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on Risk Management |
| By: | Yuri Imamura; Takashi Kato |
| Abstract: | In this paper, we provide a new property of value at risk (VaR), which is a standard risk measure that is widely used in quantitative financial risk management. We show that the subadditivity of VaR for given loss random variables holds for any confidence level if and only if those are comonotonic. This result also gives a new equivalent condition for the comonotonicity of random vectors. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.12558 |
| By: | Anand Deo |
| Abstract: | Conditional Value-at-Risk (CVaR) is a risk measure widely used to quantify the impact of extreme losses. Owing to the lack of representative samples CVaR is sensitive to the tails of the underlying distribution. In order to combat this sensitivity, Distributionally Robust Optimization (DRO), which evaluates the worst-case CVaR measure over a set of plausible data distributions is often deployed. Unfortunately, an improper choice of the DRO formulation can lead to a severe underestimation of tail risk. This paper aims at leveraging extreme value theory to arrive at a DRO formulation which leads to representative worst-case CVaR evaluations in that the above pitfall is avoided while simultaneously, the worst case evaluation is not a gross over-estimate of the true CVaR. We demonstrate theoretically that even when there is paucity of samples in the tail of the distribution, our formulation is readily implementable from data, only requiring calibration of a single scalar parameter. We showcase that our formulation can be easily extended to provide robustness to tail risk in multivariate applications as well as in the evaluation of other commonly used risk measures. Numerical illustrations on synthetic and real-world data showcase the practical utility of our approach. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.16230 |
| By: | Yuting Su; Taizhong Hu; Zhenfeng Zou |
| Abstract: | The extreme cases of risk measures, when considered within the context of distributional ambiguity, provide significant guidance for practitioners specializing in risk management of quantitative finance and insurance. In contrast to the findings of preceding studies, we focus on the study of extreme-case risk measure under distributional ambiguity with the property of increasing failure rate (IFR). The extreme-case range Value-at-Risk under distributional uncertainty, consisting of given mean and/or variance of distributions with IFR, is provided. The specific characteristics of extreme-case distributions under these constraints have been characterized, a crucial step for numerical simulations. We then apply our main results to stop-loss and limited loss random variables under distributional uncertainty with IFR. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.23073 |
| By: | Evžen Kočenda; Peter Albrecht; Daniel Pastorek |
| Abstract: | We investigate the impact and propagation of geopolitical risk among oil-based energy commodities. First, we endogenously identify key geopolitical events affecting the connectedness among the oil-based commodities and then evaluate their transitory and persistent impacts. We identify four major shocks that resulted in persistent shifts in connectedness: the 9/11 attacks, the Crimea crisis, the political shift in Nigeria, and the Russian invasion of Ukraine. Using a quantile-based framework, we demonstrate that volatility transmissions due to geopolitical risk are not uniform but significantly depend on market conditions. Notably, heating oil and crude oil are identified as primary transmitters of risk, especially during economic turmoil. We quantify the negative economic and financial impacts of geopolitical risks through a multivariate dynamic portfolio analysis and through an impact on the profitability of ten global banks with high exposure to oil commodities. Our findings enhance the understanding of how geopolitical shocks influence connectedness and informed portfolio decisions, highlighting the need for adaptive strategies in finance. |
| Keywords: | geopolitical risk, extreme market conditions, oil-based energy commodities, volatility connectedness, transitory and persistent effects, portfolio composition and hedging, global banks with high exposure to oil |
| JEL: | C32 C58 G15 Q02 Q35 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12133 |
| By: | A. H. Nzokem |
| Abstract: | The cryptocurrency market presents both significant investment opportunities and higher risks relative to traditional financial assets. This study examines the tail behavior of daily returns for two leading cryptocurrencies, Bitcoin and Ethereum, using seven-parameter estimates from prior research, which applied the Generalized Tempered Stable (GTS) distribution. Quantile-quantile (Q-Q) plots against the Normal distribution reveal that both assets exhibit heavy-tailed return distributions. However, Ethereum consistently shows a greater frequency of extreme values than would be expected under its Bitcoin-modeled counterpart, indicating more pronounced tail risk. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01983 |
| By: | Crystal Rust |
| Abstract: | We introduce a new risk modeling framework where chaotic attractors shape the geometry of Bayesian inference. By combining heavy-tailed priors with Lorenz and Rossler dynamics, the models naturally generate volatility clustering, fat tails, and extreme events. We compare two complementary approaches: Model A, which emphasizes geometric stability, and Model B, which highlights rare bursts using Fibonacci diagnostics. Together, they provide a dual perspective for systemic risk analysis, linking Black Swan theory to practical tools for stress testing and volatility monitoring. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.08183 |
