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on Risk Management |
| By: | Wenbin Wu; Can Liu |
| Abstract: | This paper investigates systemic risk transmission across stablecoin markets using Quantile Vector Autoregression (QVAR). Analyzing eight major stablecoins with day data coverage from 2021 to 2025, supplemented by minute-level event studies on three additional coins experiencing major depegs until 2025, we document three findings. First, stabilization mechanism dictates tail-risk behavior: fiat-backed stablecoins function as "stability anchors" with near-zero net spillovers across quantiles, while algorithmic and crypto-collateralized designs become risk amplifiers specifically under extreme market conditions. Second, the theoretical risk isolation between fiat and crypto markets breaks down during stress: direct volatility channels emerge between the US Dollar Index and Bitcoin that bypass stablecoin intermediation. Third, Forbes-Rigobon contagion tests across four depeg events show heterogeneous transmission: after adjusting for volatility, algorithmic stablecoins exhibit significant residual contagion while fiat-backed coins show flight-to-quality effects. These findings imply that uniform stablecoin regulation is inappropriate; regulatory capital buffers for extreme losses should be 2--3x higher for non-fiat-backed stablecoins than median-based measures indicate. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.18820 |
| By: | Ms. Laura Valderrama; Mr. Richard Varghese |
| Abstract: | This paper applies network analysis to examine the impact of non-bank financial institutions (NBFIs) and financial market stress on contagion risk within the interbank network. Using network-based simulations on euro area banks’ supervisory data, we find that banks’ strong capital and liquidity buffers significantly reduce contagion through interbank exposures: base-line scenarios show only modest capital losses and no cascading defaults. In contrast, stress originating from NBFIs under heightened market volatility markedly amplifies systemic risk. These findings highlight NBFIs and market volatility as key amplifiers of financial stress in the euro area. Our findings call for integrating contagion models into system-wide stress testing and designing macroprudential policies that encompass the entire financial ecosystem. Such policies should account for amplification risks from banks’ NBFI exposures when calibrating buffers and identifying systemic institutions. |
| Keywords: | Systemic Risk; Network Analysis; Interconnectedness; NBFIs; Market Risk |
| Date: | 2026–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/033 |
| By: | Anna Amirdjanova; David Lynch; Anni Zheng |
| Abstract: | We examine prospective classification of crypto currencies risks within the ISDA Standardized Initial Margin Model (SIMM) framework for calculation of initial margin on trades sensitive to cryptocurrencies’ risk factors in the uncleared market. Consistent with the view that cryptocurrencies are digital assets that fundamentally rely on distributed ledger technology (DLT) and induce financial risks that are significantly different from those in traditional risk classes like commodities or FX, we find that cryptocurrencies are best classified into a distinct risk class within SIMM that is split into two buckets – pegged and floating (unpegged) crypto currencies as risk factors - and suggest risk weights’ calibration methodology within the cryptocurrencies risk class that is consistent with the existing approaches adopted in SIMM. |
| Keywords: | Risk management; Cryptocurrencies; Credit risk; Derivatives |
| JEL: | G12 G13 G18 G28 |
| Date: | 2026–02–12 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:102799 |
| By: | Shikoli, Alaga Celestine; Omido, Karim Hassanali; Chepkulei, Bella |
| Abstract: | This study examined the effect of credit risk management practices on the financial performance of registered Deposit-Taking SACCOs in the Coastal Region of Kenya. The objectives of the study were to determine the effects of credit appraisal methods, credit risk identification, credit risk mitigation practices, and credit risk monitoring on SACCO financial performance. The study focused on SACCOs operating in the Coastal Region, including those headquartered elsewhere but with branches in the region. The theoretical framework was guided by Asymmetric Information Theory, Transaction Costs Theory, the 5 C’s Model for Client Assessment, and Credit Liquidity Theory. A non-experimental correlational research design was adopted, targeting 30 participants from 10 SACCOs using a census sampling approach. Primary data were collected through structured questionnaires, with validity and reliability ensured through expert review and a pilot study. Data were analyzed using SPSS, and inferential statistics were performed