Paper constructs a CRRIX index to assess cryptocurrency market risks from regulatory changes.
problem Lack of indices quantifying regulatory risks in cryptocurrencies.
method CRRIX index based on news coverage frequency, using Latent Dirichlet Allocation and Hellinger distance.
result CRRIX successfully captures major policy-changing moments and synchronizes with market volatility.
Regshock visualizes financial risks to help regulators manage systemic shocks.
problem Managing systemic risks in financial networks.
method Risk-island visualization algorithm and regshock visual exploration approach.
result Demonstrated improved risk management and control capabilities.
Optimizes insurance profits under regulatory constraints.
problem Maximizing profits while adhering to regulatory and risk policies.
method Developed a formalism for in-force business profit optimisation.
result Identified optimal asset allocation and annual opportunity cost.
Study examines how business units can benefit from group cohesion under regulatory constraints.
problem Regulatory constraints limit business units' ability to form a single cohesive group.
method Defined and analyzed cohesive risk measures to minimize capital costs.
result Cohesive risk measures allow groups to achieve minimal capital costs without altering individual liabilities.
Proposes a new method for determining LGD discount rates based on cost of capital.
problem Determining an appropriate discount rate for LGD estimation.
method Market-consistent pricing of defaulted loan portfolios to infer discount rates.
result Discount rates reflect both undiversifiable risk and time value of money.
New risk measure improves creditor protection in financial regulation.
problem Current solvency requirements fail to control the size of recovery on creditors' claims.
method Developed Recovery Value at Risk (Recovery VaR) to control recovery on creditors' claims.
result Recovery VaR flexibly controls recovery on creditors' claims and integrates protection needs into management incentives.
This paper examines if CTE risk measure aligns with profit-maximizing risk capital allocations.
problem Whether CTE risk measure aligns with profit-maximizing risk capital allocations.
method Exhaustive probabilistic model settings analysis.
result CTE risk measure may align with profit-maximizing risk capital allocations under certain conditions.
Regulations impose idiosyncratic capital and funding costs for holding derivatives. Capital requirements are costly because derivatives desks are risky businesses; funding is costly in part because regulations increase the minimum funding tenor. Idiosyncratic costs mean no single measure makes derivatives martingales f…
SHARC explains machine learning risk models for regulatory capital, linking outputs to scenarios.
problem Inability to explain machine learning model outputs to regulatory bodies.
method SHAP-based explainability framework for Hybrid GPR-HS architecture and SVaR stress-testing.
result SHARC links SVaR outputs to scenario inputs, providing auditable traceability.
This paper improves operational risk modeling by selecting better loss severity distributions.
problem Inconsistent regulatory capital calculations due to changing loss severity distribution families.
method Presented truncation probability estimates and a consistent quantile scoring function for selection criteria. Also, recommended collecting loss frequencies below the minimum reporting threshold.
result More stable regulatory capital calculations through better selection of loss severity distributions.
Paper introduces RiskEmbed, a finetuned model for financial risk management.
problem Improving retrieval accuracy in financial question-answering systems.
method Curated dataset and finetuned BERT model for financial domain.
result RiskEmbed significantly outperforms general-purpose and financial embedding models.
This research develops a new framework to measure AI investment returns considering both gains and risks.
problem Traditional ROI calculations fail to account for AI's dual impact on risk reduction and new exposures.
method Integrates ISO 42001 and regulatory exposure into a comprehensive financial framework using risk quantification methods.
result Accurate AI investment evaluation requires modeling both productivity gains and risk exposures.
Funding is a cost to trading desks that they see as an input. Current FVA-related literature reflects this by also taking funding costs as an input, usually constant, and always risk-neutral. However, this funding curve is the output from a Treasury point of view. Treasury must consider Regulatory-required liquidity bu…
Research creates a taxonomy to bridge AI security and regulatory gaps.
problem Disciplinary disconnect between technical and legal teams in AI risk assessment.
method Developed an AI System Threat Vector Taxonomy with 9 domains and 53 sub-threats.
result Empirically validated and aligned with ISO/IEC 42001 controls and NIST AI RMF functions.
Over the last 23 years, the U.S. Securities and Exchange Commission has required over 34,000 companies to file over 165,000 annual reports. These reports, the so-called "Form 10-Ks," contain a characterization of a company's financial performance and its risks, including the regulatory environment in which a company op…
Introduces factor risk measures to assess risk relative to multiple factors.
problem Measuring risk relative to multiple factors.
method Introduces a double-argument mapping as a risk measure to assess risk relative to a vector of factors.
result Characterizes various types of factor risk measures including distortion, quantile, linear, and coherent measures.
