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on Risk Management |
| By: | Ariston Karagiorgis (Athens University of Economics and Business); Dimitrios Anastasiou (Athens University of Economics and Business - Department of Business Administration); Konstantinos Drakos (Athens University of Economics and Business - Department of Accounting and Finance); Steven Ongena (University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)) |
| Abstract: | Using an extensive matched hedge fund-prime broker panel dataset for the period 2001-2021, we document a strong positive relationship between hedge fund leverage and prime broker's stock price crash risk after controlling for other crash risk drivers. Our results are not only statistically, but also economically significant, showing that a one-standard-deviation increase in hedge fund leverage is associated on average with an increase of around 5% of a standard deviation in the negative skewness or the down-to-up-volatility of bank stock returns. Moreover, they remain robust when accounting for endogeneity and conducting many robustness checks. We also document that some investment strategies, such as one focusing on fixed income, appear to decrease the slope of the risk metrics of prime brokers, and ultimately leading to lower stock price crash risk. |
| Keywords: | Hedge Funds, Leverage, Prime Broker, Price Crash Risk |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2557 |
| By: | Eduardo Abi Jaber (CMAP - Centre de Mathématiques Appliquées de l'Ecole polytechnique - Inria - Institut National de Recherche en Informatique et en Automatique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique); Louis-Amand Gérard (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We investigate the use of path signatures in a machine learning context for hedging exotic derivatives under non-Markovian stochastic volatility models. In a deep learning setting, we use signatures as features in feedforward neural networks and show that they outperform LSTMs in most cases, with orders of magnitude less training compute. In a shallow learning setting, we compare two regression approaches: the first directly learns the hedging strategy from the expected signature of the price process; the second models the dynamics of volatility using a signature volatility model, calibrated on the expected signature of the volatility. Solving the hedging problem in the calibrated signature volatility model yields more accurate and stable results across different payoffs and volatility dynamics. |
| Keywords: | Deep-hedging, non-Markovian stochastic volatility models, path-signatures, exotic derivatives, Fourier methods |
| Date: | 2025–08–03 |
| URL: | https://d.repec.org/n?u=RePEc:hal:cesptp:hal-05197836 |
| By: | Gauch, Kevin; Quick, Reiner |
| Abstract: | Organizations face severe cyber risks, which may lead companies to contract related insurance or to demand cybersecurity assurance services to signal risk management. This paper experimentally investigates how cybersecurity assurance and insurance against cyber risks impact nonprofessional investors. We conducted an experiment with a 2 × 2 between‐subjects design with 100 UK nonprofessional investors and manipulated the assurance provision and insurance purchase to analyze their impact on willingness to invest. Our results suggest that cybersecurity assurance and cyber risk insurance positively affect willingness to invest. The results confirm the usefulness of measures to handle cyber risks and are of interest to managers, auditors, regulators, and academics. |
| Date: | 2025–07–28 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:156027 |
| By: | Abdullah Karasan; Ozge Sezgin Alp; Gerhard-Wilhelm Weber |
| Abstract: | In this study, we propose a novel machine-learning-based measure for stock price crash risk, utilizing the minimum covariance determinant methodology. Employing this newly introduced dependent variable, we predict stock price crash risk through cross-sectional regression analysis. The findings confirm that the proposed method effectively captures stock price crash risk, with the model demonstrating strong performance in terms of both statistical significance and economic relevance. Furthermore, leveraging a newly developed firm-specific investor sentiment index, the analysis identifies a positive correlation between stock price crash risk and firm-specific investor sentiment. Specifically, higher levels of sentiment are associated with an increased likelihood of stock price crash risk. This relationship remains robust across different firm sizes and when using the detoned version of the firm-specific investor sentiment index, further validating the reliability of the proposed approach. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.16287 |
| By: | Arslan Ahmad; Ian Dobson |
| Abstract: | Resilience risk metrics must address the customer cost of the largest blackouts of greatest impact. However, there are huge variations in blackout cost in observed distribution utility data that make it impractical to properly estimate the mean large blackout cost and the corresponding risk. These problems are caused by the heavy tail observed in the distribution of customer costs. To solve these problems, we propose resilience metrics that describe large blackout risk using the mean of the logarithm of the cost of large-cost blackouts, the slope index of the heavy tail, and the frequency of large-cost blackouts. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.12016 |
