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on Risk Management |
| By: | Rubenstein, Elias |
| Abstract: | Sequence-of-returns risk (SoRR) matters because the order of returns—rather than only their long-run average—determines whether real, inflation-indexed withdrawal plans survive the early retirement years. For EUR/JPY spenders invested in globally diversified, USD-centric portfolios, SoRR is co-determined by market and FX paths in the spending currency. This paper proposes a state-dependent Swiss-franc (CHF) overlay—implemented via cash/bills or liquid FX instruments—as crisis insurance rather than generic hedging. A transparent stress score triggers and sizes the sleeve; outcomes are evaluated on sequence-sensitive metrics (e.g., CVaR(95), maximum drawdown, time-underwater, and the 5th percentile of sustainable withdrawals). Indexing and FX procedures follow MSCI and WM/Refinitiv methodology; the design is fully auditable and modular for empirical tables/figures. |
| Keywords: | sequence-of-returns risk; safe-haven currency; Swiss franc (CHF); currency overlay; FX hedge; regime switching; drawdown management; decumulation; retirement income; CVaR; global multi-asset portfolios; international finance |
| JEL: | C58 E44 G11 G12 G15 G17 G31 |
| Date: | 2025–11–02 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126680 |
| By: | Aline Goulard; Karl Grosse-Erdmann |
| Abstract: | In financial and actuarial research, distortion and Haezendonck-Goovaerts risk measures are attractive due to their strong properties. They have so far been treated separately. In this paper, following a suggestion by Goovaerts, Linders, Van Weert, and Tank, we introduce and study a new class of risk measure that encompasses the distortion and Haezendonck-Goovaerts risk measures, aptly called the distortion Haezendonck-Goovaerts risk measures. They will be defined on a larger space than the space of bounded risks. We provide situations where these new risk measures are coherent, and explore their risk theoretic properties. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.03267 |
| By: | Liu, Mengqiao; Zhang, Yu Yvette; Jia, Ruixin |
| Keywords: | Financial Economics, Risk and Uncertainty |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea24:343698 |
| By: | Benjamin C\^ot\'e; Ruodu Wang; Qinyu Wu |
| Abstract: | Risk aversion and insurance are two prominent and interconnected concepts in economics and finance. To explore their fundamental connection, we introduce risk-insurance parity, which associates various classes of insurance contracts with different notions of risk aversion. We show that the classic notions -- both weak and strong -- of risk aversion can be characterized by propensity to different classes of insurance contracts, generalizing recent results on propensity to full, proportional, and deductible-limit contracts in the literature. We obtain full characterizations of the classes of insurance indemnity functions that correspond to weak and strong risk aversion. Risk-insurance parity allows us to define two new notions of risk aversion, between weak and strong, characterized by insurance propensity to deductible-only and limit-only contracts respectively. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.09208 |
| By: | Paul Glasserman; Siddharth Hemant Karmarkar |
| Abstract: | Differential ML (Huge and Savine 2020) is a technique for training neural networks to provide fast approximations to complex simulation-based models for derivatives pricing and risk management. It uses price sensitivities calculated through pathwise adjoint differentiation to reduce pricing and hedging errors. However, for options with discontinuous payoffs, such as digital or barrier options, the pathwise sensitivities are biased, and incorporating them into the loss function can magnify errors. We consider alternative methods for estimating sensitivities and find that they can substantially reduce test errors in prices and in their sensitivities. Using differential labels calculated through the likelihood ratio method expands the scope of Differential ML to discontinuous payoffs. A hybrid method incorporates gamma estimates as well as delta estimates, providing further regularization. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.05301 |
| By: | Stéphane Crépey (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité); Noufel Frikha (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Azar Louzi (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité) |
