|
on Risk Management |
| By: | Isaac Otchere; Zia Mohammed; Witness Simbanegavi |
| Abstract: | In this paper we examine the relationship between fintech formations and the default risk and performance of incumbent financial institutions in South Africa. We find that the development of fintech startups is associated with lower bankruptcy risk, credit risk and stock return volatility among banks and other financial institutions. Fintech startup formations are also associated with improvement in incumbent institutions performance. Further analysis shows that the risk reduction effect of fintech development is more pronounced for smaller banks. Overall, our results are consistent with the assertion that fintech formations generally improve risk management efficiency and reduce incumbent financial institutions default risk. However, the relationship is nonlinear, suggesting that the initial collaboration, which reduces default risk, can turn into increased competition as more fintech startups enter the market. From a policy standpoint, efforts to promote more collaboration should be encouraged, but regulators need to be cautious of potential systemic risk. |
| Date: | 2025–08–04 |
| URL: | https://d.repec.org/n?u=RePEc:rbz:wpaper:11082 |
| By: | Sicheng Fu; Fangfang Zhu; Xiangdong Liu |
| Abstract: | This paper investigates the dynamics of risk transmission in cryptocurrency markets and proposes a novel framework for volatility forecasting. The framework uncovers two key empirical facts: the asymmetric amplification of volatility spillovers in both tails, and a structural decoupling between market size and systemic importance. Building on these insights, we develop a state-adaptive volatility forecasting model by extracting time-varying quantile spillover features across different volatility components. These features are embedded into an extended Log-HAR structure, resulting in the SA-Log-HAR model. Empirical results demonstrate that the proposed model outperforms benchmark alternatives in both in-sample fitting and out-of-sample forecasting, particularly in capturing extreme volatility and tail risks with greater robustness and explanatory power. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.22409 |
| By: | Duo Zhang; Jiayu Li; Junyi Mo; Elynn Chen |
| Abstract: | Accurate volatility forecasts are vital in modern finance for risk management, portfolio allocation, and strategic decision-making. However, existing methods face key limitations. Fully multivariate models, while comprehensive, are computationally infeasible for realistic portfolios. Factor models, though efficient, primarily use static factor loadings, failing to capture evolving volatility co-movements when they are most critical. To address these limitations, we propose a novel, model-agnostic Factor-Augmented Volatility Forecast framework. Our approach employs a time-varying factor model to extract a compact set of dynamic, cross-sectional factors from realized volatilities with minimal computational cost. These factors are then integrated into both statistical and AI-based forecasting models, enabling a unified system that jointly models asset-specific dynamics and evolving market-wide co-movements. Our framework demonstrates strong performance across two prominent asset classes-large-cap U.S. technology equities and major cryptocurrencies-over both short-term (1-day) and medium-term (7-day) horizons. Using a suite of linear and non-linear AI-driven models, we consistently observe substantial improvements in predictive accuracy and economic value. Notably, a practical pairs-trading strategy built on our forecasts delivers superior risk-adjusted returns and profitability, particularly under adverse market conditions. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.01880 |
| By: | Kang, Wilson; Smyth, Russell; Vespignani, joaquin vespignani |
| Abstract: | This paper applies the macroeconomic fragility framework for studying the effects of supply chain disruptions, proposed by Acemoglu and Tahbaz-Salehi (2024), to critical minerals markets. A key prediction of the macroeconomic fragility framework is that equilibrium supply chains are inherently fragile, meaning that even small shocks can trigger cascading supply chain breakdowns that can significantly magnify the discontinuous response of aggregate supply to shocks, leading to higher volatility and prices of critical minerals. We highlight the important role that the non-technical risk premium plays in magnifying global supply chain shocks in the specific case of critical minerals. Using a mixed-frequency Structural VAR model with agnostic sign restrictions and newly constructed data on non-technical risk premiums, we estimate the impact of supply chain disruption, the non-technical risk premium and their interaction on the prices and volatility of six critical minerals. We find that global supply chain disruptions, magnified by non-technical risk premiums, significantly increase critical mineral prices and price volatility for all six critical minerals studied, indicating inefficient outcomes which we interpret as macroeconomic fragility in critical minerals markets. We also show that stockpiling has the potential to reduce macroeconomic fragility in critical mineral markets. |
| Keywords: | global supply chain disruption, critical minerals, non-technical risk premiums, macroeconomic fragility |
| JEL: | E0 E02 Q0 Q02 |
| Date: | 2025–03–02 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125351 |
| By: | Sung Hoon Choi; Donggyu Kim |
