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on Risk Management |
| By: | Emanuele Dri; Achille Yomi; Muthumanimaran Vetrivelan; Cedric Kuassivi; Iv\`an Diego Exposito |
| Abstract: | In this paper, we present an approach for estimating significant financial metrics within risk management by utilizing quantum phenomena for random number generation. We explore Quantum-Enhanced Monte Carlo, a method that combines traditional and quantum techniques for enhanced precision through Quantum Random Numbers Generation (QRNG). The proposed methods can be based on the use of photonic phenomena or quantum processing units to generate random numbers. The results are promising, hinting at improved accuracy with the proposed methods and slightly lower estimates (both for VaR and CVaR estimation) using the quantum-based methodology. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.02125 |
| By: | Omar Briceno Cruzado |
| Abstract: | The Basel Committee on Banking Supervision proposed replacing all approaches for operational risk capital, including the Advanced Measurement Approach (AMA), with a simplified formula called the Standardized Measurement Approach (SMA). This paper examines and criticizes the weaknesses and failures of SMA, such as instability, insensitivity to risk, superadditivity, and the implicit relationship between the SMA capital model and systemic risk in the banking sector. Furthermore, it discusses the issues of the proposed Operational Risk Capital (OpCar) model by the Basel Committee, a precursor to SMA. The paper concludes by advocating for the maintenance of the AMA internal model framework and suggests a series of standardization recommendations to unify internal operational risk modeling. The findings and viewpoints presented in this paper have been discussed and supported by numerous operational risk professionals and academics from various regions of the world. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00962 |
| By: | Benbekhti Seyf Eddine (Université Aboubekr Belkaid - University of Belkaïd Abou Bekr [Tlemcen]); Boulila Hadjer (Université Aboubekr Belkaid - University of Belkaïd Abou Bekr [Tlemcen]); Benbouziane Mohamed (Université Aboubekr Belkaid - University of Belkaïd Abou Bekr [Tlemcen]) |
| Abstract: | This paper investigates the safe haven potential of Islamic stocks within the context of the London stock market, alongside other prominent hedging assets, namely gold, Brent crude, and Bitcoin, using the M-DCC GARCH model. The study finds that Islamic finance no longer demonstrates robust safe haven characteristics but instead serves as a diversifier during periods of market stability. In contrast, gold exhibits notable hedging properties both prior to the COVID-19 pandemic and during market turmoil, outperforming all assets except Bitcoin |
| Keywords: | Islamic stocks safe haven M-DCC GARCH model covid-19. JEL Classification Codes: G11 G15 G17 G21 Islamic stocks conventional stock market or cryptocurrencies? Looking, Islamic stocks, safe haven, M-DCC GARCH model, covid-19. JEL Classification Codes: G11, G15, G17, G21 Islamic stocks, conventional stock market, or cryptocurrencies? Looking |
| Date: | 2023–11–30 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:halshs-04521347 |
| By: | Spencer Andrews; Salil Gadgil |
| Abstract: | This paper uses supervisory data to analyze how performance-based compensation influences a hedge fund manager’s investment strategy and associated risk (Working Paper no. 24-07). |
| Date: | 2024–10–30 |
| URL: | https://d.repec.org/n?u=RePEc:ofr:wpaper:24-07 |
| By: | Yaming Chang |
| Abstract: | This paper applies the realized exponential generalized autoregressive conditional heteroskedasticity (REGARCH) model to analyze the Nikkei 225 index from 2010 to 2017, utilizing realized variance (RV) and realized range-based volatility (RRV) as high-frequency measures of volatility. The findings show that REGARCH models outperform standard GARCH family models in both in-sample fitting and out-of-sample forecasting, driven by the dynamic information embedded in high-frequency realized measures. Incorporating multiple realized measures within a joint REGARCH framework further enhances model performance. Notably, RRV demonstrates superior predictive power compared to RV, as evidenced by improvements in forecast accuracy metrics. Moreover, the forecasting results remain robust under both rolling-window and recursive evaluation schemes. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.02695 |
| By: | Chintamani Bagwe |
