambda$VaR with capacities and monotone Lambda functions is another$\Lambda$VaR under a capacity. The explicit forms of optimal allocations are also derived. Moreover, we obtain more explicit results for risk sharing under ambiguity sets characterized by $\phi$-divergence and likelihood ratio constraints. Finally, we explore comonotonic risk-sharing for $\Lambda$VaR under ambiguity. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.00717 |
| By: | Dmitry Lesnik; Tobias Schaefer |
| Abstract: | Machine learning models used for high-stakes predictions in domains like credit risk face critical degradation due to concept drift, requiring robust and transparent adaptation mechanisms. We propose an architecture, where a dedicated correction layer is employed to efficiently capture systematic shifts in predictive scores when a model becomes outdated. The key element of this architecture is the design of a correction layer using Probabilistic Rule Models (PRMs) based on Markov Logic Networks, which guarantees intrinsic interpretability through symbolic, auditable rules. This structure transforms the correction layer from a simple scoring mechanism into a powerful diagnostic tool capable of isolating and explaining the fundamental changes in borrower riskiness. We illustrate this diagnostic capability using Fannie Mae mortgage data, demonstrating how the interpretable rules extracted by the correction layer successfully explain the structural impact of the 2008 financial crisis on specific population segments, providing essential insights for portfolio risk management and regulatory compliance. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.26627 |
| By: | Whelan, Karl |
| Abstract: | We analyze sequential investment strategies that expose capital to asymmetric payoff structures--scenarios in which losses are small and frequent, while gains are large but rare. This setup generalizes the classical gambler's ruin problem, traditionally framed as a symmetric game, into a framework for modeling repeated financial decisions under uncertainty. Payoff asymmetry is common in domains such as venture capital, tail-risk hedging, and derivative strategies. We consider cases where each investment has positive, zero, or negative expected return, and derive analytic results for ruin probabilities, expected final wealth, and game duration. Our findings show that increasing asymmetry--higher potential rewards but lower success probability--raises the likelihood of ruin in positive-return settings and mitigates it when returns are negative. For zero-return strategies, we establish bounds on ruin probabilities and show that convergence to terminal outcomes is faster when payoffs are skewed. The results have implications for portfolio risk management and capital allocation in repeated-risk environments. |
| Keywords: | Gambler's Ruin, Asymmetric Risk, Stopping Problems |
| JEL: | C61 G11 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126349 |
| By: | Yuji Sakurai |
| Abstract: | This paper presents a comprehensive framework for determining haircuts on collateral used in central bank operations, quantifying residual uncollateralized exposures, and validating haircut models using machine learning. First, it introduces four haircut model types tailored to asset characteristics—marketable or non-marketable—and data availability. It proposes a novel model for setting haircuts in data-limited environment using a satallite cross-country model. Key principles guiding haircut calibration include non-procyclicality, data-drivenness, conservatism, and the avoidance of arbitrage gaps. The paper details model inputs such as Value-at-Risk (VaR) percentiles, volatility measures, and time to liquidation. Second, it proposes a quantitative framework for estimating expected uncollateralized exposures that remain after haircut application, emphasizing their importance in stress scenarios. Illustrative simulations using dynamic Nelson-Siegel yield curve models demonstrate how volatility impacts exposure. Third, the paper explores the use of Variational Autoencoders (VAEs) to simulate stress scenarios for bond yields. Trained on U.S. Treasury data, VAEs capture realistic yield curve distributions, offering an altenative tool for validating VaR-based haircuts. Although interpretability and explainability remain concerns, machine learning models enhance risk assessment by uncovering potential model vulnerabilities. |
| Keywords: | Haircuts; Uncollateralized Exposure; Machine Learning |
| Date: | 2025–10–31 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/225 |
| By: | Lala AlAsadi; Oluwasegun Bewaji; Aayush Gugnani; Tarush Gupta; Ronald Heijmans |
