Monetary risk measures are usually interpreted as the smallest amount of external capital that must be added to a financial position to make it acceptable. We propose a new concept: intrinsic risk measures and argue that this approach provides a direct path from unacceptable positions towards the acceptance set. Intrin…
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.
Introduces an unobservable intrinsic electricity price to link storage theory with risk premium.
problem Connecting storage theory with risk premium in electricity markets.
method Introduces an unobservable intrinsic electricity price and derives prices for various contracts.
result Finds an overall negative risk premium in empirical analysis.
We review the nature of some well-known phenomena such as volatility smiles, convexity adjustments and parallel derivative markets. We propose that the market is incomplete and postulate the existence of intrinsic risks in every contingent claim as a basis for understanding these phenomena. In a continuous time framewo…
Proposes eDNNs and iDNNs for deep learning on manifolds.
problem Deep learning on manifolds with geometric preservation and intrinsic geometry incorporation.
method Intrinsic and extrinsic deep neural networks (iDNNs and eDNNs) with geometric embeddings and maps.
result Empirical risk minimizers of eDNNs and iDNNs converge optimally.
Financial volatility risk and its relation to a business cycle-related intrinsic time is addressed through a multiple round evolutionary quantum game equilibrium leading to turbulence and multifractal signatures in the financial returns and in the risk dynamics. The model is simulated and the results are compared with …
Study uses CSIE to estimate portfolio volatility relative to market.
problem Estimating relative volatility risk of stock portfolios.
method Cross-sectional intrinsic entropy (CSIE) model to estimate cross-sectional volatility.
result Discover sets of symbols that outperform market indices in terms of return with similar or lower risk.
Wasserstein Autoencoders improve model efficiency and interpretability for low-dimensional data.
problem Limited statistical guarantees for WAEs in low-dimensional data.
method Proper network architecture selection and analysis of expected excess risk convergence rates.
result WAEs can learn data distributions efficiently when intrinsic dimension is considered.
This work analyzes CVaR under heavy-tailed data, providing generalization and robustness bounds.
problem Understanding CVaR's behavior under heavy-tailed data and rare high-impact losses.
method Learning-theoretic analysis of CVaR-based empirical risk minimization.
result Sharp, high-probability generalization and excess risk bounds under minimal moment assumptions.
Paper introduces CSIE for estimating stock market volatility.
problem Temporal uncertainty in stock market volatility.
method Cross-sectional intrinsic entropy model based on OHLC prices.
result CSIE is 10 times more sensitive to market changes.
Study finds multifractal cross-correlations between agricultural markets and external uncertainties.
problem Investigating relationships between agricultural spot markets and external uncertainties.
method Multifractal detrending moving-average cross-correlation analysis (MF-X-DMA).
result Maize exhibits intrinsic joint multifractality with all uncertainty proxies.
The risk premium is one of main concepts in mathematical finance. It is a measure of the trade-offs investors make between return and risk and is defined by the excess return relative to the risk-free interest rate that is earned from an asset per one unit of risk. The purpose of this article is to determine upper and …
Deep Evidence Regression improves credit risk prediction uncertainty.
problem Quantifying uncertainty in credit risk predictions.
method Applying Deep Evidence Regression to credit risk settings.
result Demonstrated improved prediction of Loss Given Default.
The instability of the financial system as experienced in recent years and in previous periods is often linked to credit defaults, i.e., to the failure of obligors to make promised payments. Given the large number of credit contracts, this problem is amenable to be treated with approaches developed in statistical physi…
A new risk measure framework captures multivariate risk in banking.
problem Scalar risk measures fail to capture the multivariate nature of risk in banking.
method A novel multivariate risk measure framework based on the Magnitude-Propensity approach.
result The proposed framework provides a more comprehensive characterization of extreme events.
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).
Proposes tests for comparing high-dimensional manifold samples.
problem Determining if two manifold samples come from the same distribution.
method Integral Probability Metric (IPM) with neural network approximations.
result Tests achieve type-II risk in specific orders of n. ConvResNets approximate Besov functions and classify on low-dimensional manifolds.
problem Lack of statistical theories for deep learning on high-dimensional data.
method Exploits low-dimensional geometric structures of real-world data sets using ConvResNets.
result ConvResNets can approximate Besov functions and learn classifiers with optimal excess risk.
