The study proposes a method for risk reduction without relying on risk measurement.
problem Theoretical utopia of risk minimization vs. practical risk reduction.
method Generalization of matrix rank and condition number for identifying riskiest scenarios.
result Risk reduction achieved without risk measurement, validated by real data.
Paper explores how risk-averse individuals' willingness to pay for insurance varies with risk probability.
problem Understanding how risk-averse individuals' willingness to pay for insurance varies with risk probability.
method Analyzes willingness to pay (WTP) for partial risk reduction within the dual theory of decision.
result In dual theory, reducing the probability of risk and providing insurance can be complementary if the surplus increases with risk reduction.
The paper analyzes how to combine self-protection and self-insurance for risk reduction.
problem Combining self-protection and self-insurance for risk reduction when market insurance is absent.
method The approach uses Value-at-Risk and Tail Value-at-Risk to evaluate residual risk and solves the problem using isoquant geometry based on marginal-balance curves.
result The analysis identifies the conditions under which self-protection and self-insurance behave as substitutes or complements.
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.
A model order reduction framework reduces financial risk analysis models efficiently.
problem Simulating high-dimensional financial risk models.
method Adaptive greedy sampling based on POD and surrogate modeling.
result Reduced models provide significant speedup with excellent accuracy.
This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dy…
Auto insurers improve risk assessment using t-SNE.
problem Accurate risk estimation for auto insurance policyholders.
method Combining neural network with t-SNE for dimensionality reduction.
result Visual representation of risk as a 2D surface, revealing high vs low risk policyholders.
Families of exact solutions are found to a nonlinear modification of the Black-Scholes equation. This risk-adjusted pricing methodology model (RAPM) incorporates both transaction costs and the risk from a volatile portfolio. Using the Lie group analysis we obtain the Lie algebra admitted by the RAPM equation. It gives …
New methods reduce bias in estimating optimality gaps for risk-averse stochastic programs.
problem Optimality gap estimation bias in risk-averse stochastic programs.
method Two independent samples, each estimating a different component of the optimality gap.
result Our method reduces bias in estimating optimality gaps for risk-averse problems.
Optimizes differentially private kernel learning with random projection.
problem Privacy-preserving learning algorithms with optimal performance.
method Differentially private kernel ERM algorithm based on random projection in reproducing kernel Hilbert space.
result Achieves minimax-optimal excess risk rates for various loss functions.
A new algorithm reduces bias and variance in distributionally robust optimization.
problem Distributionally robust optimization with bias and variance issues.
method Prospect, a stochastic gradient-based algorithm that reduces hyperparameter tuning.
result Prospect achieves linear convergence and 2-3x faster convergence on various benchmarks.
New method turns optimization algorithms into uniformly stable learning algorithms for non-Euclidean norms.
problem Non-Euclidean norms in binary classification problems.
method Black-box reduction method using uniformly convex regularizers.
result Achieves optimal statistical risk bounds on excess risk for non-Euclidean norms.
Paper proposes an algorithm to optimize CVaR using retrospective approximation and importance sampling.
problem Optimizing risk-averse problems with large sample requirements for CVaR.
method Retrospective approximation combined with importance sampling, tailored for CVaR optimization.
result The proposed algorithm reduces variance efficiently and is computationally efficient.
Reduces complexity of financial contagion dynamics on networks.
problem Complexity of financial contagion dynamics on networks.
method Dimensional reduction methods (degree-weighted and spectral reduction).
result Spectral reduction better handles heterogeneous networks.
PCA-based dimensionality reduction improves robustness in overparameterized linear models.
problem Improving robustness in overparameterized linear models.
method PCA-based dimensionality reduction (PCA-OLS)
result PCA-OLS can achieve better generalization than ordinary least squares (OLS) in the overparameterized regime.
We analyze the possibility of reduction of systemic risk in financial markets through Pigouvian taxation of financial institutions which is used to support the rescue fund. We introduce the concept of the cascade risk with a clear operational definition as a subclass and a network related measure of the systemic risk. …
Paper develops a two-population model to assess longevity basis risk.
problem Mismatch between hedger's liability and hedging instrument causes longevity basis risk.
method Develops a two-population mortality model using Lee-Carter model and renewal process.
result Proposed model provides significant risk reduction when mortality jumps and sampling risk are considered.
