|
on Risk Management |
| By: | Duran-Fernandez, Roberto |
| Abstract: | This paper proposes a new framework that enhances traditional risk assessment by incorporating higher-order statistical moments—specifically skewness and kurtosis—through the Cornish-Fisher expansion. Standard risk models, which rely primarily on mean and variance, often underestimate the financial buffers required to hedge against extreme, low-probability shocks. To address this limitation, this study applies the Cornish-Fisher expansion to develop a more comprehensive framework that quantifies the economic impact of extreme events, with direct implications for financial institutions and policymakers. Utilizing a two-period intertemporal consumption model, this study employs Monte Carlo simulations to assess how catastrophic shocks—such as the COVID-19 pandemic—affect key economic indicators. The results demonstrate that negative skewness amplifies downside risk, necessitating stronger precautionary savings and financial reserves. Furthermore, the persistent underinvestment in global preparedness—particularly in pandemic risk management—can be attributed to conventional risk models that fail to capture the full severity of extreme events. The perspective presented in this paper not only enhances theoretical models but also has critical implications for practical applications, particularly in risk management and policy design, where extreme outcomes must be carefully accounted for. |
| Keywords: | Catastrophic Risk, Extreme Event Modeling, Financial Buffers |
| JEL: | G32 C15 D81 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:310336 |
| By: | Peter Albrecht; Evžen Kočenda; Evžen Kocenda |
| Abstract: | Our study presents an in-depth analysis of the interconnectedness in returns among five major cryptocurrencies from 2018 to 2023. Our work introduces novel findings by employing a novel bootstrap-after-bootstrap method of Greenwood-Nimmo et al. (2024) to establish a link between increases in connectedness and various systematic events. We found a clear rise in connectedness within a month following the event for ten endogenously selected events. Further, we identify Bitcoin and Ethereum as net return transmitters, mainly to Binance coin and Ripple. Moreover, we found that these transmissions increased by up to 20% for up to one month after the shocks occurred. We calculate optimal portfolio weights and hedging ratios for cryptocurrency risk management. Our findings reveal that Cardano and Ripple are the most effective choices in portfolio optimization. The implications of this study are significant for devising strategies in portfolio management and risk hedging, offering valuable guidance for policy formulation in the financial sector. |
| Keywords: | return connectedness, cryptocurrencies, bootstrap-after-bootstrap procedure, portfolio composition and hedging |
| JEL: | H56 G11 G15 Q40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11658 |
| By: | Heming Chen |
| Abstract: | This study systematically examines how several alternative approaches considered affect three aspects that determine portfolio performance (the gross return, the transaction costs and the portfolio risk). We find that it is difficult to exploit the possible predictability of asset returns. However, the predictability of asset return volatility produces obvious economic value, although in a highly correlated cryptocurrencies market. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.12841 |
| By: | Stéphane Surprenant |
| Abstract: | Recent rises in macroeconomic volatility have prompted the introduction of quantile vector autoregression (QVAR) models to forecast macroeconomic risk. This paper provides an extensive evaluation of the predictive performance of QVAR models in a pseudo-out-of-sample experiment spanning 112 monthly US variables over 40 years, with horizons of 1 to 12 months. We compare QVAR with three parametric benchmarks: a Gaussian VAR, a generalized autoregressive conditional heteroskedasticity VAR and a VAR with stochastic volatility. QVAR frequently, significantly and quantitatively improves upon the benchmarks and almost never performs significantly worse. Forecasting improvements are concentrated in the labour market and interest and exchange rates. Augmenting the QVAR model with factors estimated by principal components or quantile factors significantly enhances macroeconomic risk forecasting in some cases, mostly in the labour market. Generally, QVAR and the augmented models perform equally well. We conclude that both are adequate tools for modeling macroeconomic risks. |
| Keywords: | Econometrics and statistical methods; Business fluctuations and cycles |
| JEL: | C53 E37 C55 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:25-4 |
| By: | Leslie Sheng Shen |
| Abstract: | Geopolitical risk has intensified in recent years, driven by events such as Russia’s invasion of Ukraine, escalating tensions between the United States and China, and conflicts in the Middle East. But how risky is the geopolitical landscape according to US firms? This brief presents a new index based on earnings call transcripts that reflects US firms’ perceptions of geopolitical risk and examines how those assessments affect their future investment, that is, their spending on long-term assets such as facilities, equipment, and technology. |