Improved MLMC method boosts risk estimation efficiency.
problem Estimating risk measures like Value-at-Risk in financial risk management.
method Novel MLMC parametrization and antithetic sampling.
result Significantly improved performance in practical settings.
We present a model of predatory traders interacting with each other in the presence of a central reserve (which dissipates their wealth through say, taxation), as well as inflation. This model is examined on a network for the purposes of correlating complexity of interactions with systemic risk. We suggest the use of s…
Compact formulas for evaluating insurance policies' risks.
problem Quantifying demographic risk in insurance portfolios.
method Cohort-based approach with market-consistent valuation.
result Formal closed formula for idiosyncratic risk (accidental mortality).
Paper uses TDA to assess cryptocurrency risk by measuring phase space instability.
problem Traditional risk measures fail to capture market dynamics' geometric structure.
method Applied Takens' Delay Embedding Theorem to generate point cloud, computed persistent homology groups, defined Topological Persistence Norm.
result Proposed leverage calibration heuristic based on persistence of 1-dimensional cycles.
Graph neural networks improve SME credit risk assessment.
problem Improving credit risk assessment for small and medium enterprises (SMEs).
method Graph neural networks were used to model the relationships between financial indicators of enterprises, creating a graph structure and embedding representations for credit risk prediction.
result The proposed model accurately predicts enterprise credit levels, demonstrating robustness and effectiveness.
We propose a method to assess the intrinsic risk carried by a financial position X when the agent faces uncertainty about the pricing rule assigning its present value. Our approach is inspired by a new interpretation of the quasiconvex duality in a Knightian setting, where a family of probability measures replaces th…
New risk measure and quadrangle improve financial decision-making.
problem Heterogeneous risk assessments among analysts.
method Established analytical characterizations of WGRM and incorporated FRQ into WRQ.
result WGRM and WRQ framework improves risk-adjusted performance and downside resilience.
Paper breaks down risk contribution into inherent and correlation risk components.
problem Understanding the sources of risk in portfolio contributions.
method Leave-one-out decomposition approach to separate inherent and correlation risk contributions.
result The decomposition reveals distinct contributions of position volatility and correlation to portfolio risk.
A new method calculates intrinsic effective sample size for manifold-valued data.
problem Challenges in choosing effective sample size for manifold-valued data.
method Proposes an intrinsic effective sample size based on kernel discrepancy.
result Establishes an exact finite-sample risk interpretation and consistency of the estimator.
NDI aims to forecast future natural disasters risk for insurers.
problem Increasing intensity and frequency of natural disasters.
method Develops a Natural Disasters Index (NDI) based on NOAA data.
result NDI forecasts future natural disasters risk for insurers.
PoEL protocol aims to efficiently create and secure liquidity for blockchain networks.
problem Lack of sustainable liquidity and network security in Proof of Stake blockchains.
method PoEL uses staking rewards to attract risk capital, structuring incentives for capital efficiency and security.
result PoEL protocol enhances blockchain network security and liquidity sustainability.
The paper develops a new framework for pricing and hedging liquidity in crypto markets.
problem Arbitrage and risk management in crypto market making.
method Developed a new mathematical framework using a coordinate system defined by price and intrinsic liquidity.
result Established a linear dependence of asset reserves and value functions on intrinsic liquidity, facilitating arbitrage-free pricing and delta hedging.
We apply the network Lasso to solve binary classification and clustering problems for network-structured data. To this end, we generalize ordinary logistic regression to non-Euclidean data with an intrinsic network structure. The resulting "logistic network Lasso" amounts to solving a non-smooth convex regularized empi…
This work reviews and tests risk allocation strategies in finance, highlighting Shapley allocation's advantages.
problem Risk allocation in financial institutions with non-additive risk measures and layered structures.
method Systematic review of risk allocation strategies, testing in simplified and realistic settings, including Basel 2.5 and FRTB.
result Shapley allocation offers the best compromise between simplicity, mathematical properties, and computational cost.