| Abstract: | We propose a multilevel stochastic approximation (MLSA) scheme for the computation of the value-at-risk (VaR) and expected shortfall (ES) of a financial loss, which can only be computed via simulations conditional on the realization of future risk factors. Thus, the problem of estimating its VaR and ES is nested in nature and can be viewed as an instance of stochastic approximation problems with biased innovations. In this framework, for a prescribed accuracy $\varepsilon$, the optimal complexity of a nested stochastic approximation algorithm is shown to be of order $\varepsilon^{-3}$. To estimate the VaR, our MLSA algorithm attains an optimal complexity of order $\varepsilon^{-2-\delta}$ , where $\delta<1$ is some parameter depending on the integrability degree of the loss, while to estimate the ES, it achieves an optimal complexity of order $\varepsilon^{-2}|\ln{\varepsilon}|^2$. Numerical studies of the joint evolution of the error rate and the execution time demonstrate how our MLSA algorithm regains a significant amount of the performance lost due to the nested nature of the problem. |
| Keywords: | numerical finance, Multilevel Monte Carlo, Nested Monte Carlo, stochastic approximation algorithm, Expected Shortfall, Value-at-Risk |
| Date: | 2025–08–27 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04037328 |
| By: | Marwa Abdullah; Revzon Oksana Anatolyevna; Duaa Abdullah |
| Abstract: | In this paper, to determine the financial risks faced by an industrial company, assessing the relative importance of these risks and identifying the years most exposed to financial risk using modern multi-criteria decision-making techniques. Applied to AL-Ahliah Vegetable Oil Company, the research utilizes the Analytical Hierarchy Process and Simple Additive Weighting to analyze financial ratios from 2008 to 2017. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.04035 |
| By: | Tetsuya Takaishi |
| Abstract: | We employ single-qubit quantum circuit learning (QCL) to model the dynamics of volatility time series. To assess its effectiveness, we generate synthetic data using the Rational GARCH model, which is specifically designed to capture volatility asymmetry. Our results show that QCL-based volatility predictions preserve the negative return-volatility correlation, a hallmark of asymmetric volatility dynamics. Moreover, analysis of the Hurst exponent and multifractal characteristics indicates that the predicted series, like the original synthetic data, exhibits anti-persistent behavior and retains its multifractal structure. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.10584 |
| By: | Chavas, Jean-Paul; Li, Jian; Wang, Linjie |
| Keywords: | Agricultural Finance, Risk and Uncertainty, Demand and Price Analysis |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea24:343544 |
| By: | Bouveret, Antoine; Darpeix, Pierre-Emmanuel; Ferrari, Massimo; Grill, Michael; Molestina Vivar, Luis; Schmidt, Daniel Jonas; Weistroffer, Christian; Okseniuk, Dorota; Raillon, Franck; Schäfer, Annegret |
| Abstract: | This paper proposes a framework for monitoring risks arising from the build-up of leverage in EU-domiciled alternative investment funds (AIFs) and examines policy tools that could be effective in mitigating these risks in line with international recommendations. We develop a novel framework that combines confidential fund-level and transaction-level data on derivatives and repurchase agreements to present a comprehensive overview of the sources of leverage in highly leveraged AIFs. Using a range of risk metrics, our analysis identifies hedge funds and funds pursuing liability-driven investment (LDI) strategies as the most vulnerable to leverage-related risks. If interest rates rise, LDI funds may face significant mark-to-market losses and liquidity needs due to margin and collateral calls. Hedge funds appear to be more resilient against this type of shock but are sensitive to credit risk, especially hedge funds with relative value strategies. To mitigate these risks, we evaluate the impact of a range of policy tools such as leverage limits and minimum haircuts on collateral used for repo. Our analysis shows that the impact of the tools depends on the types of funds considered. Imposing a direct limit on net leverage of ten times the net asset value would lead to a sizeable reduction in the net exposures of hedge funds, but would barely affect other leveraged AIFs. Minimum haircuts on collateral would most likely affect only hedge funds with relative value strategies, as LDI funds, which already operate under a leverage limit, appear to have enough unencumbered assets to meet any additional collateral requirements. Overall, our findings suggest a need for tailored policy designs and highlight the complex interplay between different regulatory measures. JEL Classification: G15, G23, G28 |
| Keywords: | AIFMD, alternative investment funds, derivatives, EMIR, haircut, initial margin, leverage, leverage limit, repo, SFTR, synthetic, yield buffer |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:srk:srkops:202528 |
| By: | Sotirios D. Nikolopoulos |