| By: | Friederike Niepmann; Leslie Sheng Shen |
| Abstract: | How do banks respond to geopolitical risk, and is this response distinct from other macroeconomic risks? Using U.S. supervisory data and new geopolitical risk indices, we show that banks reduce cross-border lending to countries with elevated geopolitical risk but continue lending to those markets through foreign affiliatesâ unlike their response to other macro risks. Furthermore, banks reduce domestic lending when geopolitical risk rises abroad, especially when they operate foreign affiliates. A simple banking model in which geopolitical shocks feature expropriation risk can explain these findings: Foreign funding through affiliates limits downside losses, making affiliate divestment less attractive and amplifying domestic spillovers. |
| Keywords: | Geopolitical risk; Bank lending; Credit risk; International spillovers |
| JEL: | F34 F36 G21 |
| Date: | 2025–08–27 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgif:1418 |
| By: | Xia Han; Liyuan Lin; Mengshi Zhao |
| Abstract: | The Diversification Quotient (DQ), introduced by Han et al. (2025), is a recently proposed measure of portfolio diversification that quantifies the reduction in a portfolio's risk-level parameter attributable to diversification. Grounded in a rigorous theoretical framework, DQ effectively captures heavy tails, common shocks, and enhances efficiency in portfolio optimization. This paper further explores the convergence properties and asymptotic normality of empirical DQ estimators based on Value at Risk (VaR) and Expected Shortfall (ES), with explicit calculation of the asymptotic variance. In contrast to the diversification ratio (DR) proposed by Tasche (2007), which may exhibit diverging asymptotic variance due to its lack of location invariance, the DQ estimators demonstrate greater robustness under various distributional settings. We further evaluate their performance under elliptical distributions and conduct a simulation study to examine their finite-sample behavior. The results offer a solid statistical foundation for the application of DQ in financial risk management and decision-making. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.20385 |
| By: | Adrian Iulian Cristescu; Matteo Giordano |
| Abstract: | Predicting the probability of default (PD) of prospective loans is a critical objective for financial institutions. In recent years, machine learning (ML) algorithms have achieved remarkable success across a wide variety of prediction tasks; yet, they remain relatively underutilised in credit risk analysis. This paper highlights the opportunities that ML algorithms offer to this field by comparing the performance of five predictive models-Random Forests, Decision Trees, XGBoost, Gradient Boosting and AdaBoost-to the predominantly used logistic regression, over a benchmark dataset from Scheule et al. (Credit Risk Analytics: The R Companion). Our findings underscore the strengths and weaknesses of each method, providing valuable insights into the most effective ML algorithms for PD prediction in the context of loan portfolios. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.19789 |
| By: | Guo, Hongfei; Marín Díazaraque, Juan Miguel; Veiga, Helena |
| Abstract: | Accurately forecasting volatility is central to risk management, portfolio allocation, and asset pricing. While high-frequency realised measures have been shown to improve predictive accuracy, their value is not uniform across markets or horizons. This paper introduces a class of Bayesian neural network stochastic volatility (NN-SV) models that combine the flexibility of machine learning with the structure of stochastic volatility models. The specifications incorporate realised variance, jump variation, and semivariance from daily and intraday data, and model uncertainty is addressed through a Bayesian stacking ensemble that adaptively aggregates predictive distributions. Using data from the DAX, FTSE 100, and S&P 500 indices, the models are evaluated against classical GARCH and parametric SV benchmarks. The results show that the predictive content of high-frequency measures is horizon- and market-specific. The Bayesian ensemble further enhances robustness by exploiting complementary model strengths. Overall, NN-SV models not only outperform established benchmarks in many settings but also provide new insights into market-specific drivers of volatility dynamics. |
| Keywords: | Ensemble forecasts; GARCH; Neural networks; Realised volatility; Stochastic volatility |
| JEL: | C11 C32 C45 C53 C58 |
| Date: | 2025–09–16 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:47944 |
| By: | Correia, Maria |
| Abstract: | This paper reviews the extensive literature on the predictive power of accounting information for bankruptcy. Prior research demonstrates that financial statement information effectively predicts bankruptcy out-of-sample, both independently and in combination with market data. I discuss several attributes of accounting information that may enhance or impair its utility in bankruptcy prediction. Using a comprehensive dataset of bankruptcies from 1980 to 2023, I analyse how the role of accounting information in credit risk assessment has evolved over the past four decades. My findings reveal that the predictive power of models based solely on accounting information has remained stable over the most recent decades, whereas the predictive power of equity market information has shown a modest increase. Notably, the performance of accounting and market-based models does not always align. In periods of declining market information efficacy, accounting information often remains robust, mitigating the impact on combined models. Conversely, market information frequently offsets reductions in the predictive power of accounting data, underscoring the complementary strengths of these information sources. |
