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on Risk Management |
| By: | Abiodun Finbarrs Oketunji |
| Abstract: | This research presents a framework for quantitative risk management in volatile markets, specifically focusing on expectile-based methodologies applied to the FTSE 100 index. Traditional risk measures such as Value-at-Risk (VaR) have demonstrated significant limitations during periods of market stress, as evidenced during the 2008 financial crisis and subsequent volatile periods. This study develops an advanced expectile-based framework that addresses the shortcomings of conventional quantile-based approaches by providing greater sensitivity to tail losses and improved stability in extreme market conditions. The research employs a dataset spanning two decades of FTSE 100 returns, incorporating periods of high volatility, market crashes, and recovery phases. Our methodology introduces novel mathematical formulations for expectile regression models, enhanced threshold determination techniques using time series analysis, and robust backtesting procedures. The empirical results demonstrate that expectile-based Value-at-Risk (EVaR) consistently outperforms traditional VaR measures across various confidence levels and market conditions. The framework exhibits superior performance during volatile periods, with reduced model risk and enhanced predictive accuracy. Furthermore, the study establishes practical implementation guidelines for financial institutions and provides evidence-based recommendations for regulatory compliance and portfolio management. The findings contribute significantly to the literature on financial risk management and offer practical tools for practitioners dealing with volatile market environments. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.13391 |
| By: | Luyun Lin; Yiqing Wang |
| Abstract: | The increasing development in the consumer credit card market brings substantial regulatory and risk management challenges. The advanced machine learning models applications bring concerns about model transparency and fairness for both financial institutions and regulatory departments. In this study, we evaluate the consistency of one commonly used Explainable AI (XAI) technology, SHAP, for variable explanation in credit card probability of default models via a case study about credit card default prediction. The study shows the consistency is related to the variable importance level and hence provides practical recommendation for credit risk management |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.01851 |
| By: | Sebastian Geissel; Christoph Knochenhauer |
| Abstract: | We propose a new class of monetary risk measures capable of assessing financial and ESG risk. The construction of these risk measures is based on an extension of classical shortfall risk measures in which the loss function is replaced by a multi-attribute utility function. We present an extensive theoretical analysis of these risk measures, showing in particular how properties of the utility function translate into properties of the associated risk measure. We furthermore discuss how these multi-attribute risk measures can be used to compute minimum risk portfolios and show in a numerical study that accounting for ESG risk in optimal portfolio choice has a significant influence on the composition of portfolios. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.23496 |
| By: | Davide Veronelli; Francesca Cibrario; Emanuele Dri; Valeria Zaffaroni; Giacomo Ranieri; Davide Corbelletto; Bartolomeo Montrucchio |
| Abstract: | The analysis of credit risk is crucial for the efficient operation of financial institutions. Quantum Amplitude Estimation (QAE) offers the potential for a quadratic speed-up over classical methods used to estimate metrics such as Value at Risk (VaR) and Conditional Value at Risk (CVaR). However, numerous limitations remain in efficiently scaling the implementation of quantum circuits that solve these estimation problems. One of the main challenges is the use of costly and restrictive arithmetic that must be implemented within the quantum circuit. In this paper, we propose using Quantum Singular Value Transformation (QSVT) to significantly reduce the cost of implementing the state preparation operator, which underlies QAE for credit risk analysis. We also present an end-to-end code implementation and the results of a simulation study to validate the proposed approach and demonstrate its benefits. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.19206 |
| By: | Lichen Wang; Shijia Hua; Yuyuan Liu; Zhengyuan Lu; Liang Zhang; Linjie Liu; Attila Szolnoki |