| Abstract: | Evaluating rare-event forecasts is challenging because standard metrics collapse as event prevalence declines. Measures such as F1-score, AUPRC, MCC, and accuracy induce degenerate thresholds -- converging to zero or one -- and their values become dominated by class imbalance rather than tail discrimination. We develop a family of rare-event-stable (RES) metrics whose optimal thresholds remain strictly interior as the event probability approaches zero, ensuring coherent decision rules under extreme rarity. Simulations spanning event probabilities from 0.01 down to one in a million show that RES metrics maintain stable thresholds, consistent model rankings, and near-complete prevalence invariance, whereas traditional metrics exhibit statistically significant threshold drift and structural collapse. A credit-default application confirms these results: RES metrics yield interpretable probability-of-default cutoffs (4-9%) and remain robust under subsampling, while classical metrics fail operationally. The RES framework provides a principled, prevalence-invariant basis for evaluating extreme-risk forecasts. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.00916 |
| By: | Wu, Shujie; Huang, Joshua; Serra, Teresa |
| Abstract: | This article shows how high-frequency market data relates to low frequency events by examining the economic value of using intraday data to hedge commodity spot prices in the futures market. We use the realized minimum-variance hedging ratio (RMVHR) framework, which depends on the realized futures-cash covariance matrix forecast. We focus on the crude oil crack and soybean crush industries and consider both multiple and single-commodity portfolios, as well as different forecast strategies based on intraday data. We use the Naïve hedging ratio as the benchmark to investigate the performance of intraday data-based hedging models. Our results suggest that for each portfolio considered, there is usually one intraday data-based hedging strategy that outperforms the Naïve. Superior performance, however, is not always statistically significant, for the crack industry. Our estimates place the advantage of using intraday data between $7, 155.00 and $287.50 per contract and year on average, with these values representing the decline in the portfolio’s standard deviation achieved through hedging. This points at a promising path to improving the performance of hedging in the commodity space based on intraday data. |
| Keywords: | Agricultural and Food Policy, Demand and Price Analysis |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:nccc24:379012 |
| By: | Viktoria Alaverdyan (Central Bank of Armenia); Gevorg Minasyan (Central Bank of Armenia); Aleksandr Shirkhanyan (Central Bank of Armenia) |
| Abstract: | This paper examines whether macroprudential foreign exchange (FX) regulations unintentionally shift currency risk to sectors not directly targeted by such measures. Using a difference-indifferences framework and a highly granular dataset combining loan-level credit registry data with bank-level balance sheet information, we analyse how Armenian banks adjusted their portfolios following the introduction of a differentiated loan-to-value (LTV) regulation that imposed stricter limits on FX-denominated mortgages. The results show that the differentiated LTV, while tightening borrowing conditions for FX-denominated mortgages, also led to an increase in the dollarization of business loans and a higher share of foreign-currency bonds in banks' portfolios. These shifts imply that FX-related macroprudential policies can reallocate rather than reduce currency risk, emphasizing the need for system-wide oversight to prevent its build-up in unregulated segments of the financial system. |
| Keywords: | Macroprudential policy; Foreign exchange regulation; Loan-to-value limits; Dollarization; Bank portfolio reallocation |
| JEL: | E58 G21 G28 F31 E44 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:ara:wpaper:wp-2025-04 |
| By: | Bullock, David W.; Okoto, Edna M. |
| Abstract: | The predictive ability of two alternative forward price distribution forecasting methods based upon the full range of option premiums was developed and tested using 10 years of price and premium history for five traded commodities. The two models were a best-fit parametric distribution and a non-parametric linear interpolation fit. These were compared to two traditional approaches: historical time series and Black-76 option implied volatility. The forecast horizons ranged from 6 months to 1 week in duration. A modification of the theoretical results of King and Fackler (1985) nonparametric option pricing model was presented to justify the fitting of a price probability density function to the option premiums with the intrinsic value removed. Time series fits to the historical futures price indicted that the integrated ARCH (1) and GARCH (1, 1) models were the most prevalent best fit to the