Type 2 diabetes mellitus (T2DM) is a chronic disease that often results in multiple complications. Risk prediction and profiling of T2DM complications is critical for healthcare professionals to design personalized treatment plans for patients in diabetes care for improved outcomes. In this paper, we study the risk of …
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48 results for “multiple risk factors”
We introduce a class of dependence structures, that we call the Multiple Risk Factor (MRF) dependence structures. On the one hand, the new constructions extend the popular CreditRisk+ approach, and as such they formally describe default risk portfolios exposed to an arbitrary number of fatal risk factors with condition…
Factor risk measuresq-fin.MF
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.
Pricing equity-linked life insurance contracts with multiple risk factors by neural networksq-fin.PR
The paper uses neural networks to price complex life insurance contracts with multiple risk factors.
problem Pricing equity-linked life insurance contracts with various stochastic risk factors.
method Assuming hedging to reduce local variance, the price is expressed as a system of non-linear PDEs. Reformulated as a backward SDE with jumps, solved numerically using neural networks.
result Neural networks provide an efficient numerical solution for pricing these complex contracts.
Novel convex risk measures aggregate multiple uncertain sources for insurance firms.
problem Managing risk from multiple uncertain sources in insurance.
method Proposes convex risk measures based on Fréchet mean.
result Allows for robust risk characterization and closed-form expressions.
Paper proposes C-STM for multimodal neuroimaging data classification.
problem Multimodal neuroimaging data fusion for better classification.
method Coupled Support Tensor Machine (C-STM) using latent factors from ACMTF.
result C-STM achieves better classification performance than single-mode classifiers.
Global fixed income returns span across multiple maturities and economies, that is, they naturally reside on multi-dimensional data structures referred to as tensors. In contrast to standard "flat-view" multivariate models that are agnostic to data structure and only describe linear pairwise relationships, we introduce…
Bayesian hypergraph inference models disease pathways from EHR data.
problem Modeling rare diseases influenced by shared risk factors.
method Bayesian hypergraph inference framework reframing multi-disease modeling.
result Interpretable disease pathways and well-calibrated uncertainty quantification.
The article constructs a forward utility for markets with multiple default risks.
problem Characterizing forward performance processes in a market with multiple default risks.
method Using Jacod-Pham decomposition and recursive BSDEs, the article constructs a forward utility and proves its existence and uniqueness.
result The article identifies the risk-sensitive long-run growth rate of the optimal wealth process in a stochastic factor model with ergodic dynamics.
Generalized Technical Analysis. Effects of transaction volume and riskcond-mat.stat-mech
We generalize the momentum indicator idea taking into account the volume of transactions as a multiplicative factor. We compare returns obtained following strategies based on the classical or the generalized technical analysis, taking into account a sort of risk investor criterion.
Priced risk in corporate bondsq-fin.PR
Study finds it hard to establish common factor pricing in corporate bonds.
problem Difficulty in establishing common factor pricing in corporate bonds.
method Portfolio- and bond-level analyses using multifactor models.
result Common factor pricing in corporate bonds is not significantly explanatory.
Copulas have become an important tool in the modern best practice Enterprise Risk Management, often supplanting other approaches to modelling stochastic dependence. However, choosing the `right' copula is not an easy task, and the temptation to prefer a tractable rather than a meaningful candidate from the encompassing…
Genome-wide association studies (GWAS) have achieved great success in the genetic study of Alzheimer's disease (AD). Collaborative imaging genetics studies across different research institutions show the effectiveness of detecting genetic risk factors. However, the high dimensionality of GWAS data poses significant cha…
We propose a modified time lag random matrix theory in order to study time lag cross-correlations in multiple time series. We apply the method to 48 world indices, one for each of 48 different countries. We find long-range power-law cross-correlations in the absolute values of returns that quantify risk, and find that …
Conformal Risk Controlstat.ME
Extends conformal prediction for controlling expected risk of monotone loss functions.
problem Controlling expected risk of monotone loss functions.
method Generalizes split conformal prediction with coverage guarantee, extending to distribution shift, quantile risk, multiple, adversarial, and expectations of U-statistics.
result Tight up to an factor, with worked examples in computer vision and natural language processing.
Enhances cyber risk assessment with entity-specific features.
problem Lack of high-quality public cyber incident data.
method Develops an InsurTech framework to enrich cyber incident data with entity-specific attributes and implements machine learning models.
result InsurTech features improve prediction robustness and provide customized risk profiles.