| Abstract: | This paper investigates the volatility dynamics of USD-backed stablecoins, challenging the assumption of inherent stability. Using a multi-level econometric framework, including GARCH, SVAR, and TVP-VAR models, we analyze how stablecoins respond to macro-financial shocks such as monetary policy changes, market uncertainty, and crypto volatility. Results show that USDC and TUSD are highly sensitive to external disturbances, while USDT and DAI remain relatively resilient. Stablecoins primarily absorb volatility but become more connected to systemic risk during crises. Frequency-domain analysis reveals short-term spillovers dominate during stress events, with long-term integration increasing post-2021. The findings highlight the heterogeneous nature of stablecoins and their growing ties to traditional finance, underscoring the need for tailored regulation and ongoing monitoring to mitigate systemic vulnerabilities. |
| Keywords: | stablecoins; volatility; financial markets; monetary policy |
| JEL: | F31 G14 E42 E58 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:dnb:dnbwpp:846 |
| By: | Nikolas Anic; Andrea Barbon; Ralf Seiz; Carlo Zarattini |
| Abstract: | This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to ChatGPT that inform the model when a stock is about to enter a momentum portfolio. The LLM evaluates whether recent news supports a continuation of past returns, producing scores that condition both stock selection and portfolio weights. An LLM-enhanced momentum strategy outperforms a standard long-only momentum benchmark, delivering higher Sharpe and Sortino ratios both in-sample and in a truly out-of-sample period after the model's pre-training cut-off. These gains are robust to transaction costs, prompt design, and portfolio constraints, and are strongest for concentrated, high-conviction portfolios. The results suggest that LLMs can serve as effective real-time interpreters of financial news, adding incremental value to established factor-based investment strategies. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.26228 |
| By: | Qiang Liu; Zhi Liu; Wang Zhou |
| Abstract: | We study the estimation of leverage effect and volatility of volatility by using high-frequency data with the presence of jumps. We first construct spot volatility estimator by using the empirical characteristic function of the high-frequency increments to deal with the effect of jumps, based on which the estimators of leverage effect and volatility of volatility are proposed. Compared with existing estimators, our method is valid under more general jumps, making it a better alternative for empirical applications. Under some mild conditions, the asymptotic normality of the estimators is established and consistent estimators of the limiting variances are proposed based on the estimation of volatility functionals. We conduct extensive simulation study to verify the theoretical results. The results demonstrate that our estimators have relative better performance than the existing ones, especially when the jump is of infinite variation. Besides, we apply our estimators to a real high-frequency dataset, which reveals nonzero leverage effect and volatility of volatility in the market. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.00944 |
| By: | Gerrit Meyerheim |
| Abstract: | This paper integrates tail aversion, implemented via a one-period entropic tilt, with rare disasters in a consumption-based asset pricing model with CRRA utility to jointly address the equity premium and risk-free rate puzzles. The model delivers closed-form expressions for the risk-free rate and asset moments, pushes out the Hansen-Jagannathan bound, implies a low risk-free rate via diffusion and disaster channels, and delivers natural upper and lower bounds of risk aversion. Calibrated to long-run return data and disciplined by disaster evidence, the model matches average returns, volatility, and a low real risk-free rate with very modest risk aversion. |
| Keywords: | equity premium puzzle, risk-free rate puzzle, rare disasters, entropic tilt, multiplier (KL) preferences, robust control, consumption-based asset pricing |
| JEL: | G12 E44 E43 E21 D81 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12231 |
| By: | Qiang Liu; Yiming Liu; Zhi Liu; Wang Zhou |
| Abstract: | In random matrix theory, the spectral distribution of the covariance matrix has been well studied under the large dimensional asymptotic regime when the dimensionality and the sample size tend to infinity at the same rate. However, most existing theories are built upon the assumption of independent and identically distributed samples, which may be violated in practice. For example, the observational data of continuous-time processes at discrete time points, namely, the high-frequency data. In this paper, we extend the classical spectral analysis for the covariance matrix in large dimensional random matrix to the spot volatility matrix by using the high-frequency data. We establish the first-order limiting spectral distribution and obtain a second-order result, that is, the central limit theorem for linear spectral statistics. Moreover, we apply the results to design some feasible tests for the spot volatility matrix, including the identity and sphericity tests. Simulation studies justify the finite sample performance of the test statistics and verify our established theory. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.02660 |