| Keywords: | geopolitical risk; firm investment; cash position |
| JEL: | D80 E22 G30 |
| Date: | 2025–02–13 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbcq:99551 |
| By: | Jinghai He; Cheng Hua; Chunyang Zhou; Zeyu Zheng |
| Abstract: | We develop a portfolio allocation framework that leverages deep learning techniques to address challenges arising from high-dimensional, non-stationary, and low-signal-to-noise market information. Our approach includes a dynamic embedding method that reduces the non-stationary, high-dimensional state space into a lower-dimensional representation. We design a reinforcement learning (RL) framework that integrates generative autoencoders and online meta-learning to dynamically embed market information, enabling the RL agent to focus on the most impactful parts of the state space for portfolio allocation decisions. Empirical analysis based on the top 500 U.S. stocks demonstrates that our framework outperforms common portfolio benchmarks and the predict-then-optimize (PTO) approach using machine learning, particularly during periods of market stress. Traditional factor models do not fully explain this superior performance. The framework's ability to time volatility reduces its market exposure during turbulent times. Ablation studies confirm the robustness of this performance across various reinforcement learning algorithms. Additionally, the embedding and meta-learning techniques effectively manage the complexities of high-dimensional, noisy, and non-stationary financial data, enhancing both portfolio performance and risk management. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.17992 |
| By: | Miori Nagashima (Hokkaido University, Sapporo, Japan) |
| Abstract: | In late modern times, understanding “institutional risk perception†has become increasingly important (Freudenburg and Pastor 1992), and studies have been conducted to examine how various institutions amplify and attenuate risks (Rothstein 2003; Tezuka 2006). This paper focuses on a sexual harassment accusation raised by a female journalist against a top governmental bureaucrat in Japan in 2018. Rather than focusing on individuals, the analysis centers on how the institutions involved, TV Asahi and the Ministry of Finance (MOF, hereinafter), managed this crisis. The paper analyzes the sequence of various measures taken by these organizations, distinguishing inward- and outward-looking risks (Crandall, Parnell, and Spillan 2010). It argues that the attenuation measures the MOF adopted to alleged scandals increased public perception of risk and enhanced its crisis since its crisis management lacked consistency, changing schemes several times depending on criticisms they get. On the other hand, TV Asahi needed to face more inward-looking risk, verifying the adequacy of the organization’s response previously taken to the journalist when she consulted about the harassment incidents with her superior. Both institutions exhibited a lack of consistency and accountability, which are two important values for effective institutional risk management. |
| Keywords: | sexual harassment, institutional attenuation, crisis management, inward-looking risk, outward-looking risk |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0469 |
| By: | Laura Capera Romero (Vrije Universiteit Amsterdam and Tinbergen Institute); Anne Opschoor (Vrije Universiteit Amsterdam and Tinbergen Institute) |
| Abstract: | This paper disentangles the added value of using high-frequency-based (realized) covariance measures on multivariate volatility forecasting into two pillars: the realized variances and realized correlations and quantifies the corresponding economic gains using a broad set of portfolio performance metrics. Using state-of-the-art models based on daily returns and realized (co)variances, we predict the conditional covariance matrix on a daily, weekly, biweekly, and monthly frequency, both for dimensions 30 and 50. We evaluate the forecasts statistically using various loss functions and economically by constructing Global Minimum Variance (GMV) portfolios. Using a data set of 50 liquid U.S. stocks from 2001 to 2019, we find that the inclusion of realized variances largely accounts for the improvement in statistical forecast performance (between 65% and at least 78%). The results on the GMV portfolios show that realized covariance models exhibit lower ex-post volatility but tend to produce substantially lower ex-post mean returns compared to models with realized variances and daily returns. Consequently, Sharpe Ratios increase roughly by 35%, leading to significant utility gains, equivalent to up to 500 basis points per year. Combined, our results indicate that there is no economic gain by modeling correlations dynamically, either using daily returns or realized correlations. |
| Keywords: | multivariate volatility, high-frequency data, realized variances, realized correlations |
| JEL: | C32 C58 G17 |
| Date: | 2024–11–03 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240059 |
| By: | Ying Chen; Paul Griffin; Paolo Recchia; Zhou Lei; Hongrui Chang |