The paper calculates MES bounds for systemic risk contributions under uncertain dependence.
problem Measuring systemic risk contributions of financial firms under uncertainty in dependence structure.
method Derives worst-case and best-case bounds for MES under known individual firm risks and partial dependence information.
result Improved MES bounds derived for various types of dependence models.
Genome-wide association studies (GWAS) offer new opportunities to identify genetic risk factors for Alzheimer's disease (AD). Recently, collaborative efforts across different institutions emerged that enhance the power of many existing techniques on individual institution data. However, a major barrier to collaborative…
Profile Graphical Modelsstat.ME
Profile graphical models represent multivariate dependence under varying risk factors.
problem Capturing varying conditional independence structures across different levels of a risk factor.
method Introducing a novel class of graphical models (profile graphical models) that represent multivariate dependence under varying risk factors, and developing a Bayesian approach for learning shared sparsity structures.
result Demonstrated enhanced ability to capture subject-specific differences in protein network data from acute myeloid leukemia.
New risk factors improve stress testing accuracy.
problem Improving stress testing accuracy with new risk factors.
method Adapted PCA and autoencoders for dimension reduction and interpretation.
result Aggregated risk factors enhance stress testing outcomes.
We consider the problem of predicting as well as the best linear combination of d given functions in least squares regression, and variants of this problem including constraints on the parameters of the linear combination. When the input distribution is known, there already exists an algorithm having an expected excess…
Study tests if equity factors explain Bitcoin's risk and returns.
problem Explaining Bitcoin's risk and return with equity factors.
method Applied statistical methods to test Fama-French factors on Bitcoin's excess returns.
result Fama-French factors have explanatory power on Bitcoin's risk and returns.
Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates e…
This study examines the evolving causal structure of equity risk factors.
problem Redundancy and risk contagion in multi-factor strategies during financial crises.
method Causal structure learning methods applied to US equity market data over 29 years.
result Statistically significant sparsifying trend of causal structure during normal times, but densification during financial stress.
Deep learning searches for nonlinear factors for predicting asset returns. Predictability is achieved via multiple layers of composite factors as opposed to additive ones. Viewed in this way, asset pricing studies can be revisited using multi-layer deep learners, such as rectified linear units (ReLU) or long-short-term…
We discuss when and why custom multi-factor risk models are warranted and give source code for computing some risk factors. Pension/mutual funds do not require customization but standardization. However, using standardized risk models in quant trading with much shorter holding horizons is suboptimal: 1) longer horizon …
The Co-Pricing Factor Zooq-fin.PR
A study finds that only a few factors explain corporate bond risk, rendering extensive bond factor literature redundant.
problem The redundancy of extensive bond factor literature in explaining corporate bond risk premia.
method Bayesian Model Averaging Stochastic Discount Factor analysis of 18 quadrillion models.
result A Bayesian Model Averaging SDF explains risk premia better than low-dimensional models, with an out-of-sample Sharpe ratio of 1.5 to 1.8.
Paper presents a machine learning-based method for efficiently pricing and hedging autocallable structured notes with multiple underlying assets.
problem Complex pricing and hedging of autocallable notes with multiple underlying assets.
method Machine learning-based pricing method and Distributional Reinforcement Learning (RL) for hedging.
result Significantly improved efficiency in pricing and hedging, with faster computation and better risk management.
A new approach to risk allocation balances asset and factor risks.
problem Challenges in estimating expected returns for portfolio optimization.
method Risk Budgeting framework that allocates risk at the factor level.
result Effective portfolios can be constructed by balancing asset and factor risks.
This paper develops a method to select a reference contract for multi-contract quoting to minimize execution risk.
problem Minimizing execution risk in multi-contract quoting sequences.
method Develops a diagnostic framework using order-flow Hawkes forecasts and CLF to select a stable reference contract.
result Event-history and LOB-state signals offer complementary views for reference-contract selection.
Study uses generative models to assess credit risk and determine loan sizes in e-commerce supply chain finance.
problem Credit risk assessment and loan size determination for small- and medium-sized sellers in e-commerce supply chain finance.
method Proposes a unified framework using Quantile-Regression-based Generative Metamodeling (QRGMM) integrated with Deep Factorization Machines (DeepFM) to capture complex covariate interactions in e-commerce sales data.
result Validates the model's efficacy for credit risk assessment and loan size determination on synthetic and real-world data.