| Keywords: | bankruptcy prediction; default; credit risk |
| JEL: | M40 |
| Date: | 2025–09–15 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128340 |
| By: | Fallou Niakh; Arthur Charpentier; Caroline Hillairet; Philipp Ratz |
| Abstract: | We consider an economy composed of different risk profile regions wishing to be hedged against a disaster risk using multi-region catastrophe insurance. Such catastrophic events inherently have a systemic component; we consider situations where the insurer faces a non-zero probability of insolvency. To protect the regions against the risk of the insurer's default, we introduce a public-private partnership between the government and the insurer. When a disaster generates losses exceeding the total capital of the insurer, the central government intervenes by implementing a taxation system to share the residual claims. In this study, we propose a theoretical framework for regional participation in collective risk-sharing through tax revenues by accounting for their disaster risk profiles and their economic status. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.18895 |
| By: | Feliks Ba\'nka (Warsaw University of Technology, Faculty of Electronics and Information Technology); Jaros{\l}aw A. Chudziak (Warsaw University of Technology) |
| Abstract: | In volatile financial markets, balancing risk and return remains a significant challenge. Traditional approaches often focus solely on equity allocation, overlooking the strategic advantages of options trading for dynamic risk hedging. This work presents DeltaHedge, a multi-agent framework that integrates options trading with AI-driven portfolio management. By combining advanced reinforcement learning techniques with an ensembled options-based hedging strategy, DeltaHedge enhances risk-adjusted returns and stabilizes portfolio performance across varying market conditions. Experimental results demonstrate that DeltaHedge outperforms traditional strategies and standalone models, underscoring its potential to transform practical portfolio management in complex financial environments. Building on these findings, this paper contributes to the fields of quantitative finance and AI-driven portfolio optimization by introducing a novel multi-agent system for integrating options trading strategies, addressing a gap in the existing literature. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.12753 |
| By: | Juselius, Mikael; Marques, Aurea Ponte; Tarashev, Nikola A. |
| Abstract: | In managing their capital, banks balance the risk of breaching regulatory requirements against the cost of maintaining and speedily restoring "management" buffers. Using 68 quarters of data on 17 US and 17 euro-area banks, we find systematic reductions in steady-state management buffer targets and attendant rises in regulatory risk tolerance (RRT) following the Great Financial Crisis (GFC). This phenomenon is particularly pronounced at banks with higher capital requirements post GFC. In parallel, banks facing more volatile management buffer shocks set higher management buffer targets, suggesting that RRT is a conscious choice. High-RRT banks tend to respond to a depletion of their management buffers by cutting lending, whereas low-RRT banks reduce the riskiness of their assets in other ways - thus highlighting real-economy effects of capital management strategies. |
| Keywords: | Capital management, Management buffer target, Speed of reversion, Regulatory regimes |
| JEL: | G21 G28 E51 G31 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bofrdp:325490 |
| By: | Son Ku Kim; Seung Joo Lee; Sheridan Titman |
| Abstract: | This paper studies the risk choices of a firm run by an effort and risk-averse manager, where the firm’s initial risk exposure is only observed by the manager. By eliminating zero NPV risk, hedging can improve the ability of firms to efficiently induce effort from their manager. We consider conditions under which information asymmetry about risk exposure alters the optimal compensation contract. In some settings, asymmetric information has no effect on the manager’s optimal compensation. However, in other settings, inducing the manager to hedge rather than speculate requires the optimal contract to directly account for hedgeable risk. When inducing the manager to hedge is sufficiently costly, the optimal contract may restrict the use of derivatives. |
| JEL: | D81 G3 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34211 |
| By: | Katia Colaneri; Alessandra Cretarola; Edoardo Lombardo; Daniele Mancinelli |