Cryptocurrency markets show similar returns but different volatility responses to infrastructure and regulatory shocks.
problem Understanding how cryptocurrency markets differentiate between infrastructure and regulatory shocks.
method Event-level block bootstrap inference on 31 cryptocurrency events across Bitcoin, Ethereum, Solana, and Cardano (2019-2025).
result No statistically significant difference in cumulative abnormal returns between infrastructure failures and regulatory enforcement.
New method forecasts systemic risk with improved precision.
problem Improving the estimation of systemic risk measures.
method De-volatilizing observations and using extreme value theory for forecasting.
result Valid MES forecasts with good coverage in simulations and empirical applications.
This letter assesses model risk in credit capital requirements and finds substantial tail risk.
problem Uncertainty in the probability of default and loss-given-default parameters in credit capital requirements.
method Models estimation risk in a simple way, analyzing two datasets and testing parameter dependency.
result Parameter dependency significantly increases tail risk in capital requirements, requiring substantial increases in regulatory capital.
Study evaluates SHAP for credit card default model consistency.
problem Model transparency and fairness in credit card default prediction models.
method Evaluates SHAP stability in credit card default prediction models via a case study.
result SHAP consistency is related to variable importance level.
The problem of estimation error of Expected Shortfall is analyzed, with a view of its introduction as a global regulatory risk measure.
We define scenarios, propose different methods of aggregating them, discuss their properties and benchmark them against quadrant requirements.
Survey examines agentic AI in finance, highlighting its autonomy and challenges.
problem Autonomous AI systems in finance and their implications.
method Systematic review of research, technical architectures, market applications, and governance frameworks.
result Agentic AI offers enhanced market efficiency but introduces new risks.
Study shows how capital constraints can lead to systemic crises in financial systems.
problem Impact of regulatory capital constraints on fire sales and financial stability.
method Mean field game model with banks adjusting holdings via trading strategies under regulatory constraints.
result Capital constraints can lead to simultaneous defaults in a substantial proportion of the banking system.
The paper extends utility maximization by integrating partial information and robust VaR constraints.
problem Optimal investment under partial information and robust VaR-type constraints.
method Combines partial information and robust regulatory constraints (VaR) to solve the utility maximization problem.
result Optimal wealth is a decreasing function of state price density, and depends on the overall evolution of the estimated market price of risk.
The management of operational risk in the banking industry has undergone significant changes over the last decade due to substantial changes in operational risk environment. Globalization, deregulation, the use of complex financial products and changes in information technology have resulted in exposure to new risks ve…
Robust machine learning models improve DNA regulatory sequence prediction under various shifts.
problem Real-world applications of DNA regulatory sequence prediction involve shifts not captured by standard i.i.d. assumptions.
method Introduces a robustness framework combining simulation benchmarks and real data analysis.
result Models remain accurate and calibrated under mild shifts but show higher error and miscalibration under strong shifts.
RegTech improves compliance and risk management through tech solutions.
problem Increasing regulatory costs and reliance on tech for crisis management.
method Examining RegTech solutions and their benefits.
result RegTech will be a promising market due to rising compliance costs and tech reliance.
Do we know if a short selling ban or a Tobin Tax result in more stable asset prices? Or do they in fact make things worse? Just like medicine regulatory measures in financial markets aim at improving an already complex system. And just like medicine these interventions can cause side effects which are even harder to as…
Conditional forecasts of risk measures play an important role in internal risk management of financial institutions as well as in regulatory capital calculations. In order to assess forecasting performance of a risk measurement procedure, risk measure forecasts are compared to the realized financial losses over a perio…
ASRI index detects crypto market risks with high precision and lead time.
problem Detecting systemic risks in cryptocurrency markets.
method Four weighted sub-indices (Stablecoin, DeFi, Contagion, Regulatory) validated against historical crises.
result ASRI detects significant abnormal signals with high statistical significance and lead time.
InfoSEM infers gene regulatory networks without GT labels, improving performance.
problem Inferring GRNs from gene expression data with high accuracy and avoiding biases.
method InfoSEM uses deep generative models with informative priors (textual gene embeddings).
result InfoSEM outperforms existing models by 38.5% across four datasets.
