This paper describes the current taxonomy of model risk, ways for its mitigation and management and the importance of the model validation function in collaboration with other departments to design and implement them.
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48 results for “risk mitigation”
Paper discusses how financial institutions' model risk management can benefit academic research.
problem Improving academic research process and mitigating limitations.
method Adopting financial institutions' model risk management practices.
result Lessons from financial institutions can enhance academic research reliability.
Decision-Making Frameworks for Network Resilience -- Managing and Mitigating Systemic (Cyber) Riskq-fin.RM
Framework for managing cyber risks in networks.
problem Managing systemic cyber risks in digital networks.
method Three components: acceptable configurations, risk mitigation interventions, and cost function.
result Effective decision-making for network resilience.
Risk Advisor predicts and mitigates ML deployment failures.
problem Predicting and mitigating test-time failure risks of ML systems.
method Post-hoc meta-learner for estimating failure risks and uncertainties.
result Reliably predicts deployment-time failure risks across various ML models.
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.
Excessive leverage, i.e. the abuse of debt financing, is considered one of the primary factors in the default of financial institutions. Systemic risk results from correlations between individual default probabilities that cannot be considered independent. Based on the structural framework by Merton (1974), we discuss …
Survey finds many adversarial machine learning threats are not critical for most entities.
problem Adversarial machine learning threats and their impact on model accuracy.
method Literature review and analysis of real-world occurrences of adversarial attacks.
result Many adversarial machine learning threats do not warrant the cost of robust models.
Framework mitigates risk non-monotonicity in high-dimensional predictions.
problem Risk non-monotonicity in high-dimensional predictions.
method Model-agnostic framework using cross-validation and data-driven methodologies (zero- and one-step).
result Modified prediction procedures achieve monotonic asymptotic risk behavior.
Actuaries tackle loss of earning capacity in Denmark, balancing public benefits and private insurance.
problem Balancing public benefits and private insurance for loss of earning capacity in Denmark.
method Innovative approaches from researchers and practitioners, leveraging actuarial expertise.
result Development of equitable, data-driven solutions to mitigate risk and enhance societal well-being.
Proposes a network-based strategy to manage financial market risks.
problem Managing extreme events in volatile financial markets.
method Extreme value theory, network model, maximum independent set, value at risk, expected shortfall.
result Developed portfolio strategies improve risk diversification.
Paper assesses risks of stablecoins, from lending to business-to-business.
problem Credit risks in decentralized stablecoin issuance.
method Examines mechanisms, risks, and mitigation strategies at each layer.
result Potential for scaling stablecoins while maintaining systemic health.
The recently proposed unlabeled-unlabeled (UU) classification method allows us to train a binary classifier only from two unlabeled datasets with different class priors. Since this method is based on the empirical risk minimization, it works as if it is a supervised classification method, compatible with any model and …
Study develops hybrid model to mitigate stablecoin liquidity risk.
problem Increasing integration of stablecoins introduces liquidity risk during market stress.
method Hybrid monetary architecture with 100% reserve backing and liquidity facilities.
result Demonstrates significant reduction in peg deviations and stress persistence.
Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evaluation system, called MPLens, with three unique contributions. First, through MPLens, we demonstrate how membership inference attack methods …
Mitigates spurious correlations without bias labels.
problem Spurious correlations bias model performance.
method Introduces a novel training objective and debiasing method DPR.
result DPR achieves state-of-the-art performance.
Statistical depth metrics help identify risky power grid scenarios.
problem Identifying extreme scenarios for risk mitigation in power grid planning.
method Functional depth metrics for sub-selecting outlying scenarios.
result The proposed approach effectively identifies risky scenarios for operational risk mitigation.
It had been believed in the conventional practice that the risk of a bank going bankrupt is lessened in a straightforward manner by transferring the risk of loan defaults. But the failure of American International Group in 2008 posed a more complex aspect of financial contagion. This study presents an extension of the …
New risk measures for financial networks avoid external capital, reducing systemic risk.
problem Systemic risk in financial networks is underestimated by traditional methods.
method Developed set-valued, intrinsic risk measures for financial networks.
result Systemic intrinsic risk measures are more stable and avoid reliance on external capital.