| Abstract: | We study the problem of designing and hedging unit-linked life policies whose benefits depend on an investment fund that incorporates environmental criteria in its selection process. Offering these products poses two key challenges: constructing a green investment fund and developing a hedging strategy for policies written on that fund. We address these two problems separately. First, we design a portfolio selection rule driven by firms' carbon intensity that endogenously selects assets and avoids ad hoc pre-screens based on ESG scores. The effectiveness of our new portfolio selection method is tested using real market data. Second, we adopt the perspective of an insurance company issuing unit-linked policies written on this fund. Such contracts are exposed to market, carbon, and mortality risk, which the insurer seeks to hedge. Due to market incompleteness, we address the hedging problem via a quadratic approach aimed at minimizing the tracking error. We also make a numerical analysis to assess the performance of the hedging strategy. For our simulation study, we use an efficient weak second-order scheme that allows for variance reduction. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.05676 |
| By: | Naftali Cohen |
| Abstract: | The CAPM regression is typically interpreted as if the market return contemporaneously \emph{causes} individual returns, motivating beta-neutral portfolios and factor attribution. For realized equity returns, however, this interpretation is inconsistent: a same-period arrow $R_{m, t} \to R_{i, t}$ conflicts with the fact that $R_m$ is itself a value-weighted aggregate of its constituents, unless $R_m$ is lagged or leave-one-out -- the ``aggregator contradiction.'' We formalize CAPM as a structural causal model and analyze the admissible three-node graphs linking an external driver $Z$, the market $R_m$, and an asset $R_i$. The empirically plausible baseline is a \emph{fork}, $Z \to \{R_m, R_i\}$, not $R_m \to R_i$. In this setting, OLS beta reflects not a causal transmission, but an attenuated proxy for how well $R_m$ captures the underlying driver $Z$. Consequently, ``beta-neutral'' portfolios can remain exposed to macro or sectoral shocks, and hedging on $R_m$ can import index-specific noise. Using stylized models and large-cap U.S.\ equity data, we show that contemporaneous betas act like proxies rather than mechanisms; any genuine market-to-stock channel, if at all, appears only at a lag and with modest economic significance. The practical message is clear: CAPM should be read as associational. Risk management and attribution should shift from fixed factor menus to explicitly declared causal paths, with ``alpha'' reserved for what remains invariant once those causal paths are explicitly blocked. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.05760 |
| By: | Matteo Foglia (Department of Economics and Finance, University of Bari Aldo Moro, Italy); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Petre Caraiani (Institute for Economic Forecasting, Romanian Academy; Bucharest University of Economics Studies); Vincenzo Pacelli (Ionian Department in ``Legal and Economic Systems of the Mediterranean: Society, Environment, Cultures", University of Bari Aldo Moro, Italy) |
| Abstract: | The objective of this paper is to analyze time-varying spillover between bubbles in oil and stock markets of the U.S. In this regard, we first use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium and long-term in the two markets. Then, in the second-step, we utilize a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model to conduct the spillover analysis among the indexes of oil and stock positive and negative bubbles. Based on data covering the monthly period of January 1999 to June 2025, we find that negative bubble spillovers are significantly stronger and more directional than positive ones, with the U.S. equity market emerging as the transmitter to the oil market post-2008. This represents a structural shift from the traditional oil-to-equity transmission paradigm. Moreover, spillover effects are most pronounced at short- and medium-term horizons, intensifying during crisis periods. Our findings suggest that oil is increasingly behaving as a financial asset rather than a physical commodity, with important implications for portfolio diversification and risk management. |
| Keywords: | Oil and Stock Markets, Multi-Scale Positive and Negative Bubbles, Time-Varying Spillover |
| JEL: | C22 C32 G10 Q41 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202534 |
| By: | Sebastian Fossati (University of Alberta); Xiao Li (University of Alberta) |
| Abstract: | We model the conditional distribution of future exchange rate returns for nine currencies as a function of real-time financial conditions. We show that the lower and upper quantiles of the exchange rate return distribution exhibit significant in-sample co-movement with financial conditions. Similarly, the conditional moments of the out-of-sample forecast display time-varying patterns, with the variance and kurtosis showing the most pronounced changes during and after the 2008-09 financial crisis. Deteriorating financial conditions are associated with an increase in volatility, particularly for commodity currencies. Overall, we conclude that financial conditions capture tail dependencies in exchange rate returns and contain valuable information for out-of-sample prediction. |
| Keywords: | exchange rates; financial conditions; NFCI; density forecasts |
| JEL: | C22 F31 G17 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:ris:albaec:021546 |
| By: | Peilin Rao; Randall R. Rojas |
| Abstract: | This paper provides robust, new evidence on the causal drivers of market troughs. We demonstrate that conclusions about these triggers are critically sensitive to model specification, moving beyond restrictive linear models with a flexible DML average partial effect causal machine learning framework. Our robust estimates identify the volatility of options-implied risk appetite and market liquidity as key causal drivers, relationships misrepresented or obscured by simpler models. These findings provide high-frequency empirical support for intermediary asset pricing theories. This causal analysis is enabled by a high-performance nowcasting model that accurately identifies capitulation events in real-time. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.05922 |
