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on Risk Management |
| By: | Sharif Al Mamun; Rakib Hossain; Md. Jobayer Rahman; Malay Kumar Devnath; Farhana Afroz; Lisan Al Amin |
| Abstract: | A Bayesian analytics framework that precisely quantifies uncertainty offers a significant advance for financial risk management. We develop an integrated approach that consistently enhances the handling of risk in market volatility forecasting, fraud detection, and compliance monitoring. Our probabilistic, interpretable models deliver reliable results: We evaluate the performance of one-day-ahead 95% Value-at-Risk (VaR) forecasts on daily S&P 500 returns, with a training period from 2000 to 2019 and an out-of-sample test period spanning 2020 to 2024. Formal tests of unconditional (Kupiec) and conditional (Christoffersen) coverage reveal that an LSTM baseline achieves near-nominal calibration. In contrast, a GARCH(1, 1) model with Student-t innovations underestimates tail risk. Our proposed discount-factor DLM model produces a slightly liberal VaR estimate, with evidence of clustered violations. Bayesian logistic regression improves recall and AUC-ROC for fraud detection, and a hierarchical Beta state-space model provides transparent and adaptive compliance risk assessment. The pipeline is distinguished by precise uncertainty quantification, interpretability, and GPU-accelerated analysis, delivering up to 50x speedup. Remaining challenges include sparse fraud data and proxy compliance labels, but the framework enables actionable risk insights. Future expansion will extend feature sets, explore regime-switching priors, and enhance scalable inference. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.15739 |
| By: | Ahmadian- Yazdi, Farzaneh; Roudari, Soheil; Mensi, Walid |
| Abstract: | This paper examines the spillover effects between Sukuk and key alternative assets- conventional stock, gold, and currency- in Iran from July 2013 to December 2024. Using three advanced models-Quantile-on-Quantile (Gabauer & Stenfors, 2024), the contemporaneous and lagged R2 decomposed connectedness (Balli et al., 2023), and a portfolio approach (Broadstock et al., 2022)-the study finds that Iran's Sukuk market lacks depth for hedging against gold, currency, and stock risks across direct and reverse quantiles and under various shocks. Results show that the USD is the main contemporaneous driver, while Sukuk is a net receiver in average and contemporaneous connections. Sukuk also offers low long-term returns, making it less competitive. Gold proves optimal for long-term investment, mainly when currency acts short-term. Currency is the primary source of short-term volatility, but Sukuk fails as a stabilizing tool. Thus, including Sukuk in portfolios does not enhance diversification for risk-averse investors during crises due to its limited hedging ability in Iran. |
| Keywords: | Sukuk, Stock market, Gold, Currency, Risk spillover, Portfolio management |
| JEL: | C58 G32 |
| Date: | 2025–03–17 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126962 |
| By: | Xue, Huidan; Du, Yuxuan |
| Keywords: | Demand and Price Analysis, Risk and Uncertainty, Agricultural and Food Policy |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea24:343639 |
| By: | Anand, Vaibhav; Ma, Yu-Luen; Ren, Yayuan |
| Abstract: | We develop an empirical measure of U.S. property-liability insurers’ vulnerability to catastrophe risk. Using underwriting outcomes and property damage data from 1991 to 2021, we first estimate state–line sensitivities that quantify how unexpected disaster damages translate into insured losses. Sensitivity varies widely across lines and states: homeowners and allied lines, and the Gulf and Southeastern states, show the strongest transmission. Loss ratios rise sharply in high-damage years, but decline only modestly in low-damage years. Combining sensitivities with insurers' portfolio compositions, we construct an insurer-level vulnerability metric and find that vulnerability is highly skewed. Insurers in the top quintile are roughly four times more exposed than the next group, and their vulnerability has grown by 50 percent over time. While most insurers manage catastrophe exposure through diversification, highly vulnerable insurers, typically smaller and concentrated, rely heavily on reinsurance. Our metric also reconciles prior evidence on diversification and reinsurance: while concentration lowers reinsurance demand on average, for vulnerable insurers, reinsurance usage increases with geographic concentration. |
| Date: | 2025–12–06 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:4c8tp_v1 |
| By: | Agostino Capponi; Chengpiao Huang; J. Antonio Sidaoui; Kaizheng Wang; Jiacheng Zou |
| Abstract: | We investigate machine learning models for stock return prediction in non-stationary environments, revealing a fundamental nonstationarity-complexity tradeoff: complex models reduce misspecification error but require longer training windows that introduce stronger non- stationarity. We resolve this tension with a novel model selection method that jointly optimizes model class and training window size using a tournament procedure that adaptively evaluates candidates on non-stationary validation data. Our theoretical analysis demonstrates that this approach balances misspecification error, estimation variance, and non-stationarity, performing close to the best model in hindsight. Applying our method to 17 industry portfolio returns, we consistently outperform standard rolling-window benchmarks, improving out-of-sample $R^2$ by 14-23% on average. During NBER- designated recessions, improvements are substantial: our method achieves positive $R^2$ during the Gulf War recession while benchmarks are negative, and improves $R^2$ in absolute terms by at least 80bps during the 2001 recession as well as superior performance during the 2008 Financial Crisis. Economically, a trading strategy based on our selected model generates 31% higher cumulative returns averaged across the industries. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.23596 |
| By: | Bong-Gyu Jang; Younwoo Jeong; Changeun Kim |
| Abstract: | We introduce the \textit{Consensus-Bottleneck Asset Pricing Model} (CB-APM), a partially interpretable neural network that replicates the reasoning processes of sell-side analysts by capturing how dispersed investor beliefs are compressed into asset prices through a consensus formation process. By modeling this ``bottleneck'' to summarize firm- and macro-level information, CB-APM not only predicts future risk premiums of U.S. equities but also links belief aggregation to expected returns in a structurally interpretable manner. The model improves long-horizon return forecasts and outperforms standard deep learning approaches in both predictive accuracy and explanatory power. Comprehensive portfolio analyses show that CB-APM's out-of-sample predictions translate into economically meaningful payoffs, with monotonic return differentials and stable long-short performance across regularization settings. Empirically, CB-APM leverages consensus as a regularizer to amplify long-horizon predictability and yields interpretable consensus-based components that clarify how information is priced in returns. Moreover, regression and GRS-based pricing diagnostics reveal that the learned consensus representations capture priced variation only partially spanned by traditional factor models, demonstrating that CB-APM uncovers belief-driven structure in expected returns beyond the canonical factor space. Overall, CB-APM provides an interpretable and empirically grounded framework for understanding belief-driven return dynamics. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.16251 |