| Abstract: | The frequent occurrence of natural disasters has posed significant challenges to society, necessitating the urgent development of effective risk management strategies. From the early informal community-based risk sharing mechanisms to modern formal index insurance products, risk management tools have continuously evolved. Although index insurance provides an effective risk transfer mechanism in theory, it still faces the problems of basis risk and pricing in practice. At the same time, in the presence of informal community risk sharing mechanisms, the competitiveness of index insurance deserves further investigation. Here we propose a three-strategy evolutionary game model, which simultaneously examines the competitive relationship between formal index insurance purchasing (I), informal risk sharing strategies (S), and complete non-insurance (A). Furthermore, we introduce a method for calculating insurance company profits to aid in the optimal pricing of index insurance products. We find that basis risk and risk loss ratio have significant impacts on insurance adoption rate. Under scenarios with low basis risk and high loss ratios, index insurance is more popular; meanwhile, when the loss ratio is moderate, an informal risk sharing strategy is the preferred option. Conversely, when the loss ratio is low, individuals tend to forego any insurance. Furthermore, accurately assessing the degree of risk aversion and determining the appropriate ratio of risk sharing are crucial for predicting the future market sales of index insurance. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02684 |
| By: | Wanting He; Wenyuan Li; Yunran Wei |
| Abstract: | This paper introduces an innovative framework for the periodic evaluation of defined-contribution pension funds. The performance of the pension fund is evaluated not only at retirement, but also within the interim periods. In contrast to the traditional literature, we set the dynamic risk measure as the criterion and manage the tail risk of the pension fund dynamically. To effectively interact with the stochastic environment, a model-free reinforcement learning algorithm is proposed to search for optimal investment and insurance strategies. Using U.S. data, we calibrate pension members' mortality rates and enhance mortality projections through a Lee-Carter model. Our numerical results indicate that periodic evaluations lead to more risk-averse strategies, while mortality improvements encourage more risk-seeking behaviors. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.05241 |
| By: | Carlos Bouthelier-Madre; Carlos Escudero |
| Abstract: | This work focuses on financial risks from a probabilistic point of view. The value of a firm is described as a geometric Brownian motion and default emerges as a first passage time event. On the technical side, the critical threshold that the value process has to cross to trigger the default is assumed to be an arbitrary continuous function, what constitutes a generalization of the classical Black-Cox model. Such a generality favors modeling a wide range of risk scenarios, including those characterized by strongly time-varying conditions; but at the same time limits the possibility of obtaining closed-form formulae. To avoid this limitation, we implement a qualitative classification of risk into three categories: high, medium, and low. They correspond, respectively, to a finite mean first passage time, to an almost surely finite first passage time with infinite mean, and to a positive probability of survival for all times. This allows for an extensive classification of risk based only on the asymptotic behavior of the default function, which generalizes previously known results that assumed this function to be an exponential. However, even within these mathematical conditions, such a classification is not exhaustive, as a consequence of the behavioral freedom that continuous functions enjoy. Overall, our results contribute to the design of credit risk classifications from analytical principles and, at the same time, constitute a call of attention on potential models of risk assessment in situations largely affected by time evolution. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.08101 |
| By: | Djibril Gueye; Alejandra Quintos |
| Abstract: | We propose a multivariate framework for modeling dependent default times that extends the classical Cox process by incorporating both common and idiosyncratic shocks. Our construction uses c\`adl\`ag, increasing processes to model cumulative intensities, relaxing the requirement of absolutely continuous compensators. Analytical tractability is preserved through the multiplicative decomposition of Az\'ema supermartingales under assumptions that guarantee deterministic compensators. The framework captures a wide range of dependence structures and allows for both simultaneous and non-simultaneous defaults. We derive closed-form expressions for joint survival probabilities and illustrate the flexibility of the model through examples based on L\'evy subordinators, compound Poisson processes, and shot-noise processes, encompassing several well-known models from the literature as special cases. Finally, we show how the framework can be extended to incorporate stochastic continuous components, thereby unifying gradual and abrupt sources of default risk. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.05022 |
| By: | Zängerle, Daniel; Schiereck, Dirk |