using a multiple linear regression model. The findings revealed that credit risk identification (ρ < 0.001, β= 0.312) and credit risk mitigation practices (ρ < 0.001, β = 0.511) had a statistically significant positive effect on financial performance, while client appraisal methods (ρ = 0.084, β = 0.142) and credit risk monitoring (ρ = 0.221, β = 0.119) were not statistically significant. Based on these results, the study recommends that SACCOs strengthen credit risk identification and mitigation strategies, adopt effective client appraisal methods, and enhance monitoring practices to the extent feasible. The study contributes valuable insights for SACCO managers, policymakers, investors, and researchers seeking to improve financial performance through effective credit risk management. |
| Date: | 2026–02–17 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:ankjx_v1 |
| By: | Fabrizio Cipollini; Giulia Cruciani; Giampiero M. Gallo; Alessandra Insana; Edoardo Otranto; Fabio Spagnolo |
| Abstract: | VOLARE (VOLatility Archive for Realized Estimates - https://volare.unime.it) is an open research infrastructure providing standardized realized volatility and covariance measures constructed from ultra-high-frequency financial data. The platform processes tick-level observations across equities, exchange rates, and futures using an asset-specific pipeline that addresses heterogeneous trading calendars, microstructure noise, and timestamp precision. For equities, price series are cleaned using a documented outlier detection procedure and sampled at regular intervals. VOLARE delivers a comprehensive set of realized estimators, including realized variance, range-based measures, bipower variation, semivariances, realized quarticity, realized kernels, and multivariate covariance measures, ensuring methodological consistency and cross-asset comparability. In addition to bulk dataset download, the platform supports interactive visualization and real-time estimation of established volatility models such as HAR and MEM specifications. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.19732 |
| By: | Tibor Szendrei; Nikolett V\'ag\'o; Katalin Varga |
| Abstract: | This paper develops a House Price-at-Risk framework to examine how housing subsidies, credit conditions, and supply factors influence the distribution of house price growth in Hungary. Using quantile regression with adaptive LASSO variable selection, we identify variables driving downside versus upside risks across multiple horizons. Financial stress dominates the lower tail at short horizons, while unemployment and affordability constraints become the primary drivers of downside risk at longer horizons. Housing subsidies exhibit pro-cyclical characteristics, concentrating significant positive effects on the upper quantiles while leaving the lower tail largely unaffected. Supply-side variables display horizon-dependent sign reversals, with construction permits exerting upward pressure on prices in the short run but moderating them as supply materialises. Uncertainty decomposition reveals persistent left-tail dominance across all horizons. These findings suggest that macroprudential frameworks should account for the distributional effects of housing subsidies, particularly their pro-cyclical influence on house price growth. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.18592 |
| By: | Wenxi Geng; Dingyuan Liu; Liya Li; Yiqing Wang |
| Abstract: | Post-hoc explainability is central to credit risk model governance, yet widely used tools such as coefficient-based attributions and SHapley Additive exPlanations (SHAP) often produce numerical outputs that are difficult to communicate to non-technical stakeholders. This paper investigates whether large language models (LLMs) can serve as post-hoc explainability tools for credit risk predictions through in-context learning, focusing on two roles: translators and autonomous explainers. Using a personal lending dataset from LendingClub, we evaluate three commercial LLMs, including GPT-4-turbo, Claude Sonnet 4, and Gemini-2.0-Flash. Results provide strong evidence for the translator role. In contrast, autonomous explanations show low alignment with model-based attributions. Few-shot prompting improves feature overlap for logistic regression but does not consistently benefit XGBoost, suggesting that LLMs have limited capacity to recover non-linear, interaction-driven reasoning from prompt cues alone. Our findings position LLMs as effective narrative interfaces grounded in auditable model attributions, rather than as substitutes for post-hoc explainers in credit risk model governance. Practitioners should leverage LLMs to bridge the communication gap between complex model outputs and regulatory or business stakeholders, while preserving the rigor and traceability required by credit risk governance frameworks. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.18895 |
