The paper studies risk-based prices in financial markets under volatility uncertainty.
problem Risk-based indifference prices in financial markets under volatility uncertainty.
method Asymptotic analysis of risk-based prices in discrete-time financial markets.
result Risk-based prices form a strongly continuous convex monotone semigroup.
The paper addresses sampling bias in risk-based active learning.
problem Sampling bias in active learning leads to poor decision-making performance.
method The paper uses a semi-supervised Gaussian mixture model with an EM algorithm to counteract sampling bias.
result The EM algorithm effectively incorporates pseudo-labels for unlabelled data, reducing sampling bias.
Discriminative classifiers improve decision-making in SHM systems.
problem Lack of descriptive labels for SHM data.
method Risk-based active learning with discriminative classifiers.
result Discriminative classifiers offer improved robustness and reduced inspection costs.
Risk-based active learning improves SHM decision-making.
problem Lack of prior labels for structural health monitoring.
method Risk-based active learning approach to guide data labeling.
result Improves decision-maker's performance in SHM.
Proposes a method to quantify uncertainty in DNN models for discrete inputs.
problem Uncertainty quantification for DNN models with categorical and discrete feature variables.
method Develops a mathematical framework to quantify prediction uncertainty from discrete input noise and model parameters.
result Identifies risk-sensitive cases prone to misclassification due to discrete predictor errors.
In this paper, we consider a risk-based optimal investment problem of an insurer in a regime-switching jump diffusion model with noisy memory. Using the model uncertainty modeling, we formulate the investment problem as a zero-sum, stochastic differential delay game between the insurer and the market, with a convex ris…
Two new approaches improve decision-making in asset monitoring systems.
problem Sampling bias in risk-based active learning leads to poor performance later.
method Semi-supervised learning and discriminative classification models.
result Discriminative classifiers are more robust to sampling bias.
Advocates Agnostic Allocation for long-only portfolios to reduce risk and improve performance.
problem Excess concentration, high turnover, and low-risk factor exposure in classical portfolio construction methods.
method Agnostic Allocation Portfolios (AAPs) that mitigate extreme features of classical methods while achieving similar performance.
result AAPs represent a risk-based portfolio construction framework that can be implemented in various situations.
Risk-only investment strategies have been growing in popularity as traditional in- vestment strategies have fallen short of return targets over the last decade. However, risk-based investors should be aware of four things. First, theoretical considerations and empirical studies show that apparently dictinct risk-based …
The article proposes a new portfolio allocation method using network theory.
problem Portfolio allocation problem by improving network theory tools.
method Enhancing network theory tools to construct risk-based models and using two covariance matrix estimators.
result Network-based portfolios consistently outperform standard portfolios in terms of performance and risk.
New study finds targeting based on treatment effects outperforms risk-based targeting in social interventions.
problem Lack of accurate treatment effect estimates for machine learning-based targeting in social domains.
method Empirical assessment of targeting strategies using data from 5 real-world RCTs in various domains.
result Treatment effect-based targeting outperforms risk-based targeting, even with biased estimates.
Framework assesses treatment effects by risk groups in observational studies.
problem Evaluating treatment effects in observational studies with risk stratification.
method Five-step framework for risk-based assessment of treatment effect heterogeneity.
result Low-risk patients received negligible absolute benefits, while high-risk patients had pronounced effects.
Based on a point of view that solvency and security are first, this paper considers regular-singular stochastic optimal control problem of a large insurance company facing positive transaction cost asked by reinsurer under solvency constraint. The company controls proportional reinsurance and dividend pay-out policy to…
GAICF proposes a framework for governing generative AI in banking.
problem Generative AI's impact on financial decision-making and governance.
method SR 26-2-compatible governance framework for generative AI applications.
result GAICF aligns generative AI practices with SR 26-2 supervisory expectations.
GAICF proposes a framework for managing generative AI risks in banking.
problem Generative AI's impact on financial decision-making and governance.
method SR 26-2-compatible governance framework for generative AI.
result GAICF aligns generative AI practices with SR 26-2 supervisory expectations.