| By: | Aslanidis, Nektarios; Bariviera, Aurelio; Kapetanios, George; Sarafidis, Vasilis |
| Abstract: | This paper analyzes realized return behavior across a broad set of crypto assets by estimating heterogeneous exposures to idiosyncratic and systematic risk. A key challenge arises from the latent nature of broader economy-wide risk sources: macro-financial proxies are unavailable at high-frequencies, while the abundance of low-frequency candidates offers limited guidance on empirical relevance. To address this, we develop a two-stage ``divide-and-conquer'' approach. The first stage estimates exposures to high-frequency idiosyncratic and market risk only, using asset-level IV regressions. The second stage identifies latent economy-wide factors by extracting the leading principal component from the model residuals and mapping it to lower-frequency macro-financial uncertainty and sentiment-based indicators via high-dimensional variable selection. Structured patterns of heterogeneity in exposures are uncovered using Mean Group estimators across asset categories. The method is applied to a broad sample of crypto assets, covering more than 80% of total market capitalization. We document short-term mean reversion and significant average exposures to idiosyncratic volatility and illiquidity. Green and DeFi assets are, on average, more exposed to market-level and economy-wide risk than their non-Green and non-DeFi counterparts. By contrast, stablecoins are less exposed to idiosyncratic, market-level, and economy-wide risk factors relative to non-stablecoins. At a conceptual level, our study develops a coherent framework for isolating distinct layers of risk in crypto markets. Empirically, it sheds light on how return sensitivities vary across digital asset categories -- insights that are important for both portfolio design and regulatory oversight. |
| Keywords: | Idiosyncratic and systematic risk; divide and conquer; heterogeneous exposures; endogeneity; IV estimation; high-dimensional analysis; multiple testing boosting; principal components; stablecoins; green assets; defi assets |
| JEL: | C23 C33 C44 C55 C58 G10 G11 |
| Date: | 2025–06–25 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125124 |
| By: | Olivier Lopez (CREST); Daniel Nkameni (CREST) |
| Abstract: | Index insurance is often proposed to reduce protection gaps, especially for emerging risks. Unlike traditional insurance, it bases compensation on a measurable index, enabling faster payouts and lower claim management costs. This approach benefits both policyholders, through quick payments, and insurers, through reduced costs and better risk control due to reliable data and robust statistical estimates. An important difference with the concept of Cat Bonds is that the feasibility of such coverage relies on the possibility of mutualization. Mutualization, in turn, is achieved only if a sufficiently high number of policyholders agree to subscribe. The purpose of this paper is to introduce a model for the demand for index insurance and to provide conditions under which the solvency of the portfolio is achieved. From these conditions, we deduce a product that combines index and traditional indemnity insurance in order to benefit from the best of both approaches. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.18240 |
| By: | Mínguez Solana, Roberto; Díaz Cachinero, Pablo |
| Abstract: | Chance-constrained optimization (CCO) offers exact control of failure probabilities but becomes numerically prohibitive for large scenario sets. The buffered failure probability, also known as the Conditional Value-at-Risk (CVaR), is convex and therefore tractable, but it typically leads to overly conservative designs. We introduce a new formulation, the CVaR-Chance-Constraint (CVaR-CC), which preserves the probabilistic guarantee of CCO while leveraging the convex-analytic structure of the superquantile. We develop three scalable algorithms: (i) a secant root-finding scheme that iteratively adjusts the CVaR right-hand side until the chance constraint is met; (ii) a unit-slope quasi-Newton iteration whose local convergence holds under mild assumptions; and (iii) an active-set procedure that retains only tail scenarios, shrinking the master problem and accelerating convergence for very large scenario sets. For each algorithm we establish convergence and provide explicit sufficient conditions. Numerical experiments on illustrative examples and energy-portfolio benchmarks show that CVaR-CC attains the required reliability with objective values close to the CCO solution while solving up to an order of magnitude faster than mixed-integer state-of-the-art methods. The framework reconciles risk fidelity with computational efficiency, enabling chance-constrained design in large, data-driven applications. |
| Keywords: | Stochastic programming; Chance constraints; CVaR; Risk aversion; Secant and quasi-Newton methods; Bundle algorithms |