| By: | Steven D. Baker; Michael Junho Lee |
| Abstract: | We propose a quantitative framework to track systemic risk arising from cyber vulnerabilities of the U.S. financial system. Synthesizing financial, economic, cyber, and network data that covers thousands of financial institutions and technological firms, we develop an index that tracks financial-system-level cyber vulnerability (SCV) for the financial system. Geopolitical risk, ransomware and malware incidents, and seasonal factors significantly drive the estimated adversarial component. Estimated technological and financial components exhibit fat tails in the distribution. In the cross-section, SCV is attributable to a small set of the largest firms. Large technology firms, including Microsoft, Google, Cisco, and Apple, emerge as key contributors to SCV. These providers also represent the largest cumulative count of vulnerabilities, pointing to financial stability considerations arising from the common exposure to client firms. SCV for service providers co-varies with that of financial institutions, which could amplify financial stability risks. The framework puts forth an approach to include a broad set of entities into an aggregate assessment of cyber vulnerability. |
| Keywords: | financial system architecture; index; cyber risk; systemic risk; financial stability |
| JEL: | G21 G23 G28 G29 O33 |
| Date: | 2026–02–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:102831 |
| By: | Ashima Goyal (Indira Gandhi Institute of Development Research); Sritama Ray (Indira Gandhi Institute of Development Research) |
| Abstract: | Reducing borrowing costs for emerging markets (EMs) is a challenge. The additional country risk premia that foreign investors seek are primarily driven by a fear of unexpected currency depreciation; which often does not take place. It follows there are positive excess returns from EM assets. We find while the interest rate differential (IRD) is near-zero for advanced economies, it is always positive for EMs. Excess exchange rate volatility is often due to global and not domestic factors, so that a pure float aggravates instead of mitigating shocks. Lower exchange rate volatility, risk and risk-perceptions can reduce EM IRDs. A suitable exchange rate regime and domestic as well as international prudential regulation on cross-border capital flows can lower volatility. Different phases of India's flexible float illustrate these issues well. |
| Keywords: | Exchange rate volatility, emerging markets, advanced economies, interest rate differentials, excess returns, global shocks, policies |
| JEL: | F31 F41 E65 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:ind:igiwpp:2025-026 |
| By: | Pavel Koptev; Vishnu Kumar; Konstantin Malkov; George Shapiro; Yury Vikhanov |
| Abstract: | Invoice or payment dilution is the gap between the approved invoice amount and the actual collection is a significant source of non credit risk and margin loss in supply chain finance. Traditionally, this risk is managed through the buyer's irrevocable payment undertaking (IPU), which commits to full payment without deductions. However, IPUs can hinder supply chain finance adoption, particularly among sub-invested grade buyers. A newer, data-driven methods use real-time dynamic credit limits, projecting dilution for each buyer-supplier pair in real-time. This paper introduces an AI, machine learning framework and evaluates how that can supplement a deterministic algorithm to predict invoice dilution using extensive production dataset across nine key transaction fields. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.15248 |
| By: | Willem D. Schutte; Charl Pretorius; Neill Smit; Leandra van der Merwe; Robert Maxwell |
| Abstract: | In this paper, we study how class imbalance, typical of low-default credit portfolios, affects the performance of logistic regression models. Using a simulation study with controlled data-generating mechanisms, we vary (i) the level of class imbalance and (ii) the strength of association between the predictors and the response. The results show that, for a given strength of association, achievable classification accuracy deteriorates markedly as the event rate decreases, and the optimal classification cut-off shifts with the level of imbalance. In contrast, the Gini coefficient is comparatively stable with respect to class imbalance once sample sizes are sufficiently large, even when classification accuracy is strongly affected. As a practical guideline, we summarise attainable classification performance as a function of the event rate and strength of association between the predictors and the response. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.19663 |