| Abstract: | Based on It\^o semimartingale models, several studies have proposed methods for forecasting intraday volatility using high-frequency financial data. These approaches typically rely on restrictive parametric assumptions and are often vulnerable to model misspecification. To address this issue, we introduce a novel nonparametric prediction method for the future intraday instantaneous volatility process during trading hours, which leverages both previous days' data and the current day's observed intraday data. Our approach imposes an interday-by-intraday matrix representation of the instantaneous volatility, which is decomposed into a low-rank conditional expectation component and a noise matrix. To predict the future conditional expected volatility vector, we exploit this low-rank structure and propose the Structural Intraday-volatility Prediction (SIP) procedure. We establish the asymptotic properties of the SIP estimator and demonstrate its effectiveness through an out-of-sample prediction study using real high-frequency trading data. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.22173 |
| By: | Denise Desjardins (HEC Montréal); Georges Dionne (HEC Montréal) |
| Abstract: | The goal of this research is to assess the insurance industry ability to absorb catastrophe losses and evaluate its capacity to spread risk across insurers. Dionne and Desjardins (2022) showed that the US insurance industry capacity to pay catastrophe losses was higher in 2020 than it was in 1997. Insurers could pay 98% of a $200 billion loss in 2020, compared to 81% in 1997. In this document, we consider the following research question: Is the capacity still adequate after three years of turbulence? Climate risk events have caused average home premiums to increase by 22% from 2020 to 2023 (The Guardian, December 2024). Was it sufficient to maintain market capacity? |
| Keywords: | Catastrophe losses; insurance market capacity; premiums increase; insurer capital; insured losses |
| Date: | 2025–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:ris:crcrmw:021461 |
| By: | Elisa Al\`os; \`Oscar Bur\'es; Rafael de Santiago; Josep Vives |
| Abstract: | We compare two methodologies for calibrating implied volatility surfaces: a second-order asymptotic expansion method derived via Malliavin calculus, and a data-driven approach based on path signatures from rough path theory. The former, developed in Al\`os et al. (2015), yields efficient and accurate calibration formulas under the assumption that the asset price follows a Heston-type stochastic volatility model. The latter models volatility as a linear functional of the signature of a primary stochastic process, enabling a flexible approximation without requiring a specific parametric form. Our numerical experiments show that the signature-based method achieves calibration accuracy comparable to the asymptotic approach when the true dynamics are Heston. We then test the model in a more general setting where the asset follows a rough Bergomi volatility process-a regime beyond the scope of the asymptotic expansion-and show that the signature approach continues to deliver accurate results. These findings highlight the model-independence, robustness and adaptability of signature-based calibration methods in settings where volatility exhibits rough or non-Markovian features. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.23392 |
| By: | Antonino Castelli; Paolo Giudici; Alessandro Piergallini |
| Abstract: | The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility. To address the challenge of managing dynamic portfolios in such an environment, this paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations. Using data on daily frequencies of the ten most capitalized cryptocurrencies from 2020 to 2025, we compare two automated investment strategies. These are a static equal weighting strategy and a rolling-window optimization strategy, both implemented to maximize the evaluation metrics of the Modern Portfolio Theory (MPT), such as Expected Return, Sharpe and Sortino ratios, while minimizing volatility. Each step of the process is handled by dedicated agents, integrated through a collaborative architecture in Crew AI. The results show that the dynamic optimization strategy achieves significantly better performance in terms of risk-adjusted returns, both in-sample and out-of-sample. This highlights the benefits of adaptive techniques in portfolio management, particularly in volatile markets such as cryptocurrency markets. The following methodology proposed also demonstrates how multi-agent systems can provide scalable, auditable, and flexible solutions in financial automation. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.20468 |
| By: | Tingyu Yuan; Xi Zhang; Xuanjing Chen |