| By: | Andrea Modena; Luca Regis; Giorgio Rizzini |
| Abstract: | In this paper, we invesstigate how mortality risk affects agents optimal decisions and asset prices within a general equilibrium framework. In our model, risk averse households facing a stochastic mortality rate allocate their net worth among consumption, risky capital production, and risk-free bonds to maximise intertemporal utility. In this setting, we show that a negative and time-varying correlation exists between mortality and risky asset prices, even when production and mortality risks are mutually independent. The correlation arises because higher mortality rates reduce the incentive to save for the future, leading to increased current consumption and decreased capital investment. As a result, higher mortality lowers the prices of risky capital and raises the risk-free rate in equilibrium. Calibrated simulations suggest that endogenous price effects account for the largest share of welfare gains and losses following sharp changes in mortality, such as pandemics or rapid increases in longevity. |
| Keywords: | equilibrium; mortality risk; portfolio choice; stochastic optimal control |
| JEL: | C6 G11 G52 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2025_709 |
| By: | Aryan Ranjan |
| Abstract: | We present a systematic trading framework that forecasts short-horizon market risk, identifies its underlying drivers, and generates alpha using a hybrid machine learning ensemble built to trade on the resulting signal. The framework integrates neural networks with tree-based voting models to predict five-day drawdowns in the S&P 500 ETF, leveraging a cross-asset feature set spanning equities, fixed income, foreign exchange, commodities, and volatility markets. Interpretable feature attribution methods reveal the key macroeconomic and microstructural factors that differentiate high-risk (crash) from benign (non-crash) weekly regimes. Empirical results show a Sharpe ratio of 2.51 and an annualized CAPM alpha of +0.28, with a market beta of 0.51, indicating that the model delivers substantial systematic alpha with limited directional exposure during the 2005--2025 backtest period. Overall, the findings underscore the effectiveness of hybrid ensemble architectures in capturing nonlinear risk dynamics and identifying interpretable, potentially causal drivers, providing a robust blueprint for machine learning-driven alpha generation in systematic trading. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.22348 |
| By: | Hansj\"org Albrecher; Filip Lindskog; Herv\'e Zumbach |
| Abstract: | Cost-of-capital valuation is a well-established approach to the valuation of liabilities and is one of the cornerstones of current regulatory frameworks for the insurance industry. Standard cost-of-capital considerations typically rely on the assumption that the required buffer capital is held in risk-less one-year bonds. The aim of this work is to analyze the effects of allowing investments of the buffer capital in risky assets, e.g.~in a combination of stocks and bonds. In particular, we make precise how the decomposition of the buffer capital into contributions from policyholders and investors varies as the degree of riskiness of the investment increases, and highlight the role of limited liability in the case of heavy-tailed insurance risks. We present a combination of general theoretical results, explicit results for certain stochastic models and numerical results that emphasize the key findings. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.00895 |
| By: | Suzanne Jenkins; Reed Romanko |
| Abstract: | Developed in the 1980s during the Texas banking crisis, the Texas ratio offers an indicator of a bank’s health by measuring the institution’s troubled loans against its capital resources to absorb losses. |
| Keywords: | Texas ratio; troubled loans; bank capital; nonperforming loans |
| Date: | 2025–11–04 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00001:102041 |
| By: | Gabriel D. Patr\'on; Di Zhang; Lavinia M. P. Ghilardi; Evelin Blom; Maldon Goodridge; Erik Solis; Hamidreza Jahangir; Jorge Angarita; Nandhini Ganesan; Kevin West; Nilay Shah; Calvin Tsay |