| Date: | 2025–07–29 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:47686 |
| By: | Erdinc Akyildirim (University of Nottingham); Gamze Ozturk Danisman (Istanbul Bilgi University); Steven Ongena (University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)) |
| Abstract: | Using a panel of 929 U.S. publicly listed firms, this paper investigates the impact of artificial intelligence (AI) employment on the disclosure of political risk in corporate earnings calls. We utilize the firm-level AI employment measure developed by Babina et al. (2024), based on resume and job posting records. Furthermore, we supplement it with our newly generated AI disclosure indices at the firm level, created through textual analysis of earnings call transcripts. Our findings indicate that firms with greater AI employment are significantly less likely to disclose information about political risk during earnings calls. We propose a dual mechanism that underpins this association. First, AI enables narrative management: firms use AI tools to strategically alter the tone and wording of disclosures, avoiding phrases that may elicit unfavorable sentiment, leading to a reduction in reputational risk. Second, AI improves firms’ internal performance and risk management, hence reducing the need for voluntary political risk disclosures. Our findings add to the literature on voluntary disclosure and the economic implications of AI by indicating that AI, as a general-purpose technology, has unintended consequences for corporate transparency. |
| Keywords: | Artificial Intelligence (AI), political risk, voluntary disclosures, earnings calls, textual analysis, AI disclosure index |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2556 |
| By: | Olkhov, Victor |
| Abstract: | We consider the investor who doesn’t trade shares of his portfolio. The investor only observes the current trades made in the market with his securities to estimate the current return, variance, and risks of his unchanged portfolio. We show how the time series of consecutive trades made in the market with the securities of the portfolio can determine the time series that model the trades with the portfolio as with a single security. That establishes the equal description of the market-based variance of the securities and of the portfolio composed of these securities that account for the fluctuations of the volumes of the consecutive trades. We show that Markowitz’s (1952) variance describes only the approximation when all volumes of the consecutive trades with securities are assumed constant. The market-based variance depends on the coefficient of variation of fluctuations of volumes of trades. To emphasize this dependence and to estimate possible deviation from Markowitz variance, we derive the Taylor series of the market-based variance up to the 2nd term by the coefficient of variation, taking Markowitz variance as a zero approximation. We consider three limiting cases with low and high fluctuations of the portfolio returns, and with a zero covariance of trade values and volumes and show that the impact of the coefficient of variation of trade volume fluctuations can cause Markowitz’s assessment to highly undervalue or overestimate the market-based variance of the portfolio. Incorrect assessments of the variances of securities and of the portfolio cause wrong risk estimates, disturb optimal portfolio selection, and result in unexpected losses. The major investors, portfolio managers, and developers of macroeconomic models like BlackRock, JP Morgan, and the U.S. Fed should use market-based variance to adjust their predictions to the randomness of market trades. |
| Keywords: | portfolio variance; portfolio theory; random market trades |
| JEL: | G11 G15 G17 G23 G24 |
| Date: | 2025–07–29 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125508 |
| By: | Martha Bernate-Valbuena (Department of Economy, Accounting and Finance, University of Monterrey, México); Begoña Gutiérrez (2Department of Accounting and Finance, Universidad de Zaragoza, Spain, School of Economics and Business) |
| Abstract: | This study examines whether earnings management indicators, which highlight unjustified variations in accounting items, can predict business bankruptcy. Using data from 179, 559 Spanish firms, from 2009 to 2014, both traditional financial ratios and earnings management indicators were analyzed. Significant differences between failed and non-failed firms were observed years before bankruptcy. To ensure robustness, a test sample from a future period validated the findings. Logistic regression revealed that certain earnings management indicators, particularly a synthetic index combining multiple indicators, can predict bankruptcy. Such indexes could enhance bankruptcy prediction models, offering valuable insights for assessing financial health and potential risks in businesses. |
| Keywords: | Bankruptcy, financial ratios, earnings management, creative accounting |
| JEL: | G33 G53 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:zar:wpaper:dt2025-01 |
| By: | Dillenberger, David; Gottlieb, Daniel; Ortoleva, Pietro |