| Abstract: | Despite growing attention to cyber risks in research and practice, quantitative cyber risk assessments remain limited, mainly due to a lack of reliable data. This analysis leverages sparse historical data to quantify the financial impact of cyber incidents at the enterprise level. For this purpose, an operational risk database—which has not been previously used in cyber research—was examined to model and predict the likelihood, severity and time dependence of a company’s cyber risk exposure. The proposed model can predict a negative time correlation, indicating that individual cyber exposure is increasing if no cyber loss has been reported in previous years, and vice versa. The results suggest that the probability of a cyber incident correlates with the subindustry, with the insurance sector being particularly exposed. The predicted financial losses from a cyber incident are less extreme than cited in recent investigations. The study confirms that cyber risks are heavy-tailed, jeopardising business operations and profitability. |
| Date: | 2025–08–11 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:156328 |
| By: | Kenneth Q. Zhou; Hongjuan Zhou |
| Abstract: | Recent studies have identified long-range dependence as a key feature in the dynamics of both mortality and interest rates. Building on this insight, we develop a novel bi-variate stochastic framework based on mixed fractional Brownian motions to jointly model their long-memory behavior and instantaneous correlation. Analytical solutions are derived under the risk-neutral measure for explicitly pricing zero-coupon bonds and extreme mortality bonds, while capturing the impact of persistent and correlated risk dynamics. We then propose a calibration procedure that sequentially estimates the model and risk premium parameters, including the Hurst parameters and the correlation parameter, using the most recent data on mortality rates, interest rates, and market conditions. Lastly, an extensive numerical analysis is conducted to examine how long-range dependence and mortality-interest correlation influence fair coupon rates, bond payouts and risk measures, providing practical implications for the pricing and risk management of mortality-linked securities in the post-pandemic environment. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.19445 |
| By: | Haibo Wang |
| Abstract: | This paper analyses the risk factors around investing in global supply chain infrastructure: the energy market, investor sentiment, and global shipping costs. It presents portfolio strategies associated with dynamic risks. A time-varying parameter vector autoregression (TVP-VAR) model is used to study the spillover and interconnectedness of the risk factors for global supply chain infrastructure portfolios from January 5th, 2010, to June 29th, 2023, which are associated with a set of environmental, social, and governance (ESG) indexes. The effects of extreme events on risk spillovers and investment strategy are calculated and compared before and after the COVID-19 outbreak. The results of this study demonstrate that risk shocks influence the dynamic connectedness between global supply chain infrastructure portfolios and three risk factors and show the effects of extreme events on risk spillovers and investment outcomes. Portfolios with higher ESG scores exhibit stronger dynamic connectedness with other portfolios and factors. Net total directional connectedness indicates that West Texas Intermediate (WTI), Baltic Exchange Dry Index (BDI), and investor sentiment volatility index (VIX) consistently are net receivers of spillover shocks. A portfolio with a ticker GLFOX appears to be a time-varying net receiver and giver. The pairwise connectedness shows that WTI and VIX are mostly net receivers. Portfolios with tickers CSUAX, GII, and FGIAX are mostly net givers of spillover shocks. The COVID-19 outbreak changed the structure of dynamic connectedness on portfolios. The mean value of HR and HE indicates that the weights of long/short positions in investment strategy after the COVID-19 outbreak have undergone structural changes compared to the period before. The hedging ability of global supply chain infrastructure investment portfolios with higher ESG scores is superior. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.04858 |
| By: | Xinbing Kong; Cheng Liu; Bin Wu |
| Abstract: | Asynchronous trading in high-frequency financial markets introduces significant biases into econometric analysis, distorting risk estimates and leading to suboptimal portfolio decisions. Existing synchronization methods, such as the previous-tick approach, suffer from information loss and create artificial price staleness. We introduce a novel framework that recasts the data synchronization challenge as a constrained matrix completion problem. Our approach recovers the potential matrix of high-frequency price increments by minimizing its nuclear norm -- capturing the underlying low-rank factor structure -- subject to a large-scale linear system derived from observed, asynchronous price changes. Theoretically, we prove the existence and uniqueness of our estimator and establish its convergence rate. A key theoretical insight is that our method accurately and robustly leverages information from both frequently and infrequently traded assets, overcoming a critical difficulty of efficiency loss in traditional methods. Empirically, using extensive simulations and a large panel of S&P 500 stocks, we demonstrate that our method substantially outperforms established benchmarks. It not only achieves significantly lower synchronization errors, but also corrects the bias in systematic risk estimates (i.e., eigenvalues) and the estimate of betas caused by stale prices. Crucially, portfolios constructed using our synchronized data yield consistently and economically significant higher out-of-sample Sharpe ratios. Our framework provides a powerful tool for uncovering the true dynamics of asset prices, with direct implications for high-frequency risk management, algorithmic trading, and econometric inference. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.12220 |