We give a complete algorithm and source code for constructing general multifactor risk models (for equities) via any combination of style factors, principal components (betas) and/or industry factors. For short horizons we employ the Russian-doll risk model construction to obtain a nonsingular factor covariance matrix.…
This paper improves credit risk analysis by incorporating state-dependent recovery rates into a factor model.
problem Accurate default forecasting in credit risk analysis.
method Extends a one-factor Gaussian copula model to include state-dependent recovery rates and a common factor.
result The proposed model outperforms other models in default prediction, especially during hectic periods.
A new model optimizes portfolios by learning stock return distributions conditioned on factors.
problem Optimizing portfolios with high-dimensional asset-specific factors.
method Conditional Diffusion Transformer architecture linking each asset's return to its factor vector.
result The model outperforms benchmarks in mean-variance and mean-CVaR optimization.
A Data-Driven Probabilistic Framework for Cascading Urban Risk Analysis Using Bayesian Networkscs.LG
Bayesian network framework assesses urban risks across multiple domains.
problem Complex interdependencies in urban systems.
method Bayesian Belief Networks (BBNs) with DAGs, Hill-Climbing search, BIC, K2 scoring, synthetic data, SMOTE.
result Identifies key risk factors and quantifies likelihood of cascading failures.
A new portfolio method uses NMF for risk budgeting, outperforming classical methods.
problem Portfolio diversification and risk management in crypto and traditional assets.
method Risk factor budgeting using convex Non-negative Matrix Factorization (NMF).
result Our method outperforms classical portfolio allocations in diversification and risk profile.
Deep learning improves covariance matrix estimation for better portfolio risk management.
problem Improving the accuracy of covariance matrix estimation for portfolio risk management.
method Formulated as a learning problem, used deep learning to automatically discover risk factors.
result 1.9% higher explained variance and reduced portfolio risk.
New statistical factors improve portfolio risk estimation.
problem Improving estimation of portfolio risk using new statistical factors.
method Matrix factor models and statistical methods (partial F test, double selection LASSO).
result New statistical factors add explanatory power in asset pricing.
Russian-Doll Risk Modelsq-fin.PM
We give a simple explicit algorithm for building multi-factor risk models. It dramatically reduces the number of or altogether eliminates the risk factors for which the factor covariance matrix needs to be computed. This is achieved via a nested "Russian-doll" embedding: the factor covariance matrix itself is modeled v…
Factor Models for Alpha Streamsq-fin.PM
We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor …
Improves statistical learning bounds with self-concordant losses.
problem Statistical prediction with nuisance components.
method Orthogonal statistical learning with self-concordant loss.
result Non-asymptotic bounds on excess risk improved by a dimension factor.
AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariatescs.LG
AICov integrates population covariates for better COVID-19 forecasting.
problem Forecasting COVID-19 with broader social context.
method Integrative deep learning framework with LSTM and multiple data sources.
result Improved prediction of COVID-19 cases and deaths with population risk factors.
New model explains low-volatility anomaly using adaptive multi-factor approach.
problem Explaining the low-volatility anomaly in stock markets.
method Used Adaptive Multi-Factor (AMF) model with GIBS algorithm to identify significant risk factors.
result Low-volatility portfolios perform better due to loaded risk factors, not just low volatility.
The study measures systemic risk using common and tail dependence factors.
problem Measuring systemic risk accurately during economic downturns.
method Modeling systemic risk with a common factor for market-wide shocks and a tail dependence factor for extreme events.
result Measures including a tail dependence factor offer better forecasting of financial stress than measures based solely on a common factor.
New method improves transductive learning predictions with multiplicative oracle inequalities.
problem Improving transductive learning predictions with known covariates.
method Median of Level-Set Aggregation (MLSA) for transductive LOO prediction.
result Proved multiplicative oracle inequality for LOO error.
The Shapley value theory is used for risk allocation in non-orthogonal risk factors.
problem Risk allocation among non-orthogonal risk factors in financial portfolios.
method Using Shapley value from cooperative game theory to allocate risk contributions.
result Explicit formulas and numerical algorithms for calculating risk allocations are derived.
Sensitivity analysis for individualized effects in OTRs with binary risk factors.
problem Addressing omitted confounding in individualized effects of OTRs.
method Simulation-based sensitivity analysis to simulate unmeasured confounders.
result Benchmarking the strength of omitted confounding for binary risk factors.
New method estimates individual treatment effects using domain generalization.
problem Estimating causal individual treatment effects from observational data with treatment bias.
method Invariant Risk Minimization (IRM) framework to learn predictors invariant to domain-dependent factors.
result IRM-based ITE estimator shows gains over classical regression approaches in settings with pronounced support mismatch.