| Abstract: | We study how the separation of time and risk preferences relates to a property called Stochastic Impatience. We show that, within a broad class of models, Stochastic Impatience holds if and only if risk aversion and the inverse elasticity of intertemporal substitution are sufficiently close. In the models of Epstein and Zin (1989) and Hansen and Sargent (1995), Stochastic Impatience is violated for commonly used parameters. Our result also provides a simple, one-question test for the separation of time and risk preferences. |
| Keywords: | stochastic impatience; Epstein-Zin preferences; separation of time and risk preferences; risk sensitive preferences; non-expected utility |
| JEL: | D81 D90 G11 |
| Date: | 2025–07–31 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:125994 |
| By: | Kwasniok, Sascha |
| Abstract: | Loss of the ability to work is a risk that threatens financial existence, which is often not adequately covered by state benefits. Although in many countries exists a market for occupational disability insurance, private provision is moderate. This study uses Germany as an example to examine the factors that influence consumer demand for private disability insurance from a behavioral perspective. A qualitative-quantitative approach is employed. Based on the Theory of Planned Behavior, semi-structured interviews among insurance sales people are conducted to identify additional factors that are relevant to insurance demand in practice. These factors include perceived usefulness and perceived risk of purchasing private disability insurance, as well as knowledge of disability insurance. The developed research model is then tested quantitatively using a structural equation model. Therefore, data from 323 consumer were collected through an online questionnaire. The results confirm the suitability of the Theory of Planned Behavior for explaining demand for private disability insurance. Of the extended factors derived from the qualitative part, disability insurance knowledge is highly relevant to the consumer purchase decision. These findings can be used to further theorizing in the field of insurance demand behavior. Furthermore, they also provide practical recommendations for increasing demand for disability insurance and thus improving coverage of existential risks. |
| Keywords: | Theory of planned behavior, insurance literacy, SmartPLS, Importance-performance map analysis (IPMA), expert interview, mixed method |
| JEL: | G22 G52 G53 G41 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:321819 |
| By: | Patrick Chan; Ronnie Sircar; Iosif Zimbidis |
| Abstract: | We consider a dynamic portfolio optimization problem that incorporates predictable returns, instantaneous transaction costs, price impact, and stochastic volatility, extending the classical results of Garleanu and Pedersen (2013), which assume constant volatility. Constructing the optimal portfolio strategy in this general setting is challenging due to the nonlinear nature of the resulting Hamilton-Jacobi-Bellman (HJB) equations. To address this, we propose a multi-scale volatility expansion that captures stochastic volatility dynamics across different time scales. Specifically, the analysis involves a singular perturbation for the fast mean-reverting volatility factor and a regular perturbation for the slow-moving factor. We also introduce an approximation for small price impact and demonstrate its numerical accuracy. We formally derive asymptotic approximations up to second order and use Monte Carlo simulations to show how incorporating these corrections improves the Profit and Loss (PnL) of the resulting portfolio strategy. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.17162 |
| By: | van der Kwaak, Christiaan (University of Groningen) |
| Abstract: | In this paper, we study the long-run impact of the central bank lending at low-interest rates to banks in times of financial crisis. While the provision of such funding mitigates the impact of financial crises ex post, we find that it increases bank risk taking ex ante, and therefore increases the likelihood of financial crises. Despite more frequent crises, however, the long-run impact on the macroeconomy is beneficial, as the positive effect from low interest-rate funding mitigates the contraction of credit at the height of a crisis. The long-run impact on the macroeconomy, however, is quantitatively small. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:gro:rugfeb:2024014-eef |
| By: | Olivier Lopez (CREST); Daniel Nkameni (CREST) |
| Abstract: | In this paper, we consider the question of providing insurance protection against heavy tail losses, where the expectation of the loss may not even be finite. The product we study is based on a combination of traditional insurance up to some limit, and a parametric (or index-based) cover for larger losses. This second part of the cover is computed from covariates available just after the claim, allowing to reduce the claim management costs via an instant compensation. To optimize the design of this second part of the product, we use a criterion which is adapted to extreme losses (that is distribution of the losses that are of Pareto type). We support the calibration procedure by theoretical results that show its convergence rate, and empirical results from a simulation study and a real data analysis on tornados in the US. We conclude our study by empirically demonstrating that the proposed hybrid contract outperforms a traditional capped indemnity contract. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.18207 |