Motivated by the unceasing interest in hidden Markov models (HMMs), this paper re-examines hidden path inference in these models, using primarily a risk-based framework. While the most common maximum a posteriori (MAP), or Viterbi, path estimator and the minimum error, or Posterior Decoder (PD), have long been around, …
We propose a route for the evaluation of risk based on a transformation of the covariance matrix. The approach uses a `potential' or `objective' function. This allows us to rescale data from different assets (or sources) such that each data set then has similar statistical properties in terms of their probability distr…
Assessment of risk levels for existing credit accounts is important to the implementation of bank policies and offering financial products. This paper uses cluster analysis of behaviour of credit card accounts to help assess credit risk level. Account behaviour is modelled parametrically and we then implement the behav…
cCorrGAN approximates conditional correlation matrices using GANs.
problem Learning empirical conditional distributions in the elliptope of correlation matrices.
method Conditional Generative Adversarial Networks (GANs) applied to correlation matrices.
result Validated through Monte Carlo simulations in finance.
Dynamic model considers private asset markets' complexities.
problem Understanding and optimizing private asset allocation.
method State-of-the-art dynamic model with machine learning.
result Optimal investment policies quantified over fund life.
This paper considers optimal control problem of a large insurance company under a fixed insolvency probability. The company controls proportional reinsurance rate, dividend pay-outs and investing process to maximize the expected present value of the dividend pay-outs until the time of bankruptcy. This paper aims at des…
Paper introduces dynamic strategies for multi-period investment models.
problem Optimizing investment strategies over multiple periods with risk and return considerations.
method Developed a Bellman principle for discrete time multi-period mean-variance models, leading to dynamic optimal strategies and efficient frontiers.
result Dynamic optimal strategies can achieve higher returns with lower risk compared to the 1/n strategy.
Model predicts default risk based on company's financial forecasts and credit conditions.
problem Estimating the risk of a company defaulting on its financial obligations.
method Developed an equilibrium model linking interest rates to corporate performance and credit supply.
result Estimates idiosyncratic default risk and provides forward-looking probability of default (PD).
Study uses spectral risk for learning with heavy-tailed data.
problem Learning with heavy-tailed loss distributions.
method Spectral risk with Lipschitz-continuous density, derivative-free learning.
result Excess risk guarantees and improved performance over traditional methods.
New risk class defined based on loss location and deviation.
problem Risk assessment in loss distributions.
method Wrapper around smooth loss functions, M-estimators, stochastic gradient methods.
result Finite-sample stationarity guarantees for stochastic gradient methods.
Dynamic reinsurance aims to minimize surplus risk using martingale transport.
problem Minimizing surplus risk in dynamic reinsurance.
method Martingale optimal transport techniques.
result A tractable solution analogous to the Bass martingale is found.
Paper presents a deep learning method for estimating asset return precision matrices in noisy financial markets.
problem Estimating precision matrices of asset returns in low signal-to-noise ratio environments.
method Non-linear factor model within deep learning framework, consistent estimator with error covariance estimator.
result Superior accuracy in simulations and empirical data.
Risk management is an important practice in the banking industry. In this paper we develop a new methodology to estimate and predict the probability of default (PD) based on the rating transition matrices, which relates the rating transition matrices to the macroeconomic variables. Our method can overcome the shortcomi…
Model predicts cognitive health risks based on smartphone usage patterns.
problem Identifying cognitive health risks through smartphone usage.
method Structured models of smartphone interactions analyzed over 12 weeks.
result AUROC of 0.79 in discriminating between healthy and symptomatic subjects.
We introduce an equilibrium asset pricing model, which we build on the relationship between a novel risk measure, the Expected Downside Risk (EDR) and the expected return. On the one hand, our proposed risk measure uses a nonparametric approach that allows us to get rid of any assumption on the distribution of returns.…
We analyse the importance of international relations between countries on the financial stability. The contagion effect in the network is tested by implementing an epidemiological model, comprising a number of European countries and using bilateral data on foreign claims between them. Banking statistics of consolidated…
New model predicts financial market abnormalities using stock index uncertainties.
problem Forecasting abnormal financial fluctuations in the market.
method Quantitative analysis of mean and volatility uncertainties, constructing early warning indicators.
result Established a new abnormal fluctuations warning model.