| By: | Tae-Hwy Lee (Department of Economics, University of California Riverside); Tianyan Tu (University of California, Riverside) |
| Abstract: | Motivated by the multi-dimensional nature of financial data, we propose the tensor portfolios, a framework exploiting the intrinsic multi-way structure of stock returns to reduce the number of free parameters required for portfolio construction. We develop three distinct methods tailored to specific structural assumptions. We systematically compare tensor and vector portfolios through Monte Carlo simulations and empirical studies. The simulation results show tensor portfolios yield significantly higher out-of-sample Sharpe ratios whenever the data exhibits a tensor structure. Empirical analysis further corroborates the effectiveness of tensor portfolios; their general outperformance over vector portfolios in read-world markets highlights the practical significance of exploiting multi-way information. |
| Keywords: | Tensor Portfolio Optimization; Kronecker Separability; High-Dimensionality; Tensor Factor Model; Tensor Graphical LASSO |
| JEL: | C13 C40 C55 C58 G11 G17 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202601 |
| By: | Tim J. Boonen; Kenneth Tsz Hin Ng; Tak Wa Ng; Thai Nguyen |
| Abstract: | We propose a peer-to-peer (P2P) insurance scheme comprising a risk-sharing pool and a reinsurer. A plan manager determines how risks are allocated among members and ceded to the reinsurer, while the reinsurer sets the reinsurance loading. Our work focuses on the strategic interaction between the plan manager and the reinsurer, and this focus leads to two game-theoretic contract designs: a Pareto design and a Bowley design, for which we derive closed-form optimal contracts. In the Pareto design, cooperation between the reinsurer and the plan manager leads to multiple Pareto-optimal contracts, which are further refined by introducing the notion of coalitional stability. In contrast, the Bowley design yields a unique optimal contract through a leader-follower framework, and we provide a rigorous verification of the individual rationality constraints via pointwise comparisons of payoff vectors. Comparing the two designs, we prove that the Bowley-optimal contract is never Pareto optimal and typically yields lower total welfare. In our numerical examples, the presence of reinsurance improves welfare, especially with Pareto designs and a less risk-averse reinsurer. We further analyze the impact of the single-loading restriction, which disproportionately favors members with riskier losses. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.14223 |
| By: | Leon Stolle; Jonas Boeschemeier; Benjamin F. Hobbs; Karsten Neuhoff |
| Abstract: | Locational marginal pricing (LMP) provides efficient locational dispatch and investment signals but requires a complementary congestion hedging instrument to function effectively. This paper investigates how exposure to locational price differences is managed in North American nodal electricity markets through the implementation of financial transmission rights (FTRs). Drawing on insights from 15 industry experts directly involved across all major North American electricity markets, we consolidate first-hand perspectives that reveal the practical complexities of FTR design and implementation. While most interviewees view FTRs positively, their experiences uncover multiple nuanced challenges to successfully design locational hedging instruments, which are often overlooked in the academic literature. As FTR design depends on market characteristics, we apply the findings to the European electricity market and discuss implications for a possible implementation of LMP in Europe. |
| Keywords: | Financial transmission rights, locational marginal pricing, nodal pricing, risk hedging, congestion revenue, electricity market design, contracts for differences |
| JEL: | D44 D47 L94 Q40 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2156 |
| By: | Julia Manso |
| Abstract: | Sentiment towards the Chinese real estate sector has deteriorated following the introduction of financing constraints in 2020 with the ''three red lines." Forcing developers to restructure their debt, the policy triggered a cascade of financing troubles, defaults, and reduced housing demand, ultimately culminating in a prolonged real estate crisis. This paper utilizes a network approach in line with Demirer et al. (2018) and Diebold and Yilmaz (2014) to measure daily time-varying connectedness in the stock return volatilities of major Chinese real estate developers throughout the crisis. Focusing on spillover between companies as reflected by market perception, this paper examines how connectedness evolves over time across firms with different regional exposures and state-ownership statuses, filling a gap in the literature to elucidate where property demand and real estate firm trustworthiness have deteriorated