data. For parametric fits to the option premiums, the Burr Type XII and Dagum distributions were the most prevalent best fits. Predictive ability was measured using 10-percent value-at-risk portfolio models for simple short and long futures positions where the number of actual exceptions was compared to the theoretical values. The predictive results indicated that the parametric and non-parametric distribution fits performed best on the short futures portfolios over the longer-term forecast horizons (6- and 3-months) while the Black-76 performed best over the same time horizon. For the shorter time horizons (1-month or less), the Black-76 and time series methods performed best. These results point to the possibility that a hybrid Black-76 and premium distribution fit approach (via a splice) might perform best for longer-term projections. |
| Keywords: | Agricultural and Food Policy, Demand and Price Analysis |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:nccc24:379004 |
| By: | Toshiaki Yamanaka |
| Abstract: | We develop a robust linear-quadratic mean-field control framework for systemic risk under model uncertainty, in which a central bank jointly optimizes interest rate policy and supervisory monitoring intensity against adversarial distortions. Our model features multiple policy instruments with interactive dynamics, implemented via a variance weight that depends on the policy rate, generating coupling effects absent in single-instrument models. We establish viscosity solutions for the associated HJB--Isaacs equation, prove uniqueness via comparison principles, and provide verification theorems. The linear-quadratic structure yields explicit feedback controls derived from a coupled Riccati system, preserving analytical tractability despite adversarial uncertainty. Simulations reveal distinct loss-of-control regimes driven by robustness-breakdown and control saturation, alongside a pronounced asymmetry in sensitivity between the mean and variance channels. These findings demonstrate the importance of instrument complementarity in systemic risk modeling and control. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.04704 |
| By: | Ludington, Evan; Liao, Yanjun (Penny) (Resources for the Future); Walls, Margaret A. (Resources for the Future) |
| Abstract: | In 2022, California implemented a major insurance reform requiring insurance companies to provide premium discounts to policyholders who undertake wildfire hazard mitigation, such as installation of fire-resistant roofs, vents, and windows and maintaining defensible space around homes. To evaluate early implementation of this reform, we draw on insurance rules and rate filings to create a database of mitigation discounts offered by insurers. We analyze how discount amounts vary across mitigation measures, insurers, and regions, and assess whether they are large enough to motivate homeowners to undertake these actions. We also compare the California policy to similar policies in states subject to hurricane and windstorm risks. Our results indicate that the current discounts are small: the costs of property retrofits are orders of magnitude greater than the insurance savings. They are also considerably smaller than wind insurance discounts in other states, which we attribute largely to greater uncertainty in the effectiveness of individual wildfire mitigation efforts, coupled with risk externalities from structure-to-structure fire spread and community-level fuel hazards that weaken the link between household-level investments and expected insurer losses. |
| Date: | 2025–12–10 |
| URL: | https://d.repec.org/n?u=RePEc:rff:dpaper:dp-25-30 |
| By: | Mohammad Rezoanul Hoque; Md Meftahul Ferdaus; M. Kabir Hassan |
| Abstract: | Reinforcement learning (RL) is an innovative approach to financial decision making, offering specialized solutions to complex investment problems where traditional methods fail. This review analyzes 167 articles from 2017--2025, focusing on market making, portfolio optimization, and algorithmic trading. It identifies key performance issues and challenges in RL for finance. Generally, RL offers advantages over traditional methods, particularly in market making. This study proposes a unified framework to address common concerns such as explainability, robustness, and deployment feasibility. Empirical evidence with synthetic data suggests that implementation quality and domain knowledge often outweigh algorithmic complexity. The study highlights the need for interpretable RL architectures for regulatory compliance, enhanced robustness in nonstationary environments, and standardized benchmarking protocols. Organizations should focus less on algorithm sophistication and more on market microstructure, regulatory constraints, and risk management in decision-making. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.10913 |