Tests assess if predictions are prudent by comparing observations and predictions.
problem Assessing the prudence of predictions in samples of observations and predictions.
method Bootstrap and normal approximation algorithms for testing unweighted and weighted means, accounting for randomness.
result Tests reveal whether predictions are prudent by showing significantly negative mean differences.
Study examines cryptocurrency risk spillover effects before and after pandemic.
problem Analyzing risk propagation among cryptocurrencies during extreme events.
method Asymmetric breakpoint approach and network analysis.
result Cryptocurrency risk spillover effect increased during pandemic.
Paper proposes a new GPR-HS framework for accurate VCV estimation in global equity indices.
problem Accurate forecasting of Volatility-Covariance Matrix (VCV) for regulatory processes.
method Hybrid Gaussian Process Regression-Historical Simulation (GPR-HS) framework.
result GPR-HS framework achieves regulatory compliance and outperforms static VaR benchmarks.
Digital currencies and cryptocurrencies have hesitantly started to penetrate the investors, and the next step will be the regulatory risk management framework. We examine the Value-at-Risk and Expected Shortfall properties for the major digital currencies, Bitcoin, Ethereum, Litecoin, and Ripple. The methodology used i…
GAN improves financial risk prediction by generating synthetic minority events.
problem Data imbalance in financial market supervision.
method Generative Adversarial Networks (GAN) to generate synthetic data.
result GAN-generated synthetic data significantly improves prediction accuracy.
Model explains money creation under regulatory constraints.
problem Understanding money creation dynamics under regulatory pressures.
method Agent-based model of secured interbank network.
result Excess liquidity and repurchase agreements emerge due to regulatory constraints.
New SigSwap model for path-dependent financial risk.
problem Managing complex, path-dependent financial risks.
method Geometry-based approach using path-signature and Signature Expected Shortfall.
result Path-dependent risks can be converted into transparent risk factors.
New framework makes ML methods compliant with regulations.
problem Ensuring ML methods meet regulatory standards.
method InfoGram and Admissible Machine Learning framework.
result Redesigns ML methods for regulatory compliance.
This thesis examines the accuracy of scaling VaR estimates for longer holding periods.
problem The accuracy of VaR estimates for longer holding periods using the square root of time rule.
method Examined VaR scaling for longer holding periods using empirical analysis.
result Scaling can provide good estimates of VaR but may lead to significant losses over time.
The quantification of diversification benefits due to risk aggregation plays a prominent role in the (regulatory) capital management of large firms within the financial industry. However, the complexity of today's risk landscape makes a quantifiable reduction of risk concentration a challenging task. In the present pap…
Simple method calculates WWR for regulatory and accounting purposes.
problem Estimating WWR for regulatory and accounting capital requirements.
method Model-independent approach using integral expressions and component calibration.
result WWR effects for FVA are significantly more material than for CVA.
The Basel II internal ratings-based (IRB) approach to capital adequacy for credit risk plays an important role in protecting the Australian banking sector against insolvency. We outline the mathematical foundations of regulatory capital for credit risk, and extend the model specification of the IRB approach to a more g…
The dependency structure of credit risk parameters is a key driver for capital consumption and receives regulatory and scientific attention. The impact of parameter imperfections on the quality of expected loss (EL) in the sense of a fair, unbiased estimate of risk expenses however is barely covered. So far there are n…
Regulation and risk management in banks depend on underlying risk measures. In general this is the only purpose that is seen for risk measures. In this paper we suggest that the reporting of risk measures can be used to determine the loss distribution function for a financial entity. We demonstrate that a lack of suffi…
Model predicts insolvency risks in banks due to liquidity and credit risks.
problem Determining insolvency regions in banks due to non-linear interaction between liquidity and credit risks.
method Developed a continuous-time structural dynamic model integrating Basel III requirements into a stochastic optimal control framework. Used Hamilton-Jacobi-Bellman (HJB) equation to solve for insolvency boundary. Derived surrogate analytical approximation for real-time monitoring.
result Calibrated model reveals significant non-linear threshold effects and accelerates insolvency transition.
Within the Own Risk and Solvency Assessment framework, the Solvency II directive introduces the need for insurance undertakings to have efficient tools enabling the companies to assess the continuous compliance with regulatory solvency requirements. Because of the great operational complexity resulting from each comple…