| By: | Jakub Growiec; Klaus Prettner |
| Abstract: | We investigate the salience of extinction risk as a source of impatience. Our framework distinguishes between human extinction risk and individual mortality risk while allowing for various degrees of intergenerational altruism. Additionally, we consider the evolutionarily motivated "selfish gene" perspective. We find that the risk of human extinction is an indispensable component of the discount rate, whereas individual mortality risk can be hedged against - partially or fully, depending on the setup - through human reproduction. Overall, we show that in the face of extinction risk, people become more impatient rather than more farsighted. Thus, the greater the threat of extinction, the less incentive there is to invest in avoiding it. Our framework can help explain why humanity consistently underinvests in mitigation of catastrophic risks, ranging from climate change mitigation, via pandemic prevention, to addressing the emerging risks of transformative artificial intelligence. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.04855 |
| By: | Niklas Ahlgren; Alexander Back; Timo Ter\"asvirta |
| Abstract: | We develop misspecification tests for building additive time-varying (ATV-)GARCH models. In the model, the volatility equation of the GARCH model is augmented by a deterministic time-varying intercept modeled as a linear combination of logistic transition functions. The intercept is specified by a sequence of tests, moving from specific to general. The first test is the test of the standard stationary GARCH model against an ATV-GARCH model with one transition. The alternative model is unidentified under the null hypothesis, which makes the usual LM test invalid. To overcome this problem, we use the standard method of approximating the transition function by a Taylor expansion around the null hypothesis. Testing proceeds until the first non-rejection. We investigate the small-sample properties of the tests in a comprehensive simulation study. An application to the VIX index indicates that the volatility of the index is not constant over time but begins a slow increase around the 2007-2008 financial crisis. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.23821 |
| By: | Jiwook Yoo |
| Abstract: | This article proposes a calibration framework for complex option pricing models that jointly fits market option prices and the term structure of variance. Calibrated models under the conventional objective function, the sum of squared errors in Black-Scholes implied volatilities, can produce model-implied variance term structures with large errors relative to those observed in the market and implied by option prices. I show that this can occur even when the model-implied volatility surface closely matches the volatility surface observed in the market. The proposed joint calibration addresses this issue by augmenting the conventional objective function with a penalty term for large deviations from the observed variance term structure. This augmented objective function features a hyperparameter that governs the relative weight placed on the volatility surface and the variance term structure. I test this framework on a jump-diffusion model with stochastic volatility in two calibration exercises: the first using volatility surfaces generated under a Bates model, and the second using a panel of S&P 500 equity index options covering the 1996-2023 period. I demonstrate that the proposed method is able to fit observed option prices well while delivering realistic term structures of variance. Finally, I provide guidance on the choice of hyperparameters based on the results of these numerical exercises. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.08096 |
| By: | Ruisi Li; Xinhui Gu |
| Abstract: | Propose a deep learning driven multi factor investment model optimization method for risk control. By constructing a deep learning model based on Long Short Term Memory (LSTM) and combining it with a multi factor investment model, we optimize factor selection and weight determination to enhance the model's adaptability and robustness to market changes. Empirical analysis shows that the LSTM model is significantly superior to the benchmark model in risk control indicators such as maximum retracement, Sharp ratio and value at risk (VaR), and shows strong adaptability and robustness in different market environments. Furthermore, the model is applied to the actual portfolio to optimize the asset allocation, which significantly improves the performance of the portfolio, provides investors with more scientific and accurate investment decision-making basis, and effectively balances the benefits and risks. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.00332 |
| By: | Matteo Buttarazzi; Tiziano De Angelis; Gabriele Stabile |