| By: | Andrew L. Allan; Anna P. Kwossek; Chong Liu; David J. Pr\"omel |
| Abstract: | Based on the theory of c\`adl\`ag rough paths, we develop a pathwise approach to analyze stability and approximation properties of portfolios along individual price trajectories generated by standard models of financial markets. As a prototypical example from portfolio theory, we study the log-optimal portfolio in a classical investment-consumption optimization problem on a frictionless financial market modelled by an It\^o diffusion process. We identify a fully deterministic framework that enables a pathwise construction of the log-optimal portfolio, for which we then establish pathwise stability estimates with respect to the underlying model parameters. We also derive pathwise error estimates arising from the time-discretization of the log-optimal portfolio and its associated capital process. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.18232 |
| By: | Salvatore Federico; Andrea Modena; Luca Regis |
| Abstract: | In this paper, we examine how dividend taxes (and bans) and capital requirements that vary with the state of the economy influence a bank’s optimal capital buffers and shareholder value. In the model, the bank distributes dividends and issues costly equity to maximise shareholder value, while its assets generate stochastic income under time varying macroeconomic conditions. We solve the bank’s stochastic control problem and derive the distribution of its capital buffers in closed form. Imposing dividend taxes (or bans) in bad macroeconomic states generates an intertemporal trade-off, as it encourages capital buffers accumulation in those states but promotes dividend payouts in the good ones. Furthermore, the policy undermines financial stability by reducing the bank’s value and weakening its incentives to recapitalise in both good and bad states. Coordinating dividend taxes with counter-cyclical capital requirements can mitigate value losses and ease the trade-off, but it also exacerbates disincentives for recapitalisation. |
| Keywords: | Capital requirements; dividend bans; dividend taxes; policy coordination; stochastic optimal control |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cca:wpaper:746 |
| By: | Fabian Alex |
| Abstract: | In the SRI-augmented version of the Arrow-Debreu-model by Arnold (2023), the restriction to no risk of bankruptcy is immaterial. Furthermore, shareholder unanimity is still valid when a firm’s bond issuance (viz., its leverage) is chosen endogenously. The debt-equity-ratio of firms may not only be set arbitrarily (independent of their capital choice) with respect to shareholder value, but also to entire budget sets, implying an economy-wide Modigliani-Miller type of irrelevance given market completeness. If SRI leads individuals to constrain the set of assets they are prepared to buy and, thus, reduces their personal marketed subspace, over-indebtedness may restore it. |
| Keywords: | socially responsible investing, general equilibrium, complete markets, Modigliani-Miller, asset pricing, Arrow-Debreu |
| JEL: | D51 D52 D53 G12 G33 M14 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:bav:wpaper:241_alex.rdf |
| By: | USDA Risk Management Agency |
| Abstract: | [Contents:] Defining Crop Insurance --- Why Purchase Crop Insurance? --- How to Purchase Crop Insurance --- Benefits Available for Beginning/Veteran Farmers and Ranchers --- Cost of Crop Insurance? --- Whole Farm Revenue Protection and Micro Farm --- Key Terms in Crop Insurance --- Key Dates in Crop Insurance --- Connecting with RMA --- Other USDA Resources. |
| Keywords: | Agricultural and Food Policy, Crop Production/Industries, Risk and Uncertainty |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:ags:usdami:364292 |
| By: | Gabor Petnehazi; Laith Al Shaggah; Jozsef Gall; Bernadett Aradi |
| Abstract: | This study explores the potential of zero-shot time series forecasting, an innovative approach leveraging pre-trained foundation models, to forecast mortality rates without task-specific fine-tuning. We evaluate two state-of-the-art foundation models, TimesFM and CHRONOS, alongside traditional and machine learning-based methods across three forecasting horizons (5, 10, and 20 years) using data from 50 countries and 111 age groups. In our investigations, zero-shot models showed varying results: while CHRONOS delivered competitive shorter-term forecasts, outperforming traditional methods like ARIMA and the Lee-Carter model, TimesFM consistently underperformed. Fine-tuning CHRONOS on mortality data significantly improved long-term accuracy. A Random Forest model, trained on mortality data, achieved the best overall performance. These findings underscore the potential of zero-shot forecasting while highlighting the need for careful model selection and domain-specific adaptation. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.13521 |