| By: | Gogova Lea; Hledik Juraj (European Commission - JRC); Klacso Jan |
| Abstract: | Climate change is expected to lead to more frequent and intense extreme weather events, such as floods and droughts, which in turn increase physical risks. In this paper, we assess the direct exposure of Slovak banks' corporate loan portfolios to riverine flood risk. We propose several monitoring metrics and estimate exposures at risk due to riverine flooding. Our analysis leverages a comprehensive dataset that integrates flood risk maps from the European Commission's Joint Research Centre, cadastral data on firm properties, credit register data, and firms' financial statements. While a significant share of firms are located in flood-prone areas, only a subset are likely to face flood levels that exceed critical thresholds. Consequently, the direct impact of riverine flooding on corporate credit risk appears to be relatively moderate — with the estimated increase of exposure at default ranging from 2 to 10 basis points of the corporate loan portfolio under standard scenarios, and up to 50–60 basis points in conservative stress cases accounting for asset value declines. Under counterfactual scenarios assuming a fivefold increase in the frequency of floods, the estimated increase exceeds 1 percentage point of the loan portfolio. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:jrs:wpaper:202508 |
| By: | Gianni De Nicolò |
| Abstract: | Using a large sample of US Bank Holding Companies during 1995-2023, we find that a standard measure of franchise value predicts lower bank capitalization and higher bank risk of insolvency. The franchise value hypothesis (FVH), postulating a negative relationship between bank franchise value and bank risk-taking, is thus rejected in our sample. We then construct proxy measures of bank pricing power rents and rents due to government guarantees, and show that an increase in either type of rents is associated with higher franchise values. Furthermore, higher rents are positively and significantly associated with higher operating costs, suggesting the existence of a rent-efficiency trade-off. To rationalize our empirical findings, we calibrate two standard financial models of the banking firm and examine the theoretical implications of a banking industry model featuring imperfect competition, agency costs, and endogenous market structure. These models support FVH incentive mechanism, but equilibrium outcomes do not necessarily imply a negative relationship between franchise value and bank risk-taking. |
| Keywords: | Tobin Q, bank risk of insolvency, bank rents |
| JEL: | C21 E44 G21 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12044 |
| By: | Leonardo Perotti; Lech A. Grzelak; Cornelis W. Oosterlee |
| Abstract: | Prepayment risk embedded in fixed-rate mortgages forms a significant fraction of a financial institution's exposure. The embedded prepayment option bears the same interest rate risk as an exotic interest rate swap with a suitable stochastic notional. Focusing on penalty-free prepayment because of the contract owner's relocation to a new house, we model the prepayment option value as an European-type interest rate receiver swaption with stochastic maturity matching the stochastic time of relocation. This is a convenient representation since it allows us to compute the prepayment option value in terms of well-known pricing formulas for European-type swaptions. We investigate the effect of a stochastic housing market activity as the explanatory variable for the distribution of the relocation time, as opposed to the conventional assumption of a deterministic housing market activity. We prove that the housing market covariance drives the prepayment option price difference between the stochastic setting and its deterministic counterpart. The prepayment option exposure is hedged using market instruments based on Delta-Gamma replication. Furthermore, since the housing market activity is a non-tradable risk factor, we perform non-standard actuarial hedging focusing on controlling the prepayment option exposure yield by risky housing market scenarios. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.08641 |
| By: | Liyuan Cui; Wenyuan Li |