most. An event-study analysis of four key moments of the crisis outlines distinct phases of market sentiment: with the introduction of the three red lines, connectedness primarily reflects shared exposure and a uniform shock to the market. Then, the early unrest surrounding Evergrande exposes strong regional differentiation, with firms concentrated in less developed regions receiving significant spillover. By one year into the crisis, previously stable regions receive higher levels of spillover, and there is evidence of a substitution effect towards private developers. Two years into the crisis, the market has much less homogeneity in effects across regions and state-ownership status: major shocks induce minimal network changes, reflecting how investors have already priced in their beliefs. This paper also offers one of the most extensive timelines of the Chinese real estate crisis to date, and a new R package, GephiForR, was created for the network visualization in this paper. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.19740 |
| By: | Tomas Jankauskas |
| Abstract: | Understanding how short- and long-term assets are priced is one of the fundamental questions in finance. The term structure of risk premia allows us to perform net present value calculations, test asset pricing models, and potentially explain the sources of many cross-sectional asset pricing anomalies. In this post, I construct a forward-looking estimate of the term structure of risk premia in the corporate bond market following Jankauskas (2024). The U.S. corporate bond market is an ideal laboratory for studying the relationship between risk premia and maturity because of its large size (standing at roughly $16 trillion as of the end of 2024) and because the maturities are well defined (in contrast to equities). |
| Keywords: | risk premia term structure; corporate bonds |
| JEL: | G10 G12 |
| Date: | 2026–02–24 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:102808 |
| By: | Gavin Harper; Viet Nguyen-Tien |
| Abstract: | Why critical materials are central to global strategic partnerships |
| Keywords: | Green Growth |
| Date: | 2026–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepcnp:727 |
| By: | Anyfantaki, Sofia; Migiakis, Petros; Petroulakis, Filippos; Giannakidis, Haris; Malliaropulos, Dimitris |
| Abstract: | Using granular security-level data from bond funds domiciled in the US and the euro area, we identify a market-based risk-taking channel of monetary policy transmission via the credit-risk and the maturity structure of bond funds’ portfolios. We measure credit risk at the fund level as the weighted average credit rating of the fund’s bond holdings. We find that accommodative monetary policies by the Fed and the ECB are associated with increased risk in bond funds’ portfolios. Interestingly, risk-taking is more pronounced for funds with longer-term holdings relative to short-term ones and unconventional monetary policy exerts stronger market-based risk-taking effects than interest rate policy. Finally, we find that Fed’s monetary policy has a stronger impact on funds’ risk-taking behaviour than the ECB’s, highlighting the dominant role of US monetary policy in global financial markets. JEL Classification: E52, G12, G15, G20 |
| Keywords: | investment funds, monetary policy, non-bank financial intermediation, risk-taking channel |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263196 |
| By: | Mr. Serhan Cevik; Amit Kara |
| Abstract: | Financial intermediation in Montenegro has been on a declining trend since independence, with domestic credit to the private sector decreasing from a peak of 86.5 percent of GDP in 2008 to 46.4 percent in 2024. Net interest margin (NIM)—a common indicator of intermediation costs—has remained elevated, ranking among the highest in the Western Balkans. This paper analyzes the determinants of NIMs using a unique bank-level dataset comprising quarterly observations on all commercial banks in Montenegro during the period 2013–25. The empirical analysis reveals three key findings, each with important policy implications. First, larger banks tend to exhibit lower NIMs, reflecting economies of scale, diversification, and stronger market power. Second, higher asset quality is associated with narrower margins, underscoring the role of effective credit risk management. Third, greater operational efficiency correlates with lower NIMs, highlighting the importance of cost control and managerial effectiveness. Taken together, these results underscore the need for policy initiatives that support banking sector consolidation, reinforce credit risk management practices, and promote operational efficiency improvements. |
| Keywords: | Banks; financial intermediation; credit; net interest margin |
| Date: | 2026–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/029 |