| Abstract: | This paper addresses the problem of determining the optimal time for an individual to convert retirement savings into a lifetime annuity. The individual invests their wealth into a dividend-paying fund that follows the dynamics of a geometric Brownian motion, exposing them to market risk. At the same time, they face an uncertain lifespan influenced by a stochastic mortality force. The latter is modelled as a piecewise deterministic Markov process (PDMP), which captures sudden and unpredictable changes in the individual's mortality force. The individual aims to maximise expected lifetime linear utility from consumption and bequest, balancing market risk and longevity risk under an irreversible, all-or-nothing annuitization decision. The problem is formulated as a three-dimensional optimal stopping problem and, by exploiting the PDMP structure, it is reduced to a sequence of nested one-dimensional problems. We solve the optimal stopping problem and find a rich structure for the optimal annuitization rule, which cover all parameter specifications. Our theoretical analysis is complemented by a numerical example illustrating the impact of a single health shock on annuitization timing, along with a sensitivity analysis of key model parameters. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.13091 |
| By: | Danielsson, Jon; Uthemann, Andreas |
| Abstract: | The rapid adoption of artificial intelligence (AI) poses new and poorly understood threats to financial stability. We use a game-theoretic model to analyse the stability impact of AI, finding that it amplifies existing financial system vulnerabilities — leverage, liquidity stress and opacity — through superior information processing, common data, speed and strategic complementarities. The consequence is crises become faster and more severe, where the likelihood of a crisis is directly affected by how effectively the authorities engage with AI. In response, we propose that the financial authorities develop their own AI systems and expertise, establish direct AI-to-AI communication, implement automated crisis facilities and monitor AI use. |
| Keywords: | crises; systemic risk; AI |
| JEL: | F3 G3 |
| Date: | 2025–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128657 |
| By: | Chen, Peng; Zhu, Jin; Zhu, Junxian; Wang, Xueqin |
| Abstract: | We consider the probabilistic simplex-constrained sparse recovery problem. The commonly used Lasso-type penalty for promoting sparsity is ineffective in this context since it is a constant within the simplex. Despite this challenge, fortunately, simplex constraint itself brings a self-regularization property, i.e., the empirical risk minimizer without any sparsity-promoting procedure obtains the usual Lasso-type estimation error. Moreover, we analyze the iterates of a projected gradient descent method and show its convergence to the ground truth sparse solution in the geometric rate until a satisfied statistical precision is attained. Although the estimation error is statistically optimal, the resulting solution is usually more dense than the sparse ground truth. To further sparsify the iterates, we propose a method called PERMITS via embedding a tail screening procedure, i.e., identifying negligible components and discarding them during iterations, into the projected gradient descent method. Furthermore, we combine tail screening and the special information criterion to balance the trade-off between fitness and complexity. Theoretically, the proposed PERMITS method can exactly recover the ground truth support set under mild conditions and thus obtain the oracle property. We demonstrate the statistical and computational efficiency of PERMITS with both synthetic and real data. The implementation of the proposed method can be found in https://github.com/abess-team/PERMITS. |
| Keywords: | projected gradient descent; self-regularization; simplex constrained sparse recovery; special information criterion; tail screening |
| JEL: | C1 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129540 |
| By: | Patrick J. Laub; Tu Pho; Bernard Wong |
| Abstract: | This paper introduces the Actuarial Neural Additive Model, an inherently interpretable deep learning model for general insurance pricing that offers fully transparent and interpretable results while retaining the strong predictive power of neural networks. This model assigns a dedicated neural network (or subnetwork) to each individual covariate and pairwise interaction term to independently learn its impact on the modeled output while implementing various architectural constraints to allow for essential interpretability (e.g. sparsity) and practical requirements (e.g. smoothness, monotonicity) in insurance applications. The development of our model is grounded in a solid foundation, where we establish a concrete definition of interpretability within the insurance context, complemented by a rigorous mathematical framework. Comparisons in terms of prediction accuracy are made with traditional actuarial and state-of-the-art machine learning methods using both synthetic and real insurance datasets. The results show that the proposed model outperforms other methods in most cases while offering complete transparency in its internal logic, underscoring the strong interpretability and predictive capability. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.08467 |
| By: | Hongyi Liu |
| Abstract: | We propose a new pseudo-Siamese Network for Asset Pricing (SNAP) model, based on deep learning approaches, for conditional asset pricing. Our model allows for the deep alpha, deep beta and deep factor risk premia conditional on high dimensional observable information of financial characteristics and macroeconomic states, while storing the long-term dependency of the informative features through long short-term memory network. We apply this method to monthly U.S. stock returns from 1970-2019 and find that our pseudo-SNAP model outperforms the benchmark approaches in terms of out-of-sample prediction and out-of-sample Sharpe ratio. In addition, we also apply our method to calculate deep mispricing errors which we use to construct an arbitrage portfolio K-Means clustering. We find that the arbitrage portfolio has significant alphas. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.04812 |