| Abstract: | This paper investigates optimal investment and insurance strategies under a mean-variance criterion with path-dependent effects. We use a rough volatility model and a Hawkes process with a power kernel to capture the path dependence of the market. By adding auxiliary state variables, we degenerate a non-Markovian problem to a Markovian problem. Next, an explicit solution is derived for a path-dependent extended Hamilton-Jacobi-Bellman (HJB) equation. Then, we derive the explicit solutions of the problem by extending the Functional Ito formula for fractional Brownian motion to the general path-dependent processes, which includes the Hawkes process. In addition, we use earthquake data from Sichuan Province, China, to estimate parameters for the Hawkes process. Our numerical results show that the individual becomes more risk-averse in trading when stock volatility is rough, while more risk-seeking when considering catastrophic shocks. Moreover, an individual's demand for catastrophe insurance increases with path-dependent effects. Our findings indicate that ignoring the path-dependent effect would lead to a significant underinsurance phenomenon and highlight the importance of the path-dependent effect in the catastrophe insurance pricing. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.15355 |
| By: | Saadaoui, Jamel |
| Abstract: | This paper introduces a novel identification strategy of geopolitical turning points, defined as sharp, unanticipated inflection points in bilateral relations. These shocks are measured using the second difference of the Political Relationship Index (Δ²PRI), an event-based monthly index derived from Chinese sources. Unlike standard geopolitical risk indices, Δ²PRI isolates events that represent exogenous shifts in diplomatic relationships. Using quantile IV-local projections, the paper studies the causal and asymmetric impact of these shocks on global oil prices. An improvement in US–China relations reduces oil prices by 0.2% in the short run and raises them by 0.3% in the medium run, with effects varying across the oil price distribution. The extension to the Japan–China dyad supports external validity. The Δ²PRI instrument offers a reusable framework for analyzing bilateral political shocks across various macroeconomic outcomes, making a methodological contribution to the international economics literature. |
| Keywords: | Geopolitical Risk, Oil Prices, Quantile Local Projections, Instrumental Variables. |
| JEL: | C26 C32 F41 F51 |
| Date: | 2025–01–31 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125586 |
| By: | Ziyao Wang |
| Abstract: | We extend the Lindquist-Rachev (LR) option-pricing framework--which values derivatives in markets lacking a traded risk-free bond--by introducing common Levy jump dynamics across two risky assets. The resulting endogenous "shadow" short rate replaces the usual risk-free yield and governs discounting and risk-neutral drifts. We focus on two widely used pure-jump specifications: the Normal Inverse Gaussian (NIG) process and the Carr-Geman-Madan-Yor (CGMY) tempered-stable process. Using Ito-Levy calculus we derive an LR partial integro-differential equation (LR-PIDE) and obtain European option values through characteristic-function methods implemented with the Fast Fourier Transform (FFT) and Fourier-cosine (COS) algorithms. Calibrations to S and P 500 index options show that both jump models materially reduce pricing errors and fit the observed volatility smile far better than the Black-Scholes benchmark; CGMY delivers the largest improvement. We also extract time-varying shadow short rates from paired asset data and show that sharp declines coincide with liquidity-stress episodes, highlighting risk signals not visible in Treasury yields. The framework links jump risk, relative asset pricing, and funding conditions in a tractable form for practitioners. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.20338 |
| By: | Yumeng Shi; Zhongliang Yang; DiYang Lu; Yisi Wang; Yiting Zhou; Linna Zhou |
| Abstract: | Corporate credit rating serves as a crucial intermediary service in the market economy, playing a key role in maintaining economic order. Existing credit rating models rely on financial metrics and deep learning. However, they often overlook insights from non-financial data, such as corporate annual reports. To address this, this paper introduces a corporate credit rating framework that integrates financial data with features extracted from annual reports using FinBERT, aiming to fully leverage the potential value of unstructured text data. In addition, we have developed a large-scale dataset, the Comprehensive Corporate Rating Dataset (CCRD), which combines both traditional financial data and textual data from annual reports. The experimental results show that the proposed method improves the accuracy of the rating predictions by 8-12%, significantly improving the effectiveness and reliability of corporate credit ratings. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02738 |
| By: | Alen Mulabdic (World Bank); Yoto Yotov (School of Economics, Drexel University) |