| Abstract: | Energy storage can promote the integration of renewables by operating with charge and discharge policies that balance an intermittent power supply. This study investigates the scheduling of energy storage assets under energy price uncertainty, with a focus on electricity markets. A two-stage stochastic risk-constrained approach is employed, whereby electricity price trajectories or specific power markets are observed, allowing for recourse in the schedule. Conditional value-at-risk is used to quantify tail risk in the optimization problems; this allows for the explicit specification of a probabilistic risk limit. The proposed approach is tested in an integrated hydrogen system (IHS) and a battery energy storage system (BESS). In the joint design and operation context for the IHS, the risk constraint results in larger installed unit capacities, increasing capital cost but enabling more energy inventory to buffer price uncertainty. As shown in both case studies, there is an operational trade-off between risk and expected reward; this is reflected in higher expected costs (or lower expected profits) with increasing levels of risk aversion. Despite the decrease in expected reward, both systems exhibit substantial benefits of increasing risk aversion. This work provides a general method to address uncertainties in energy storage scheduling, allowing operators to input their level of risk tolerance on asset decisions. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.27528 |
| By: | Natalia Fabra (CEMFI, Centro de Estudios Monetarios y Financieros); Gerard Llobet (CEMFI, Centro de Estudios Monetarios y Financieros) |
| Abstract: | This paper investigates the implications of counterparty risk - stemming from potential defaults or renegotiations by buyers - on long-term contract markets. It develops a theoretical model highlighting how opportunistic buyer behavior leads to higher contract prices and underinvestment, potentially leading to the collapse of the contract market. The paper also evaluates public-policy interventions, including public subsidies, financial guarantees, regulator-backed contracts, and collateral requirements. While these measures can reduce price-related inefficiencies and promote investment, they involve trade-offs such as moral hazard or the reliance on costly public funds. These findings are particularly relevant for sectors with capital-intensive, long-lived assets exposed to price volatility, especially electricity markets, where underinvestment in renewable energy could delay the energy transition and hinder carbon-abatement goals. Simulations using data for the Spanish electricity market are used to quantify the theoretical predictions of the model. |
| Keywords: | Imperfect contract enforcement, counterparty risk, renewable investments, bilateral contracts, vertical integration, dynamic incentives. |
| JEL: | L13 L94 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:cmf:wpaper:wp2025_2523 |
| By: | Leonel Arango-Vasquez (EAFIT - EAFIT University) |
| Abstract: | This article examines the epistemology of finance by analyzing how different theoretical perspectives shape knowledge and decision-making in markets. A comparative analysis is adopted to contrast the underlying premises of each perspective and their applicability in contexts of uncertainty, volatility, and structural changes in markets. The findings demonstrate that, while each theoretical framework provides valuable analytical tools, their combination offers a more robust understanding of financial behavior. The integration of these approaches, along with interdisciplinary methodologies, is essential for a more holistic and adaptable view of financial epistemology, allowing for a more accurate evaluation of the interaction between rationality, uncertainty, and behavior in contemporary global markets. |
| Keywords: | portfolio diversification, market efficiency, behavioral finance, financial theory, interdisciplinary integration, financial epistemology |
| Date: | 2025–10–22 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05003891 |
| By: | Amir Ashour Novirdoust (EWI); Pia Hoffmann-Willers (EWI); Julian Keutz (EWI) |
| Abstract: | This paper develops an analytical model of sequential electricity markets in which renewable and conventional producers compete in two stages. Building on previous work, we introduce risk-averse renewable producers and distinguish between competitive and oligopolistic renewable producers. The model captures strategic bidding behavior under uncertainty in renewable production and limited flexibility of conventional producers in the second stage. Our results show that risk aversion amplifies strategic withholding in oligopolistic settings, thereby increasing the forward premium. This effect intensifies when conventional producers are less flexible. While risk aversion has no impact on welfare under perfect competition or when conventional producers are fully flexible, its interaction with market power and supply-side inflexibility generates welfare losses. In a heterogeneous market structure of renewable producers, competitive producers benefit from higher prices caused by the withholding of oligopolistic producers, particularly when those producers are risk-averse. |
| Keywords: | Sequential Markets; Strategic Bidding; Risk Aversion; Market Power; Renewable Energy |
| JEL: | D43 D81 L13 L94 Q21 |
| Date: | 2025–11–05 |
| URL: | https://d.repec.org/n?u=RePEc:ris:ewikln:021748 |