| By: | Guillaume Maitrier; Gr\'egoire Loeper; Jean-Philippe Bouchaud |
| Abstract: | This work extends and complements our previous theoretical paper on the subtle interplay between impact, order flow and volatility. In the present paper, we generate synthetic market data following the specification of that paper and show that the approximations made there are actually justified, which provides quantitative support our conclusion that price volatility can be fully explained by the superposition of correlated metaorders which all impact prices, on average, as a square-root of executed volume. One of the most striking predictions of our model is the structure of the correlation between generalized order flow and returns, which is observed empirically and reproduced using our synthetic market generator. Furthermore, we were able to construct proxy metaorders from our simulated order flow that reproduce the square-root law of market impact, lending further credence to the proposal made in Ref. [2] to measure the impact of real metaorders from tape data (i.e. anonymized trades), which was long thought to be impossible. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.05065 |
| By: | Wei Liang; Heng-fu Zou |
| Abstract: | We develop a new theory of asset pricing based on the concept of liberal ideas capital-a stock of intangible, ideational resources rooted in human dignity, individual liberty, free speech, constitutional democracy, property rights, and the rule of law. Building upon the philosophical foundations of John Locke, Thomas Jefferson, Alexis de Tocqueville, Lord Acton, Lud- wig von Mises, Friedrich Hayek, and Milton Friedman, and integrating recent contributions by McCloskey, Phelps, and Heng-Fu Zou, we construct a dynamic general equilibrium model in which liberal ideas capital plays a central role in driving productivity, institutional stability, and investor confidence. In this framework, countries that invest in and accu mulate liberal ideas capital experience lower transaction costs, more secure property rights, higher innovation, and thus, superior asset returns and lower volatility over time. Empirically, we assemble cross-country data on liberalism-related indices-freedom of speech, property rights, rule of law, press freedom, and democratic governance-and demonstrate that nations with higher levels of liberal ideas capital exhibit systematically higher long-term equity returns and stronger economic performance. In contrast, countries dominated by authoritarian ideas capital exhibit per sistent institutional fragility, lower risk-adjusted returns, and weaker in vestor protection. Our findings challenge conventional asset pricing mod els by revealing the deep ideational foundations of market behavior. We argue that liberal ideas form the cognitive and institutional prerequisite for asset pricing itself, making price discovery and risk assessment possible. This elevates the intergenerational transmission of these norms-a process of "soulcraft" - to the most critical form of capital investment for ensuring long-run financial stability and prosperity. The paper thus offers a unified theory of political economy, ideology, and fnance, concluding that a society's most valuable asset is the shared belief in liberty. |
| Date: | 2025–08–08 |
| URL: | https://d.repec.org/n?u=RePEc:cuf:wpaper:779 |
| By: | De Simone, Lisa; Giese, Henning; Koch, Reinald; Rehrl, Christoph |
| Abstract: | This study examines the real effects of earnings stripping rules introduced in the European Union in 2019, which tie interest deductibility to contemporaneous profitability. Exploiting a quasi-natural experiment created by the EU's harmonized implementation under the Anti-Tax Avoidance Directive and using a difference-in-differences design, we analyze consolidated data from 3, 312 firms across 22 EU Member States from 2012 to 2023. We find that earnings stripping rules significantly reduce operational risk-taking, investment, and innovation, consistent with profit-contingent deductibility lowering the expected debt tax shield in low-profit years. These effects are particularly pronounced among firms with high pre-reform operating risk, which also experience slower growth and a higher likelihood of financial distress following the reform. This study contributes to the literature on corporate taxation and risk-taking, showing that profit-linked interest limitations have real effects and underscoring the importance of rule design in balancing anti-avoidance objectives with investment and innovation. |
| Keywords: | corporate risk-taking, capital structure, asymmetric taxation, earnings stripping rule |
| JEL: | G32 G33 H25 H26 H87 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:arqudp:325832 |
| By: | Heng-fu Zou |