| Abstract: | We study the impact of geopolitical risks on international trade. To this end, we use the Geopolitcal Risk (GPR) index of Caldara and Iacoviello (2022) and an empirical gravity model. The impact of spikes in GPR on trade is negative, strong, and heterogeneous across sectors. Specifically, we find that increases in geopolitical risk reduce trade by about 30-40%. These effects are equivalent to a global tariff increase of up to 14%. Services trade is most vulnerable to geopolitical risks, followed by agriculture, while the impact on manufacturing trade is moderate. These negative effects are partially mitigated by cultural and geographic proximity, as well as by the presence of trade agreements. |
| Keywords: | Geopolitical Risks, International Trade, Structural Gravity |
| JEL: | F13 F14 F16 F51 F52 H56 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:drx:wpaper:202532 |
| By: | Massimiliano Caporin (Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241, 35121 Padova, Italy); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Sowmya Subramaniam (Indian Institute of Management Lucknow, Prabandh Nagar off Sitapur Road, Lucknow, Uttar Pradesh 226013, India); Hudson S. Torrent (Department of Statistics, Universidade Federal do Rio Grande do Sul Porto Alegre, 91509-900, Brazil) |
| Abstract: | We use a mixed-frequency non-parametric causality-in-quantiles test to detect predictability from newspapers articles-based daily indexes of supply bottlenecks to the conditional distributions of monthly inflation rate and its volatility of China, the European Monetary Union (EMU), the United Kingdom (UK) and the United States (US). Based on a sample period of January 2010 to December 2024, we find that the causal impact of supply bottlenecks on inflation volatility is consistently ob-served across the four economies, while the same is particularly strong for the inflation rates of the EMU and the UK. The second-moment impact is further emphasized in a forecasting set-up, as we detect statistically significant impact of these supply chain constraints in the prediction of the lower quantiles of inflation volatility. Our findings have important implications for monetary policy decisions. |
| Keywords: | Inflation, Inflation Volatility, Supply Bottlenecks, Mixed-Frequency, Nonparametric Causality-in-Quantiles Test |
| JEL: | C22 C53 E23 E31 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202526 |
| By: | Stefan Nagel |
| Abstract: | Return prediction with Random Fourier Features (RFF)—a very large number, P , of nonlinear trans-formations of a small number, K, of predictor variables—has become popular recently. Surprisingly, this approach appears to yield a successful out-of-sample stock market index timing strategy even when trained in rolling windows as small as T = 12 months with P in the thousands. However, when P ≫ T , the RFF-based forecast becomes a weighted average of the T training sample returns, with weights determined by the similarity between the predictor vectors in the training data and the current predictor vector. In short training windows, similarity primarily reflects temporal proximity, so the forecast reduces to a recency-weighted average of the T return observations in the training data—essentially a momentum strategy. Moreover, because similarity declines with predictor volatility, the result is a volatility-timed momentum strategy. The strong performance of the RFF-based strategy thus stems not from its ability to extract predictive signals from the training data, but from the fact that a volatility-timed momentum strategy happened to perform well in historical data. This point becomes clear when applying the same method to artificial data in which returns exhibit reversals rather than momentum: the RFF approach still constructs the same volatility-timed momentum strategy, which then performs poorly. |
| JEL: | G12 G14 G17 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34104 |
| By: | Adriana Corrales-Quesada (Economic Division, Central Bank of Costa Rica); Fabio Gómez-Rodríguez (Department of Economic Research, Central Bank of Costa Rica); Carlos Segura-Rodriguez (Department of Economic Research, Central Bank of Costa Rica) |
| Abstract: | This technical note describes the procedure currently used by the Central Bank of Costa Rica (BCCR, by its initials in Spanish) to conduct stress tests on the portfolios of financial entities. The analysis identified two opportunities for improvement compared to the current practice; a procedure that was implemented as part of a technical assistance provided by the IMF. First, the stress tests are calculated based on the estimation of the PAR yield curves in colones and dollars carried out by the BCCR, which better reflect the behavior of the Costa Rican market. Secondly, resampling methods are used to determine the loss distribution based on available data, rather than using only predefined shocks, which facilitates the comparison of the predefined shocks currently used with the actual observed events. In conclusion, it is recommended to implement these two improvements in the execution of the stress tests. |
| Keywords: | Stress Tests, Yield Curve Estimation, Financial Crisis, Pruebas de tensión, Curvas de rendimiento, Crisis financieras |
| JEL: | G21 G28 C63 |
| Date: | 2024–02 |
| URL: | https://d.repec.org/n?u=RePEc:apk:nottec:2401 |
| By: | Joshua Aslett; Thomas Cantens; François Chastel; Emmanuel A Crown; Stuart Hamilton |