| Abstract: | In the face of global economic uncertainty, financial auditing has become essential for regulatory compliance and risk mitigation. Traditional manual auditing methods are increasingly limited by large data volumes, complex business structures, and evolving fraud tactics. This study proposes an AI-driven framework for enterprise financial audits and high-risk identification, leveraging machine learning to improve efficiency and accuracy. Using a dataset from the Big Four accounting firms (EY, PwC, Deloitte, KPMG) from 2020 to 2025, the research examines trends in risk assessment, compliance violations, and fraud detection. The dataset includes key indicators such as audit project counts, high-risk cases, fraud instances, compliance breaches, employee workload, and client satisfaction, capturing both audit behaviors and AI's impact on operations. To build a robust risk prediction model, three algorithms - Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) - are evaluated. SVM uses hyperplane optimization for complex classification, RF combines decision trees to manage high-dimensional, nonlinear data with resistance to overfitting, and KNN applies distance-based learning for flexible performance. Through hierarchical K-fold cross-validation and evaluation using F1-score, accuracy, and recall, Random Forest achieves the best performance, with an F1-score of 0.9012, excelling in identifying fraud and compliance anomalies. Feature importance analysis reveals audit frequency, past violations, employee workload, and client ratings as key predictors. The study recommends adopting Random Forest as a core model, enhancing features via engineering, and implementing real-time risk monitoring. This research contributes valuable insights into using machine learning for intelligent auditing and risk management in modern enterprises. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.06266 |
| By: | Shi, Ruihan; Chen, Pinxian |
| Abstract: | Corporate risk-taking is a key factor in corporate decision-making, and in recent years, the influence of cultural factors as informal institutions on corporate decisions has attracted widespread attention from scholars. Confucian culture, which upholds core values such as benevolence and righteousness, forgiveness and tolerance, integrity, and loyalty and filial piety, has long permeated various levels of Chinese society. Using non-financial listed firms in China as the sample, this study measures the strength of Confucian cultural influence by the number of Confucian temples within varying distances around each firm and further explores the impact of Confucian culture on corporate risk-taking. The results show that Confucian culture is negatively associated with corporate risk-taking, indicating that in regions where Confucian culture is more deeply rooted, firms tend to exhibit lower levels of risk-taking. This study provides an in-depth empirical analysis of the factors influencing corporate risk-taking and the role of cultural factors, offering important guidance for corporate strategic development and risk management strategies while contributing to a deeper and more comprehensive understanding of the critical role of cultural factors, such as Confucian culture, in risk-taking decisions. |
| Date: | 2025–07–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:abhse_v1 |
| By: | Victor Olkhov |
| 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. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.21824 |
| By: | Bahram Adrangi; Arjun Chatrath; Saman Hatamerad; Kambiz Raffiee |
| Abstract: | This study investigates the relationship between the market volatility of the iShares Asia 50 ETF (AIA) and economic and market sentiment indicators from the United States, China, and globally during periods of economic uncertainty. Specifically, it examines the association between AIA volatility and key indicators such as the US Economic Uncertainty Index (ECU), the US Economic Policy Uncertainty Index (EPU), China's Economic Policy Uncertainty Index (EPUCH), the Global Economic Policy Uncertainty Index (GEPU), and the Chicago Board Options Exchange's Volatility Index (VIX), spanning the years 2007 to 2023. Employing methodologies such as the two-covariate GARCH-MIDAS model, regime-switching Markov Chain (MSR), and quantile regressions (QR), the study explores the regime-dependent dynamics between AIA volatility and economic/market sentiment, taking into account investors' sensitivity to market uncertainties across different regimes. The findings reveal that the relationship between realized volatility and sentiment varies significantly between high- and low-volatility regimes, reflecting differences in investors' responses to market uncertainties under these conditions. Additionally, a weak association is observed between short-term volatility and economic/market sentiment indicators, suggesting that these indicators may have limited predictive power, especially during high-volatility regimes. The QR results further demonstrate the robustness of MSR estimates across most quantiles. Overall, the study provides valuable insights into the complex interplay between market volatility and economic/market sentiment, offering practical implications for investors and policymakers. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.05552 |
| By: | Soane, Emma |
| Abstract: | Organizational risk is the possibility of events preventing the achievement of objectives and disrupting organizational viability. Developing understanding of organizational risk is necessary to allow realization of opportunities and protection from harm. However, much existing theorizing focuses on either a higher level of analysis, for example, studies of organizational risk culture, or a lower level of analysis, such as studies of individual perception, personality, and risk‐taking. One way to advance theorizing involves connecting both levels of analysis. These connections are central to a microfoundations perspective that suggests organizational phenomena can be understood by linking macrolevel contexts with microlevel contexts and actions. I draw on this perspective to develop a model of organizational risk and explain how cross‐level processes connect macro‐ and