| Abstract: | This paper develops a unified institution-based asset pricing model in which rare disasters are reinterpreted as endogenous institutional breakdowns such as revolutions, regime collapses, legal erosion, and constitutional crises - rather than purely exogenous macroeconomic shocks. Building upon and extending the rare disaster literature of Rietz(1988), Barro (2006, 2009), and Gabaix (2012), we model institutional capital as a dynamic stochastic process subject to both continuous fluctuations and discontinuous collapses driven by Poisson jump processes. These institutional shocks directly impact the stochastic discount factor and hence the pricing of risky assets. We analytically derive the resulting equity premium and show that institutional volatility, erosion, and catastrophic regime transitions generate sizable and persistent risk premia even under standard CRRA preferences. Simulations using realistic institutional indi- cators for OECD and emerging economies demonstrate that our model explains not only the magnitude of equity premia, but also their time varia tion and cross-country heterogeneity. The framework unifies consumption based, production-based, and disaster-based asset pricing under the general concept of institutional capital, offering a comprehensive explanation for the equity premium puzzle and broader asset price movements under political uncertainty. We conclude by discussing implications for institutional reform and global financial stability. |
| Keywords: | Institutional capital, equity premium puzzle, rare disasters, stochastic discount factor, political risk, regime shifts, asset pricing, Poisson jumps, macro-finance, constitutional collapse, OECD economies |
| Date: | 2025–07–20 |
| URL: | https://d.repec.org/n?u=RePEc:cuf:wpaper:777 |
| By: | Peter Forsyth; Pieter van Staden; Yuying Li |
| Abstract: | We examine strategically incorporating broad stock market leveraged exchange-traded funds (LETFs) into investment portfolios. We demonstrate that easily understandable and implementable strategies can enhance the risk-return profile of a portfolio containing LETFs. Our analysis shows that seemingly reasonable investment strategies may result in undesirable Omega ratios, with these effects compounding across rebalancing periods. By contrast, relatively simple dynamic strategies that systematically de-risk the portfolio once gains are observed can exploit this compounding effect, taking advantage of favorable Omega ratio dynamics. Our findings suggest that LETFs represent a valuable tool for investors employing dynamic strategies, while confirming their well-documented unsuitability for passive or static approaches. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.19200 |
| By: | Yiran Wan; Xinyu Ying; Shengzhen Xu |
| Abstract: | Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multi-dimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer-DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5\% in terms of the average return excluding the crude oil market due to relatively low fluctuation. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.07987 |
| By: | Peter Ganong; Pascal J. Noel; Christina Patterson; Joseph S. Vavra; Alexander Weinberg |
| Abstract: | This paper uses high-frequency administrative data to show that the majority of U.S. workers experience substantial month-to-month fluctuations in pay, even within ongoing employment relationships. This earnings instability is pervasive, but it has been masked in past analysis of annual data. Moreover, this instability is unequally distributed: lower-income, hourly workers face more instability than higher-income, salaried workers. This is because earnings instability arises in large part from firm-driven fluctuations in hours. This earnings instability is a meaningful source of economic risk: we provide causal evidence that it increases consumption volatility and also leads to greater job separations, and we find that workers have a high willingness to pay to reduce earnings instability. These findings suggest that short-term earnings risk is a significant and previously underappreciated feature of the labor market. |
| JEL: | E21 J23 J31 J33 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34227 |
| By: | Matthew O. Jackson; Agathe Pernoud |
| Abstract: | We examine optimal regulation of financial networks with debt interdependencies between financial firms. We first characterize when it is firms have an incentive to choose excessively risky portfolios and overly correlate their portfolios with those of their counterparties. We then characterize how optimal regulation depends on a firm's financial centrality and its available investment opportunities. In standard core-periphery networks, optimal regulation depends non-monotonically on the correlation of banks' investments, with maximal restrictions for intermediate levels of correlation. Moreover, it can be uniquely optimal to treat banks asymmetrically: restricting the investments of one core bank while allowing an otherwise identical core bank (in all aspects, including network centrality) to invest freely. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.16648 |
| By: | Jimmy Risk; Shen-Ning Tung; Tai-Ho Wang |
| Abstract: | This paper presents a comprehensive study on the empirical dynamics of Uniswap v3 liquidity, which we model as a time-tick surface, $L_t(x)$. Using a combination of functional principal component analysis (FPCA) and dynamic factor methods, we analyze three distinct pools over multiple sample periods. Our findings offer three main contributions: a statistical characterization of automated market maker liquidity, an interpretable and portable basis for dimension reduction, and a robust analysis of liquidity dynamics using rolling window metrics. For the 5 bps pools, the leading empirical eigenfunctions explain the majority of cross-tick variation and remain stable, aligning closely with a low-order Legendre polynomial basis. This alignment provides a parsimonious and interpretable structure, similar to the dynamic Nelson-Siegel method for yield curves. The factor coefficients exhibit a time series structure well-captured by AR(1) models with clear GARCH-type heteroskedasticity and heavy-tailed innovations. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.05013 |