| Abstract: | This technical note provides an introduction to generative artificial intelligence (GenAI) and its potential to support compliance risk analysis in tax and customs administration. Written primarily for a technical audience, it seeks to raise awareness of GenAI by explaining and demonstrating its capabilities. The note opens with a brief conceptual overview of GenAI technology. It then describes four generalized use cases where GenAI can augment the work of risk analysts. As experimental proofs of concept, a selection of worked examples is presented. Having demonstrated GenAI’s potential, the note then provides basic guidelines to help administrations that may be considering implementing the technology in an operational setting. It concludes with forward-looking statements on likely developments. |
| Keywords: | Tax administration; customs administration; artificial intelligence |
| Date: | 2025–08–08 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imftnm:2025/013 |
| By: | Albert Prades-Colomé |
| Abstract: | As populations age, understanding the health impact of long-term care systems is critical for shaping effective policy. This study investigates the association between long-term care benefits and mortality risk among older adults in Catalonia, Spain, using comprehensive administrative data from July 2015 to December 2024. The analysis focuses on individuals aged 50+ who were assessed for long-term care needs, categorizing them by severity (Grades I–III) and type of benefit received: home care, residential care, a combination of both or no benefit. Applying survival analysis techniques—including Kaplan-Meier estimators and Cox proportional hazards models—it finds that individuals with long-term care needs receiving benefits have significantly lower mortality hazards. Notably, individuals transitioning from home to residential care exhibit the most favourable hazard ratios, suggesting that responsive care pathways are associated with better survival outcomes, potentially due to a most accurate matching of care to needs. Residential care alone is associated to higher mortality risk than home care in the population with the highest grades of long-term care needs. Individuals with recognized longterm care needs who do not receive any benefits face significantly higher risks, a pattern that may reflect the consequences of unmet care needs. Mortality risk varies by sex, age, and clinical profile, with higher hazards observed among men, older individuals, and those with haematological, neoplastic, or respiratory conditions. These findings underscore the association between formal long-term care systems and lower mortality risk and emphasize the importance of timely, adaptive care pathways in mitigating health decline among aging populations. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:fda:fdaddt:2025-10 |
| By: | Ismet Gocer; Julia Darby; Serdar Ongan |
| Abstract: | Important game-changer economic events and transformations cause uncertainties that may affect investment decisions, capital flows, international trade, and macroeconomic variables. One such major transformation is Brexit, which refers to the multiyear process through which the UK withdrew from the EU. This study develops and uses a new Brexit-Related Uncertainty Index (BRUI). In creating this index, we apply Text Mining, Context Window, Natural Language Processing (NLP), and Large Language Models (LLMs) from Deep Learning techniques to analyse the monthly country reports of the Economist Intelligence Unit from May 2012 to January 2025. Additionally, we employ a standard vector autoregression (VAR) analysis to examine the model-implied responses of various macroeconomic variables to BRUI shocks. While developing the BRUI, we also create a complementary COVID-19 Related Uncertainty Index (CRUI) to distinguish the uncertainties stemming from these distinct events. Empirical findings and comparisons of BRUI with other earlier-developed uncertainty indexes demonstrate the robustness of the new index. This new index can assist British policymakers in measuring and understanding the impacts of Brexit-related uncertainties, enabling more effective policy formulation. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.02439 |
| By: | Robin Musolff; Florian Zimmermann |
| Abstract: | Mental models help people navigate complex environments. This paper studies how people deal with model uncertainty. In an experiment, participants estimate a company’s value, facing uncertainty about which one of two models correctly determines its true value. Using a between-subjects design, we vary the degree of model complexity. Results show that in high-complexity conditions people fully neglect model uncertainty in their actions. However, their beliefs continue to reflect model uncertainty. This disconnect between beliefs and actions suggests that complexity leads to biased decision-making, while beliefs remain more nuanced. Furthermore, we show that complexity, via full uncertainty neglect, leads to higher confidence in the optimality of own actions. |
| Keywords: | mental models, geliefs, attention, confidence, representations |
| JEL: | D01 D83 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12041 |