This paper considers the problem of estimating a high-dimensional vector of parameters θ∈Rn from a noisy observation. The noise vector is i.i.d. Gaussian with known variance. For a squared-error loss function, the James-Stein (JS) estimator is known to dominate the simple maximum-likelihood (…
The risk minimizing problem E[l((H−XTx,π)+)]⟶πmin in the multidimensional Black-Scholes framework is studied. Specific formulas for the minimal risk function and the cost reduction function for basket derivatives are shown. Explicit integral representations for the risk functi…
We consider the problem of simulating loss probabilities and conditional excesses for linear asset portfolios under the t-copula model. Although in the literature on market risk management there are papers proposing efficient variance reduction methods for Monte Carlo simulation of portfolio market risk, there is no pa…
The paper analyzes portfolio credit risk using Archimedean copulas and introduces efficient simulation methods.
problem Analyzing large losses from credit portfolio defaults with Archimedean copulas.
method Derives asymptotic results and develops variance reduction algorithms for Monte Carlo simulations.
result Proposed algorithms significantly enhance classical Monte Carlo methods for estimating portfolio credit risk.
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…
Banks in the interbank network can not assess the true risks associated with lending to other banks in the network, unless they have full information on the riskiness of all the other banks. These risks can be estimated by using network metrics (for example DebtRank) of the interbank liability network which is availabl…
Investigates JM for reducing downside risk in market regimes.
problem Mitigating downside risk during market downturns.
method Statistical jump model for identifying market regimes, optimizing penalty for state transitions.
result JM-guided strategies outperform traditional models in reducing risk and enhancing returns.
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…
This paper simplifies finding least favorable priors by reducing dimensionality.
problem Finding least favorable priors is challenging due to infinite-dimensional optimization.
method Develops a dimensionality reduction method using Bregman divergences.
result Allows use of gradient ascent algorithms for finding least favorable priors.
Deep neural networks reduce portfolio tail-risk by 99% in crisis-era simulations.
problem Managing tail risk in financial portfolios.
method Parameterizing convex-risk minimization with deep neural networks.
result Significant reduction in one-day 99% CVaR.
Repeated self-distillation improves model performance significantly.
problem How much gain is possible by applying multiple steps of self-distillation?
method Investigated linear regression tasks, applied multiple steps of self-distillation, analyzed excess risk reduction.
result Multi-step self-distillation reduces excess risk by a factor as large as d, where d is the input dimension. This paper analyzes P2P collaborative insurance products and network structure impact.
problem Analyzing P2P collaborative insurance products and their network structure impact.
method Examined a P2P insurance product with reciprocal risk sharing contracts, studied network structure impact on risk reduction, and discussed optimal reciprocal commitments.
result The network structure, particularly the distribution of degrees, significantly impacts risk reduction in P2P insurance products.
VR-ConfTr reduces noise in CP training, leading to more stable and efficient model performance.
problem Improving the efficiency and stability of conformal prediction during model training.
method Variance-reduced conformal training (VR-ConfTr) that incorporates variance reduction in gradient estimation.
result VR-ConfTr achieves faster convergence and smaller prediction sets compared to existing methods.
We consider a composite convex minimization problem associated with regularized empirical risk minimization, which often arises in machine learning. We propose two new stochastic gradient methods that are based on stochastic dual averaging method with variance reduction. Our methods generate a sparser solution than the…
With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the healthcare domain. There are tangible works in the literature emphasizing on developing readmission risk prediction m…
Paper explores how unsupervised learning reduces financial crime risks.
problem Identifying high-risk financial groups from complex data.
method Combines clustering and dimensionality reduction techniques.
result KPCA outperforms other techniques in reducing financial crime risks.
New method improves scalability of SGD for large datasets.
problem High variance in stochastic gradient descent.
method Adaptive measure reduction with Carathéodory's theorem.
result Improved scalability to high-dimensional spaces.