The abstract reviews financial concepts using physics.
problem Financial pricing and risk management.
method Discrete time formalism, path integral, Green's function formulas.
result Formulas for pricing and risk mitigation methods.
Paper addresses theoretical risks in neural MCCFR, proposing Robust Deep MCCFR for improved performance.
problem Theoretical risks in neural MCCFR, especially in large games.
method Adaptive framework with selective component deployment, including target networks, exploration, and variance-aware training.
result Robust Deep MCCFR achieves significant exploitability improvements in both Kuhn and Leduc Poker.
Paper introduces LR to generate synthetic data with privacy protection.
problem Privacy concerns limit the use of sensitive datasets.
method Local Resampler (LR) using k-nearest neighbors algorithm.
result LR effectively mitigates outlier-driven disclosure risks.
Financial market created for wellbeing indices to mitigate socioeconomic risks.
problem Risk mitigation in financial indices of socioeconomic wellbeing.
method Developed new quantitative measure, created financial market, and implemented insurance instruments.
result Optimal portfolio weights and efficient frontiers for wellbeing indices.
Star-shaped Risk Measuresecon.TH
Paper characterizes star-shaped risk measures and their properties.
problem Characterizing risk measures in the presence of liquidity risk and competitive delegation.
method Characterization of star-shaped risk measures, study of their properties.
result Star-shaped risk measures include all practically used risk measures.
Study characterizes and mitigates imbalances in neurosymbolic learning.
problem Characterizing and mitigating class-specific risks in neural classifiers.
method Theoretical analysis and practical techniques including estimating marginal gold labels and mitigating imbalances at training and testing time.
result Learning imbalances can be greatly impacted by the symbolic component σ, unlike in supervised and weakly supervised learning.
Investigates optimal PPI strategies in jump-diffusion models to mitigate downside risk.
problem Gap risk in PPI strategies due to jumps in asset price dynamics.
method Optimization problem with S-shaped utility functions, solved via martingale approach in a jump-diffusion framework.
result Determines optimal PPI strategy to maximize expected utility of terminal wealth.
Black-box model risk in financeq-fin.CP
Machine learning risks in finance pricing and hedging
problem Understanding and managing risks in financial models
method Analyzing machine learning applications in finance, focusing on pricing and hedging of financial options
result Identifies various sources of risk and potential mitigation strategies
Under the Basel II standards, the Operational Risk (OpRisk) advanced measurement approach allows a provision for reduction of capital as a result of insurance mitigation of up to 20%. This paper studies the behaviour of different insurance policies in the context of capital reduction for a range of possible extreme los…
A counterparty credit limit (CCL) is a limit that is imposed by a financial institution to cap its maximum possible exposure to a specified counterparty. CCLs help institutions to mitigate counterparty credit risk via selective diversification of their exposures. In this paper, we analyze how CCLs impact the prices tha…
We advocate the use of Agnostic Allocation for the construction of long-only portfolios of stocks. We show that Agnostic Allocation Portfolios (AAPs) are a special member of a family of risk-based portfolios that are able to mitigate certain extreme features (excess concentration, high turnover, strong exposure to low-…
Double descent risk in L2-regularized models explained and mitigated.
problem Risk of overparameterized models in machine learning.
method Analysis of L2-regularized models, two-layer neural networks, and CNNs.
result Double descent risk in L2-regularized models can be explained and mitigated by adjusting regularization strengths.
The insurance industry uses predictions based on customer characteristics, but this can lead to discrimination. We propose using Wasserstein barycenters to mitigate biases.
problem Discrimination in insurance predictions based on sensitive features like gender or race.
method Propose using Wasserstein barycenters instead of simple scaling to mitigate biases in insurance predictions.
result Demonstrates the effectiveness of Wasserstein barycenters in mitigating biases in insurance predictions.
Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applicationscs.LG
The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to provide valuable assistance in clinical decision making. Classical machine learning models usually only provide point-estimates and no uncertainty of predictions. In practice, uncertain predictions should be presented to …
Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradientscs.LG
The paper tackles catastrophic risk in reinforcement learning using extreme value theory.
problem Mitigating catastrophic risk in sequential decision making with limited observations.
method Developed POTPG, a policy gradient algorithm based on extreme value theory.
result POTPG outperforms common benchmarks in numerical experiments.