| Abstract: | RegTech is a rapidly rising financial services sector focused on using cutting-edge technology to improve the process of regulatory compliance. RegTech solutions are characterized by numerous features and benefits that can considerably contribute to helping organizations operate effectively in the increasingly regulated environment, when it comes to compliance and risk management. This paper sheds light on why RegTech will be one of the most promising markets, driven by the rising cost of compliance and the growing reliance on technology in crisis management. Moreover, this paper will examine the advantages of using such solutions to strike a balance between compliance and operational efficiencies. This paper will deepen the understanding of regulatory compliance, introduce RegTech, and examine the benefits of using these solutions to achieve compliance. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.18910 |
| By: | Preben Forer; Barak Budnick; Pierpaolo Vivo; Sabrina Aufiero; Silvia Bartolucci; Fabio Caccioli |
| Abstract: | We analyze the stability of financial investment networks, where financial institutions hold overlapping portfolios of assets. We consider the effect of portfolio diversification and heterogeneous investments using a random matrix dynamical model driven by portfolio rebalancing. While heterogeneity generally correlates with heightened volatility, increasing diversification may have a stabilizing or destabilizing effect depending on the connectivity level of the network. The stability/instability transition is dictated by the largest eigenvalue of the random matrix governing the time evolution of the endogenous components of the returns, for which different approximation schemes are proposed and tested against numerical diagonalization. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.19260 |
| By: | Blazsek, Szabolcs; Ayala, Astrid |
| Abstract: | Score-driven filters are updated by the scaled gradient of the log-likelihood (LL). The gradient is with respect to a dynamic parameter and the scaling parameter is 1, or the information quantity or its square root in the literature. The information quantity is minus the expected value of the Hessian of the LL with respect to a dynamic parameter, i.e. the Hessianis smoothed using a probability-weighted average for each period. We suggest an alternative approach and scale the gradients using novel Hessian-driven filters, i.e. Hessian smoothing is performed over time. The method can be used for score-driven models in general. We illustrate it for Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity). Weuse Standard & Poor's 500 (S&P 500) data. We show empirical results for in-sample statistical performance from 2015 to 2025, and out-of-sample forecasting performance from 2021 to 2025. We find for the S&P 500 that the Hessian-driven scaling is superior to the existing scaling methods for Beta-t-EGARCH. We find similar results for a Monte Carlo simulation experimentwhere misspecified Beta-t-EGARCH models with constant and Hessian-driven gradient scaling are estimated for returns generated by a Markov-switching (MS) Beta-t-EGARCH. Hessianbased gradient scaling captures regime-switching dynamics better than constant gradient scaling. |
| Keywords: | Dynamic conditional score (DCS); Generalized autoregressive score (GAS); Dynamic gradient scaling parameters in score driven filters; Gradient descent; Newton's method |
| JEL: | C22 C32 |
| Date: | 2025–02–17 |
| URL: | https://d.repec.org/n?u=RePEc:cte:werepe:45978 |
| By: | Aase, Knut K. (Dept. of Business and Management Science, Norwegian School of Economics) |
| Abstract: | We consider optimal risk sharing where agents have preferences represented by translation invariant recursive utility. The dynamics in continuous time is driven by diffusion processes and a random jump measure. The model has some appealing features compared to the scale invariant version. Economic effects of sudden events, like catastrophes or pandemics, can be interpreted and separated from ordinary shocks to the economy. Unlike the scale invariant version, this model allows for a treatment of heterogeneous preferences, and consequently optimal risk sharing at a general and basic level. A new endogenous variable, a traded security, enters via the preference structure, affecting the key relations between agents. We also implement a stock market in this setting, and derive a consumption based capital asset model. A catastrophe-insurance forward contract is analyzed as an application of our general model, where the jump part is priced and plays the essential role. |
| Keywords: | Optimal risk sharing; recursive utility; translation invariance; jump dynamics; CCAPM; the stochastic maximum principle; the mutuality principle; catastrophe forward contracts |
| JEL: | C40 C41 C53 R40 R41 |
| Date: | 2025–02–21 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:nhhfms:2025_005 |
| By: | Christian Fieberg; Lars Hornuf; Maximilian Meiler; David J. Streich |