The paper examines how risk reduction and insurance choices interact under convex premium principles.
problem Interaction between self-protection and insurance demand under convex premium principles.
method Investigates optimal prevention efforts and insurance shares using distortion risk measures.
result Self-protection and insurance are complementary, but ex ante moral hazard can turn this into a substitution effect.
We report the results of fifteen sets of portfolio selection simulations using stocks in the ASX200 index for the period May 2000 to December 2013. We investigated five portfolio selection methods, randomly and from within industrial groups, and three based on neighbor-Net phylogenetic networks. We report that using ra…
NKI integrates obfuscated datasets using nonlinear kernels for improved data collaboration.
problem Privacy-preserving data collaboration with reduced reconstruction risk.
method Formulates linear kernel integration, kernelizes it, and introduces graph regularization and centering constraints.
result NKI improves classification accuracy over existing linear integration methods under nonlinear dimensionality reduction.
A new framework for bilevel optimization tackles stochastic and global variance reduction.
problem Bilevel optimization challenges in large-scale empirical risk minimization.
method Introducing a novel framework where inner and main variables evolve simultaneously, leading to unbiased estimates and global variance reduction algorithms.
result SABA algorithm achieves $O( rac{1}{T})$ convergence rate and linear convergence under Polyak-Lojasciewicz assumption.
We investigate a multi-factor extension of the asymptotic single risk factor (ASRF) model that underlies the capital charges of the "Basel II Accord". In this extended model, it is still possible to derive closed-form solutions for the risk contributions to Value-at-Risk and Expected Shortfall. As an application of the…
This paper presents a model order reduction (MOR) approach for high dimensional problems in the analysis of financial risk. To understand the financial risks and possible outcomes, we have to perform several thousand simulations of the underlying product. These simulations are expensive and create a need for efficient …
We give a complete algorithm and source code for constructing what we refer to as heterotic risk models (for equities), which combine: i) granularity of an industry classification; ii) diagonality of the principal component factor covariance matrix for any sub-cluster of stocks; and iii) dramatic reduction of the facto…
Efficiently solves large portfolio optimization problems by reducing and sparsifying covariance matrices.
problem Large and dense covariance matrices limit efficient portfolio optimization.
method Dimension reduction and increased sparsity based on machine learning predictions.
result Improved portfolio performance and reduced runtime compared to full dense covariance matrices.
From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning. However, if its model is very flexible, empirical risks on training data will go negative, and we will suffer from serious overfitting. In this paper, we propose a…
We discuss a general dynamic replication approach to counterparty credit risk modeling. This leads to a fundamental jump-process backward stochastic differential equation (BSDE) for the credit risk adjusted portfolio value. We then reduce the fundamental BSDE to a continuous BSDE. Depending on the close out value conve…
Faster Ethereum slots boost CEX-DEX arbitrage by 535% and 203%.
problem Reducing Ethereum slot time impacts CEX-DEX arbitrage opportunities.
method Developed a trading model to simulate and compare agent behavior under different slot times.
result Faster slot times increase CEX-DEX arbitrage activity and returns.
Machine learning factors outperform traditional portfolio optimization methods.
problem Comparing machine learning and traditional portfolio optimization methods.
method Examined machine learning and factor-based portfolio optimization using autoencoder neural networks and dimensionality reduction techniques.
result Minimum-variance portfolios using latent factors derived from autoencoders and sparse methods outperform simpler benchmarks in risk minimization.
New model explains option pricing with time-varying volatility risk aversion.
problem Time variations in the shape of the pricing kernel.
method Introduced a pricing kernel with time-varying volatility risk aversion combined with Heston-Nandi GARCH model.
result Variance risk ratio (VRR) emerges as a key variable in option pricing.
Sparse grids reduce xVA exposure evaluations by up to 6000 times.
problem Efficiently computing exposures for xVA in large portfolios with many risk factors.
method Sparse Grid Method combined with Stochastic Collocation and Smolyak's extension.
result Significant reduction in the number of portfolio evaluations, up to 6000 times.