Trade-offs and synergies in managing coastal flood risk: A case study for New York Cityphysics.ao-ph
Study finds more flood risk strategies can improve outcomes in NYC.
problem Managing future flood risks with complex models.
method Used an intermediate complexity model to analyze flood risk strategies.
result More combinations of risk mitigation strategies expand the solution set and improve outcomes.
This paper offers a mathematical framework to manage inventory risk in FX cash markets.
problem Inventory risk in FX cash markets due to flow uncertainty and volatility.
method Mathematical framework and approximation techniques for scalability.
result Maximizing expected profit while controlling inventory risk.
Swapping debt contracts can mitigate risk in financial networks.
problem Mitigating risk in financial networks through debt swaps.
method Analysis of debt swapping operations in financial networks under various conditions.
result Positive debt swaps can exist in worst-case shock models to minimize losses.
Paper proposes synthetic data generator to study and mitigate bias in machine learning.
problem Bias in machine learning data can lead to unfair outcomes.
method Developed a synthetic data generator to introduce and analyze various types of bias.
result Demonstrated how synthetic data can be used to study and mitigate bias in machine learning models.
Paper tackles safe combinatorial semi-bandits with risk constraints.
problem Safe combinatorial semi-bandits with risk constraints.
method Formulated probably anytime-safe constraint, designed PASCombUCB algorithm.
result PASCombUCB is almost asymptotically optimal in minimizing regret.
Study uses satellite data to predict tailings dam collapse risk.
problem Detecting early signs of tailings dam instability.
method Spectral analysis of satellite InSAR displacement time series data.
result Algorithm detects risk milestones up to 5 months before dam collapse.
This paper explores how decentralized finance mitigates traditional finance's shortcomings.
problem Lack of transparency and moral hazard in centralized finance.
method Analysis of smart contracts and decentralized governance in DeFi.
result DeFi mitigates traditional finance's shortcomings through decentralized governance and smart contracts.
A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network an…
Multi-asset return risk measuresq-fin.MF
Extends return risk measures to multiple assets, proving properties and comparing different risk models.
problem Evaluating risk in financial markets with multiple assets.
method Develops multi-asset return risk measures (MARRMs), analyzes their properties, and compares them with other risk models.
result Proves that a positively homogeneous MARRM is quasi-convex if and only if it is convex, and provides conditions to avoid inconsistent risk evaluations.
Optimizes information acquisition to reduce estimation risk and maximize utility.
problem Estimation risk in investor decision-making.
method Derives closed-form value functions using CARA and CRRA utility functions, employs variational methods to explore optimal acquisition.
result Acquiring information earlier is more valuable in reducing estimation risk and achieving higher utility.
Models to price long term loans in the securities lending business are developed. These longer horizon deals can be viewed as contracts with optionality embedded in them. This insight leads to the usage of established methods from derivatives theory to price such contracts. Numerical simulations are used to demonstrate…
Can Metropolitan Housing Risk be Diversified? A Cautionary Tale from the Recent Boom and Bustq-fin.PM
Geographic diversification is fundamental to risk mitigation among investors and insurers of housing, mortgages, and mortgage-related derivatives. To characterize diversification potential, we provide estimates of integration, spatial correlation, and contagion among US metropolitan housing markets. Results reveal a hi…
Proposes RaT to mitigate bias in student-teacher estimation.
problem Systematic bias in teacher's predictions propagates to student model.
method Uses teacher to estimate residuals in student's predictions.
result RaT method reduces teacher bias effect and achieves optimal rate.
Robust Risk-Aware Option Hedgingq-fin.CP
Study uses RL to hedge financial derivatives, showing robust strategies outperform non-robust ones.
problem Risk mitigation and gain-seeking in hedging path-dependent financial derivatives.
method Robust risk-aware reinforcement learning (RL) with policy gradient approach.
result Robust hedging strategies outperform non-robust ones under varying data generating processes.
Favorit strategy helps farmers mitigate market price fluctuations.
problem Mitigating adverse impact of price fluctuation on farmers.
method Analyzes historical price data to select optimal market timing for crops.
result Developed a strategy to reduce volatility risk for Indian farmers.