We study prediction and estimation problems using empirical risk minimization, relative to a general convex loss function. We obtain sharp error rates even when concentration is false or is very restricted, for example, in heavy-tailed scenarios. Our results show that the error rate depends on two parameters: one captu…
New PAC-Bayes bounds derived using Legendre transform and f-divergences.
problem Deriving PAC-Bayes bounds under various assumptions.
method Combining Legendre transform and Fenchel--Young inequality to derive change-of-measure inequalities.
result Extended PAC-Bayesian guarantees under tailored assumptions.
Paper proposes a risk-aware decision-making framework for real-world sequential decisions.
problem Real-world sequential decision-making problems often have critical constraints that learning solutions often neglect.
method Actor multi-critic architecture with risk characterization.
result Our approach consistently satisfies system constraints with minimal performance toll.
Neural networks provide a rich class of high-dimensional, non-convex optimization problems. Despite their non-convexity, gradient-descent methods often successfully optimize these models. This has motivated a recent spur in research attempting to characterize properties of their loss surface that may explain such succe…
Study mirrors descent's early stopping for linear and kernel models, improving risk guarantees.
problem Understanding the statistical performance of early-stopped mirror descent algorithms.
method Characterized convexity of squared loss, identified link between offset Rademacher complexities and mirror descent convergence.
result Excess risk guarantees for mirror descent iterates traced by the path, expressed in terms of offset complexities.
We consider a system of diffusion processes that interact through their empirical mean and have a stabilizing force acting on each of them, corresponding to a bistable potential. There are three parameters that characterize the system: the strength of the intrinsic stabilization, the strength of the external random per…
The dynamics of protection processes has been a fundamental challenge in systemic risk analysis. The conceptual principle and methodological techniques behind the mechanisms involved [in such dynamics] have been harder to grasp than researchers understood them to be. In this paper, we show how to construct a large vari…
Proposes PRMs for interpreting financial risk concept drift.
problem Concept drift in high-stakes predictions like credit risk.
method Probabilistic Rule Models (PRMs) using Markov Logic Networks.
result Interpretable rules explain borrower risk changes.
Introduces intrinsic Hopf-Lax semigroup linking to intrinsic slope.
problem Understanding intrinsic Hopf-Lax semigroup and its relation to intrinsic slope.
method Introduces and proves the link between intrinsic Hopf-Lax semigroup and intrinsic slope.
result Intrinsic Hopf-Lax semigroup is a subsolution of Hamilton-Jacobi type equality.
New method estimates VaR and ES using high-frequency data, outperforming existing approaches.
problem Limitations of existing VaR and ES estimation methods in high-frequency data.
method Transforms intra-day returns using subordinator process, filters autocorrelation, fits fat-tailed distribution.
result Outperforms existing methods in VaR and ES estimation and forecasting.
This study identifies financial risk paths in digital-transformed enterprises.
problem Identifying financial risks in digital-transformed enterprises.
method DEMATEL-ISM-MICMAC method.
result Political and economic environment affects enterprise's financial structure.
This work considers the problem of binary classification: given training data x1,…,xn from a certain population, together with associated labels y1,…,yn∈{0,1}, determine the best label for an element x not among the training data. More specifically, this work considers a variant o…
This paper proposes a new clustering method based on Stochastic Dominance for asset allocation.
problem Traditional clustering methods fail to capture risk dominance relationships among assets.
method Integrates Stochastic Dominance theory with machine learning algorithms to construct a Stochastic Dominance Coefficient Matrix and modify clustering algorithms.
result The proposed method effectively facilitates customized asset allocation for investors.
What return should you expect when you take on a given amount of risk? How should that return depend upon other people's behavior? What principles can you use to answer these questions? In this paper, we approach these topics by exploring the consequences of two simple hypotheses about risk. The first is a common-sense…
Survey of intrinsically linked or knotted graphs.
problem Understanding graphs that are inherently knotted or linked.
method Survey of existing results.
result Discussion of known properties of intrinsically linked or knotted graphs.
The paper studies properties of intrinsically Lipschitz constants in metric spaces.
problem Investigating properties of intrinsically Lipschitz constants.
method Introduced Leibniz and product formulas for intrinsic slope.
result Formulated Leibniz and product formulas for intrinsic slope.