| Abstract: | Recovery rate prediction plays a pivotal role in bond investment strategies, enhancing risk assessment, optimizing portfolio allocation, improving pricing accuracy, and supporting effective credit risk management. However, forecasting faces challenges like high-dimensional features, small sample sizes, and overfitting. We propose a hybrid Quantum Machine Learning model incorporating Parameterized Quantum Circuits (PQC) within a neural network framework. PQCs inherently preserve unitarity, avoiding computationally costly orthogonality constraints, while amplitude encoding enables exponential data compression, reducing qubit requirements logarithmically. Applied to a global dataset of 1, 725 observations (1996-2023), our method achieved superior accuracy (RMSE 0.228) compared to classical neural networks (0.246) and quantum models with angle encoding (0.242), with efficient computation times. This work highlights the potential of hybrid quantum-classical architectures in advancing recovery rate forecasting. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.15828 |
| By: | Yun-Shi Dai; Peng-Fei Dai; St\'ephane Goutte; Duc Khuong Nguyen; Wei-Xing Zhou |
| Abstract: | Stable and efficient food markets are crucial for global food security, yet international staple food markets are increasingly exposed to complex risks, including intensified risk contagion and escalating external uncertainties. This paper systematically investigates risk spillovers in global staple food markets and explores the key determinants of these spillover effects, combining innovative decomposition-reconstruction techniques, risk connectedness analysis, and random forest models. The findings reveal that short-term components exhibit the highest volatility, with futures components generally more volatile than spot components. Further analysis identifies two main risk transmission patterns, namely cross-grain and cross-timescale transmission, and clarifies the distinct roles of each component in various net risk spillover networks. Additionally, price drivers, external uncertainties, and core supply-demand indicators significantly influence these spillover effects, with heterogeneous importance of varying factors in explaining different risk spillovers. This study provides valuable insights into the risk dynamics of staple food markets, offers evidence-based guidance for policymakers and market participants to enhance risk warning and mitigation efforts, and supports the stabilization of international food markets and the safeguarding of global food security. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.15173 |
| By: | Jiawen Luo (School of Business Administration, South China University of Technology, Guangzhou 510640); Shengjie Fu (School of Business Administration, South China University of Technology, Guangzhou 510640); Oguzhan Cepni (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | In this paper, we construct a set of reverse-Mixed Data Sampling (MIDAS) models to forecast the daily realized covariance matrix of United States (US) state-level stock returns, derived from 5-minute intraday data, by incorporating the information of volatility of weekly economic condition indices, which serve as proxies for economic uncertainty. We decompose the realized covariance matrix into a diagonal variance matrix and a correlation matrix and forecasting them separately using a two-step procedure. Particularly, the realized variances are forecasted by combining Heterogeneous Autoregressive (HAR) model with the reverse-MIDAS framework, incorporating the low-frequency uncertainty variable as a predictor. While the forecasting of the correlation matrix relies on the scalar MHAR model and a new log correlation matrix parameterization of Archakov and Hansen (2021). Our empirical results demonstrate that the forecast models incorporating uncertainty associated with economic conditions outperform the benchmark model in terms of both in-sample fit and out-of-sample forecasting accuracy. Moreover, economic evaluation results suggest that portfolios based on the proposed reverse-MIDAS covariance forecast models generally achieve higher annualized returns and Sharpe ratios, as well as lower portfolio concentrations and short positions. |
| Keywords: | US state-level stock returns, Covariance matrix, Uncertainty, Reverse-MIDAS, Forecasting |
| JEL: | C22 C32 C53 D80 G10 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202501 |
| By: | Ayush Jha; Abootaleb Shirvani; Ali Jaffri; Svetlozar T. Rachev; Frank J. Fabozzi |
| Abstract: | This study presents the Adaptive Minimum-Variance Portfolio (AMVP) framework and the Adaptive Minimum-Risk Rate (AMRR) metric, innovative tools designed to optimize portfolios dynamically in volatile and nonstationary financial markets. Unlike traditional minimum-variance approaches, the AMVP framework incorporates real-time adaptability through advanced econometric models, including ARFIMA-FIGARCH processes and non-Gaussian innovations. Empirical applications on cryptocurrency and equity markets demonstrate the proposed framework's superior performance in risk reduction and portfolio stability, particularly during periods of structural market breaks and heightened volatility. The findings highlight the practical implications of using the AMVP and AMRR methodologies to address modern investment challenges, offering actionable insights for portfolio managers navigating uncertain and rapidly changing market conditions. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.15793 |