| Abstract: | We study whether large language models (LLMs) can generate suitable financial advice and which LLM features are associated with higher-quality advice. To this end, we elicit portfolio recommendations from 32 LLMs for 64 investor profiles, which differ in their risk preferences, home country, sustainability preferences, gender, and investment experience. Our results suggest that LLMs are generally capable of generating suitable financial advice that takes into account important investor characteristics when determining market and risk exposures. The historical performance of the recommended portfolios is on par with that of professionally managed benchmark portfolios. We also find that foundation models and larger models generate portfolios that are easier to implement and more sensitive to investor characteristics than fine-tuned models and smaller models. Some of our results are consistent with LLMs inheriting human biases such as home bias. We find no evidence of gender-based discrimination, which can be found in human financial advice. |
| Keywords: | generative AI, artificial intelligence, large language models, financial advice portfolio management |
| JEL: | G00 G11 G40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11666 |
| By: | Hacioğlu-Hoke, Sinem (Federal Reserve Board and Centre for Economic Policy Research); Ostry, Daniel (Bank of England); Rey, Hélène (London Business School and Centre for Economic Policy Research); Rousset Planat, Adrien (London Business School); Stavrakeva, Vania (London Business School and Centre for Economic Policy Research); Tang, Jenny (Federal Reserve Bank of Boston) |
| Abstract: | We analyse the behaviour of all financial and non‑financial firms active in the UK FX derivatives market – the largest global centre for currency trading – using transaction‑level data. Based on firm‑level net currency derivatives exposures, we find that UK and EU pension funds, investment funds, insurers, and non‑financial corporations use FX derivatives primarily for hedging purposes, with dealer banks accommodating these clients’ hedging needs. In contrast, hedge funds predominantly utilise FX derivatives to speculate, with their trading activity consistent with carry trade, momentum, and macroeconomic news investment strategies. Lastly, the paper documents many novel facts that should motivate theoretical models. |
| Keywords: | FX derivatives; exchange rates; non-bank financial institutions; banks; non‑financial corporations; hedging; speculation; macro news |
| JEL: | F30 F31 G15 G20 |
| Date: | 2024–12–13 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1103 |
| By: | Dasol Kim; William Goetzmann; Robert Shiller |
| Abstract: | Narratives about past stock market crashes may affect investor concerns about crashes and create feedback loops in current markets (Working Paper no. 23-10). |
| Keywords: | stock market crash, financial crisis, market volatility |
| Date: | 2023–12–28 |
| URL: | https://d.repec.org/n?u=RePEc:ofr:wpaper:23-10 |
| By: | Nariman Djoudi (Université Yahia Fares de Médéa); Kheira Belhamri (Université Yahia Fares de Médéa) |
| Abstract: | This study aims to highlight the importance and effectiveness of using the Altman and Sherrod models in predicting the financial failure of Saidal institution during the period 2017-2020 to give early warning in disclosing the likelihood of bankruptcy. In order to achieve the objectives of the study, the two models were applied based on the institution's financial statements and the most important financial indicators. Thus, the study found that the two models are effective in predicting the future financial failure of Saidal institution during the period studied, 100% is needed. |
| Keywords: | Altman Model Sherrod Model Financial Failure Prediction Saidal Institution. JEL Classification Codes: G17 G32 M4, Altman Model, Sherrod Model, Financial Failure Prediction, Saidal Institution. JEL Classification Codes: G17, G32, M4 |
| Date: | 2023–12–30 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:halshs-04521389 |
| By: | Thomas Ruchti; Yashar Barardehi; Andrew Bird; Stephen A. Karolyi |
| Abstract: | Temporary short-selling restrictions, triggered by a sharp decline in a stock’s price, reduce market volatility and improve pricing and market liquidity (Working Paper no. 23-08). |
| Keywords: | short-selling, short selling, market stability, uptick rule, securities regulation, Rule 201, short-selling restrictions |
| Date: | 2023–10–11 |
| URL: | https://d.repec.org/n?u=RePEc:ofr:wpaper:23-08 |
| By: | Thomas M. Eisenbach; Gregory Phelan |
| Abstract: | The market for U.S. Treasury securities experienced extreme stress in March 2020, when prices dropped precipitously (yields spiked) over a period of about two weeks. This was highly unusual, as Treasury prices typically increase during times of stress. Using a theoretical model, we show that markets for safe assets can be fragile due to strategic interactions among investors who hold Treasury securities for their liquidity characteristics. Worried about having to sell at potentially worse prices in the future, such investors may sell preemptively, leading to self-fulfilling “market runs” that are similar to traditional bank runs in some respects. Our results motivate potential policy interventions to stabilize the market during times of stress and disruption (Working Paper no. 23-02). |