microlevel concepts. I focus on the organizational psychology literature that encompasses higher and lower levels of analysis to select and examine relevant concepts. I explain how organizational cultures create contexts for individual risk‐taking that are homogeneous when constraints are strong and directional or variable when constraints are weak and ambiguous. These behaviors aggregate within and across units to influence organizational risk. Individual risk‐taking also influences organizational risk via autonomy and discretion. In developing the model, I show how theories of cross‐level processes extend understanding of organizational risk. I discuss implications for advancing theorizing about organizational risk by encompassing its microfoundations and linking them with managerial actions and objectives. Future research could examine these mechanisms through empirical studies and shed light on how leaders influence processes and change organizational risk. |
| Keywords: | organizational risk; microfoundations; culture; personality; risk-taking; risk‐taking |
| JEL: | G32 J50 |
| Date: | 2025–07–26 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128876 |
| By: | Mikhail Chernov; Magnus Dahlquist; Lars A. Lochstoer |
| Abstract: | Characteristic-based factors embed large unpriced components that depress Sharpe ratios and deviate from the mean-variance efficient (MVE) frontier. We discuss how to decompose tradable factor returns into priced (MVE) and unpriced components, showing that hedging unpriced variation realigns factors with efficiency. We outline theoretical conditions for characteristic portfolios to span the MVE and describe practical hedge-portfolio construction. In some asset classes—currencies and sovereign bonds—real-time estimation of the MVE is feasible. In the case of equities, one can hedge unpriced risks from characteristic-based factors. Empirically, unpriced risks account for 30–99% of factor return variance, and hedging can more than double Sharpe ratios. |
| JEL: | F31 G12 G13 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34009 |
| By: | Aaron Green; Zihan Nie; Hanzhen Qin; Oshani Seneviratne; Kristin P. Bennett |
| Abstract: | Survival modeling predicts the time until an event occurs and is widely used in risk analysis; for example, it's used in medicine to predict the survival of a patient based on censored data. There is a need for large-scale, realistic, and freely available datasets for benchmarking artificial intelligence (AI) survival models. In this paper, we derive a suite of 16 survival modeling tasks from publicly available transaction data generated by lending of cryptocurrencies in Decentralized Finance (DeFi). Each task was constructed using an automated pipeline based on choices of index and outcome events. For example, the model predicts the time from when a user borrows cryptocurrency coins (index event) until their first repayment (outcome event). We formulate a survival benchmark consisting of a suite of 16 survival-time prediction tasks (FinSurvival). We also automatically create 16 corresponding classification problems for each task by thresholding the survival time using the restricted mean survival time. With over 7.5 million records, FinSurvival provides a suite of realistic financial modeling tasks that will spur future AI survival modeling research. Our evaluation indicated that these are challenging tasks that are not well addressed by existing methods. FinSurvival enables the evaluation of AI survival models applicable to traditional finance, industry, medicine, and commerce, which is currently hindered by the lack of large public datasets. Our benchmark demonstrates how AI models could assess opportunities and risks in DeFi. In the future, the FinSurvival benchmark pipeline can be used to create new benchmarks by incorporating more DeFi transactions and protocols as the use of cryptocurrency grows. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.14160 |
| By: | Rachel Cho; Christoph Görtz; Danny McGowan; Max Schröder |
| Abstract: | We propose a new approach to identify firm-level financial constraints by applying artificial intelligence to text of 10-K filings by U.S. public firms from 1993 to 2021. Leveraging transformer-based natural language processing, our model captures contextual and semantic nuances often missed by traditional text classification techniques, enabling more accurate detection of financial constraints. A key contribution is to differentiate between constraints that affect firms presently and those anticipated in the future. These two types of constraints are associated with distinctly different financial profiles: while firms expecting future constraints tend to accumulate cash preemptively, currently constrained firms exhibit reduced liquidity and higher leverage. We show that only firms anticipating financial constraints exhibit significant cash flow sensitivity of cash, whereas currently constrained and unconstrained firms do not. This calls for a narrower interpretation of this widely used cash-based constraints measure, as it may conflate distinct firm types – unconstrained and currently constrained – and fail to capture all financially constrained firms. Our findings underscore the critical role of constraint timing in shaping corporate financial behavior. |
| Keywords: | financial constraints, artificial intelligence, expectations, cash, cash flow, corporate finance behavior |
| JEL: | G31 G32 D92 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12054 |