| By: | Liliana Rojas-Suarez (Center for Global Development) |
| Abstract: | Basel III—the international standard for banking regulation—has strengthened global financial stability but has also led to unintended consequences that may hinder progress toward key Sustainable Development Goals (SDGs). This paper examines how Basel III’s regulatory framework may restrict bank lending to SMEs (impacting SDG 10) and constrain infrastructure finance (impacting SDG 8). Addressing these challenges requires refining risk assessment methodologies while preserving Basel III’s core objective: accurate risk evaluation. For SMEs, tailoring risk weights using local credit registry data can better reflect economic conditions in emerging markets. For infrastructure, recognizing it as a distinct asset class and leveraging credit risk mitigation tools could improve financing. Greater engagement from multilateral institutions, particularly the World Bank, is essential to advancing these solutions while maintaining financial stability. |
| Keywords: | Basel III, Sustainable Development Goals, Infrastructure Finance, SME Lending |
| JEL: | G21 G28 O1 O18 |
| Date: | 2025–02–10 |
| URL: | https://d.repec.org/n?u=RePEc:cgd:ppaper:351 |
| By: | Hazell, Peter; Timu, Anne G. |
| Abstract: | Much of the recent literature on agricultural insurance focuses on ways to increase farmers’ demand for insurance, but this paper revisits the supply side of the insurance market. To better understand the conditions under which private insurance has been successful or failed the paper draws on the available empirical and theoretical literature, on case studies, and interviews with selected insurers. While there are many examples of innovative solutions to some of the product design, marketing and delivery challenges facing agricultural insurance, our review suggests that private unsubsidized insurance can only play a limited role in terms of the overall risk management needs of agriculture. Fundamentally, agricultural insurance can only address certain types of risks, and these are often not the most important from the farmers’ perspective. For most farmers insurance is best seen as part of a broader risk management approach, and its relevance for commercial farmers linked to value chains can be quite different from that for more subsistence-oriented smallholders. Commercial farmers generally have the most options for managing risk and may benefit most from specific types of indemnity or index-based products to protect specific agricultural investments and there are many examples of insurers meeting this need on an affordable and unsubsidized basis. On the other hand, subsistence-oriented farmers, especially poor and vulnerable ones, need insurance that can help protect their household income and consumption from negative shocks. This kind of insurance is expensive and difficult to supply without subsidies and requires strong public sector support. Even if targeted in this way, private unsubsidized insurance will only thrive given a supporting policy environment and, to keep costs down and improve the relevance and delivery of its products, insurers need to take full advantage of new and emerging digital and remote sensing innovations, and where possible, partner with intermediaries who can bundle their insurance with credit, farm inputs and other services. |
| Keywords: | agricultural insurance; case studies; farmers; literature review; private sector |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ifprid:2316 |
| By: | Thibaut Duprey; Victoria Fernandes; Kerem Tuzcuoglu; Ruhani Walia |
| Abstract: | We introduce a history of macroprudential policy (MPP) events in Canada since the 1980s. We document the short-run effects of MPP announcements on market-based measures of systemic risk and find that MPPs can influence the market’s perception of large banks’ resilience. |
| Keywords: | Financial system regulation and policies; Financial stability; Financial institutions; Econometric and statistical methods |
| JEL: | E58 G21 G28 G32 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocsan:25-4 |
| By: | Federico Huneeus; Joseph P. Kaboski; Mauricio Larrain; Sergio L. Schmukler; Mario Vera |
| 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. |
| Keywords: | banking, credit demand, credit supply, crises, Covid-19, debt, employment protection, firm risk, macroeconomic risk, public credit guarantees |
| JEL: | G21 G28 G32 G33 G38 I18 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11652 |
| By: | Tibor Szendrei |
| Abstract: | Dynamic quantiles, or Conditional Autoregressive Value at Risk (CAViaR) models, have been extensively studied at the individual level. However, efforts to estimate multiple dynamic quantiles jointly have been limited. Existing approaches either sequentially estimate fitted quantiles or impose restrictive assumptions on the data generating process. This paper fills this gap by proposing an objective function for the joint estimation of all quantiles, introducing a crossing penalty to guide the process. Monte Carlo experiments and an empirical application on the FTSE100 validate the effectiveness of the method, offering a flexible and robust approach to modelling multiple dynamic quantiles in time-series data. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.10564 |