| Date: | 2023–04–03 |
| URL: | https://d.repec.org/n?u=RePEc:ofr:wpaper:23-02 |
| By: | Huneeus, Federico; Kaboski, Joseph P.; Larrain, Mauricio; Sergio Schmukler; Vera, Mario |
| Abstract: | This paper studies how credit guarantee and employment protection programs interact in assisting firms during crises times. The paper analyzes how these government programs influence credit allocation, indebtedness, and risk at both the micro and macro levels. The programs provide different incentives for firms. The low interest rate encourages riskier firms to demand government-backed credit, while banks tend to reject those credit applications. The credit demand outweighs this screening supply response, expanding micro-level indebtedness across the extensive and intensive margins among riskier firms. The uptake of the employment program is not associated with risk, as firms internalize the opportunity cost of reduced operations when sending workers home to qualify for assistance. The employment program mitigates the indebtedness expansion of the credit program by supporting firms and enabling banks to screen firms better. Macroeconomic risk of the credit program would increase by a third without the availability of the employment program. |
| Date: | 2024–10–24 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10958 |
| By: | Kamer Ali Yuksel; Hassan Sawaf |
| Abstract: | Financial metrics like the Sharpe ratio are pivotal in evaluating investment performance by balancing risk and return. However, traditional metrics often struggle with robustness and generalization, particularly in dynamic and volatile market conditions. This paper introduces AlphaSharpe, a novel framework leveraging large language models (LLMs) to iteratively evolve and optimize financial metrics. AlphaSharpe generates enhanced risk-return metrics that outperform traditional approaches in robustness and correlation with future performance metrics by employing iterative crossover, mutation, and evaluation. Key contributions of this work include: (1) an innovative use of LLMs for generating and refining financial metrics inspired by domain-specific knowledge, (2) a scoring mechanism to ensure the evolved metrics generalize effectively to unseen data, and (3) an empirical demonstration of 3x predictive power for future risk-return forecasting. Experimental results on a real-world dataset highlight the superiority of AlphaSharpe metrics, making them highly relevant for portfolio managers and financial decision-makers. This framework not only addresses the limitations of existing metrics but also showcases the potential of LLMs in advancing financial analytics, paving the way for informed and robust investment strategies. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00029 |
| By: | Gregory Phelan; David Love |
| Abstract: | This research will help policymakers understand how economic growth, risk, and the financial sector influence sustainability objectives. It provides a useful theoretical framework useful to help assess what policies related to growth and financial depth are likely to affect sustainability (Working Paper no. 23-05). |
| Date: | 2023–05–16 |
| URL: | https://d.repec.org/n?u=RePEc:ofr:wpaper:23-05 |
| By: | KOUAKOU, Thiédjé Gaudens-Omer |
| Abstract: | This paper analyzes the effect of Basel III adapted to WAEMU on the behavior of banks in the zone (intermediation and market activities). After having developed a model for optimizing the return on bank equity, under various constraints (balance sheet constraints, Basel III regulatory constraints), we resort to linear programming via the Danzig simplex algorithm and to a structure of reasonable rates to obtain the optimal values of the various bank balance sheet items. The results, obtained by comparing these theoretical values with the values observed before Basel III (before January 1, 2018), show an increase in the supply of loans, obtained not only from deposits and bank refinancing but also via resources from the financial markets. We can also observe the intuitive result of an increase of bank reserves in line with the constraint that Basel III imposes on banks to increase their liquidity. In short, Basel III tends to strengthen bank financing in the zone, while improving the soundness of banks through the constitution of larger reserves. |
| Keywords: | prudential regulation, calibration, credit supply, linear programming |
| JEL: | C44 E50 E58 |
| Date: | 2025–01–31 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123515 |