Study finds stocks with higher cyber risk scores outperform others, indicating a market-wide cyber risk premium.
problem Identifying and quantifying firms' cyber risks and their impact on stock performance.
method Machine learning algorithm to analyze disclosures and a dedicated cyber corpus.
result High cyber risk stocks significantly outperform others, indicating a market-wide cyber risk premium.
Paper studies convex risk measures linked to optimization.
problem Risk assessment in finance and insurance.
method Investigates a wide class of risk measures on Orlicz spaces.
result Characterizes the dual of risk measures and provides complementary representations.
Recently, there has been a growing interest in network research, especially in these fields of biology, computer science, and sociology. It is natural to address complex financial issues such as the European sovereign debt crisis from the perspective of network. In this article, we construct a network model according t…
We analyze the performance of RiskMetrics, a widely used methodology for measuring market risk. Based on the assumption of normally distributed returns, the RiskMetrics model completely ignores the presence of fat tails in the distribution function, which is an important feature of financial data. Nevertheless, it was …
AI systems need reliable testing to ensure safety and trustworthiness.
problem Current AI Act lacks functional trustworthiness for AI systems.
method Define technical application distribution, set risk-based performance, and conduct statistically valid testing.
result Reliable functional trustworthiness is essential for AI systems.
This study assesses risk concentration in MDB portfolios using Monte Carlo simulations.
problem Risk concentration in MDB portfolios of a few borrowers.
method Realistic MDB portfolio simulations and Monte Carlo analysis.
result Current risk adjustments may be overly conservative.
Paper proposes a new method to assess default risk using CEV process in KMV model.
problem Assessing default risk with constant volatility assumption.
method Apply CEV process to KMV model to estimate firm asset value dynamics and equivalent volatility.
result CEV-KMV model fits market better for credit risk forecasting.
Paper proposes a new method to evaluate AI model interpretability in bond default prediction.
problem Lack of standardized method to assess inherent interpretability of AI models.
method Uses LIME and SHAP to assess feature contributions in bond default prediction.
result Consistent results with intuitive understanding of model interpretability.
While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language description…
Most positive and unlabeled data is subject to selection biases. The labeled examples can, for example, be selected from the positive set because they are easier to obtain or more obviously positive. This paper investigates how learning can be ena BHbled in this setting. We propose and theoretically analyze an empirica…
The paper addresses missing data imputation issues by correcting for distribution shift.
problem Missing data imputation and the resulting distribution shift between observed and full data.
method Formulates imputation as a risk minimization problem and proposes a novel algorithm to correct for distribution shift.
result The proposed algorithm consistently improves imputation accuracy, reducing RMSE and Wasserstein distance by 3% and 7%, respectively.
We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators. New conceptual frameworks are calling for a p…
In this paper we derive robust super- and subhedging dualities for contingent claims that can depend on several underlying assets. In addition to strict super- and subhedging, we also consider relaxed versions which, instead of eliminating the shortfall risk completely, aim to reduce it to an acceptable level. This yie…
A new model forecasts Value-at-Risk using NIG distribution and dynamic scores.
problem Forecasting Value-at-Risk (VaR) in financial markets.
method Proposes a parametric forecasting model based on the normal inverse Gaussian distribution (NIG) incorporating intraday information.
result The model outperforms traditional GARCH models, especially in high-risk scenarios.
Study assesses health plan risk measures for Solvency Capital Requirement.
problem Assessing risk measures for health plans to meet Solvency Capital Requirement.
method Three-part regression model with three GLMs for claim counts, episode allocation, and severity.
result Reduction in regression models compared to traditional methods.
Study benchmarks LLMs in portfolio optimization tasks.
problem Evaluate financial decision-making of LLMs.
method Mathematically explicit portfolio optimization problems with multiple-choice questions.
result Distinct performance patterns among LLMs in different financial tasks.