Adapts Roy's criterion for non-normal returns using Cornish Fisher expansion.
problem Selecting one risky asset from many when returns are non-normal.
method Adapts Roy's criterion via Cornish Fisher expansion for non-normal returns.
result Investment objective consistent with first order stochastic dominance, equal to Sharpe ratio for normal returns.
Paper optimizes international portfolios with new copula-based scenario generation.
problem Optimizing international portfolios with realistic uncertainty models.
method Two-stage stochastic model, regular-vine copula for scenario generation, including transaction costs.
result Proposed method yields better risk-return portfolios than standard approaches.
The paper optimizes portfolios by measuring randomness in asset returns.
problem Challenges in assessing the risk of portfolios due to non-normal asset returns.
method Uses Rényi entropy, an information-theoretic criterion, to quantify uncertainty in asset returns.
result Minimizing Rényi entropy leads to portfolios with better risk-return trade-offs.
Developed criteria for selecting non-normalized models using NCE and score matching.
problem No information criteria for non-normalized models estimated by NCE or score matching.
method Developed information criteria based on discrepancy measures for non-normalized models estimated by NCE or score matching.
result The proposed criteria enable selection of the appropriate non-normalized model in a data-driven manner.
The study revisits portfolio diversification by relaxing assumptions for skewed, multi-regime, and leptokurtic asset returns.
problem Underestimation of risk in portfolio diversification due to assumptions that are inconsistent with real-world asset returns.
method Calibrated a Markov-modulated Levy process model to equity market data to demonstrate the merits of the approach.
result The calibrated models effectively match empirical moments and show the importance of relaxing assumptions in portfolio diversification.
We present some stylized facts exhibited by the time series of returns of the Mexican Stock Exchange Index (IPC) and compare them to a sample of both developed (USA, UK and Japan) and emerging markets (Brazil and India). The period of study is 1997-2011. The stylized facts are related mostly to the probability distribu…
The study explains stock return distributions using reaction functions.
problem Stock return distributions often deviate from normal distributions.
method Assumes normal event/information effects, financial over/underreaction, proposes reaction function model.
result Financial markets often underreact to minor events, overreact to significant ones, and react stronger to positive events.
Non-normal RNNs outperform orthogonal ones in sequential tasks.
problem Vanishing/exploding gradients in RNNs training.
method Investigate non-normal RNNs with non-normal recurrent connectivity matrix.
result Non-normal RNNs outperform orthogonal ones in various benchmarks.
Method estimates mixture models without normalization.
problem Estimating mixture models with intractable normalization.
method Extends noise contrastive estimation (NCE) for mixture models.
result Probabilistic clustering using deep representations.
Develops an oblique projection technique to approximate a foliation for non-normal dynamics.
problem Modeling dynamics far from a primary Spectral Submanifold (SSM) in non-normal systems.
method Oblique projection technique based on experimental data.
result Approximates a stable invariant foliation for non-normal dynamics efficiently.
Non-normal subgroups of certain groups grow homologically exponentially.
problem Homological torsion growth in non-normal subgroups of specific groups.
method Proving exponential growth of homological torsion in a sequence of non-normal subgroups.
result Exponential homological torsion growth in a sequence of non-normal subgroups.
Paper proposes MMW distribution for better financial risk modeling.
problem Modeling non-normal stock returns for risk estimation.
method Mixture of mirrored Weibull (MMW) distribution for flexible risk modeling.
result MMW model outperforms Gaussian and t-mixture models in VaR estimation.
Researchers create non-normal affine structures for 3-manifolds, calculating Poincaré series.
problem Calculating Poincaré series for plumbed 3-manifolds.
method Constructing non-normal affine monoids and modules associated with negative definite plumbed 3-manifolds.
result Combinatorial formulas for Seiberg-Witten invariants and polynomial generalizations.
Methods for prediction and tolerance intervals in non-normal models.
problem Constructing prediction and tolerance intervals for non-normal data.
method Two approaches: pivotal quantity approximation and confidence interval for mean.
result Intuitive, simple, efficient methods with proper operating characteristics.
This article proposes a new method for the estimation of the parameters of a simple linear regression model which accounts for the role of co-moments in non-Gaussian distributions being based on the minimization of a quartic loss function. Although the proposed method is very general, we examine its application to fina…
GLM-PCA simplifies complex data for easier analysis.
problem Non-normally distributed data complicates dimension reduction.
method Derives GLM-PCA, incorporates covariates, and suggests transformations.
result Improves interpretability of latent factors in non-normal data.
This paper offers a precise analytical characterization of the distribution of returns for a portfolio constituted of assets whose returns are described by an arbitrary joint multivariate distribution. In this goal, we introduce a non-linear transformation that maps the returns onto gaussian variables whose covariance …
We propose a probabilistic framework for pricing derivatives, which acknowledges that information and beliefs are subjective. Market prices can be translated into implied probabilities. In particular, futures imply returns for these implied probability distributions. We argue that volatility is not risk, but uncertaint…
New formulae identify discrete probability laws without needing normalization constants.
problem Characterizing non-normalized discrete probability distributions.
method Derive explicit formulae for mass functions using Stein's method.
result Developed tools for solving statistical problems without normalization constants.
The paper forecasts crypto-currency returns using a time-varying VAR model with t-distributed errors and shrinkage priors.
problem Forecasting daily returns of crypto-currencies with rapid changes and non-normal errors.
method Developed a time-varying parameter VAR model with t-distributed measurement errors and stochastic volatility. Used shrinkage priors to control overparameterization.
result The proposed models outperform the naive random walk benchmark in real-time forecasting.
GT-Score reduces overfitting in trading strategies by integrating multiple criteria.
problem Overfitting in data-driven financial models leads to unreliable out-of-sample performance.
method Integrates performance, statistical significance, consistency, and downside risk into a composite objective function.
result Improves generalization ratio by 98% compared to baseline objective functions in walk-forward validation.
The paper studies matrix normalization and graph balancing using a new functional and gradient descent.
problem Matrix normalization and graph balancing.
method A new functional called the non-normal energy, and gradient descent.
result Gradient descent of the non-normal energy converges to balanced graphs and preserves spectra and realness of weights.
ROME improves density estimation for multi-modal, non-normal data.
problem Robust multi-modal density estimation in non-normal, highly correlated distributions.
method ROME uses clustering to segment multi-modal data into uni-modal clusters, then combines KDE estimates for each cluster.
result ROME outperforms state-of-the-art methods and is more robust to various distributions.
Paper introduces non-normal MoE models for better data fitting.
problem Data with asymmetric behavior, heavy tails, and outliers.
method Developed SNMoE, TMoE, and STMoE models using EM and ECM algorithms.
result Effective and robust models for non-linear regression and clustering.
It is proved that if S^6 possesses an integrable complex structure, then there exists a 1-dimensional family of pairwise different exotic complex structures on P_3(C). This follows immediately from the main result of the paper: S^6 is not the underlying differentiable manifold of an almost homogeneous complex manifold …
We treat a non-normal Fefferman-type construction based on an inclusion $\SL(n+1)\embed\Spin(n+1,n+1)$. The construction associates a split signature (n,n)-conformal spin structure to a projective structure of dimension n. For n≥3 the induced conformal Cartan connection is shown to be normal if and only if it…
The study uses CoDa to analyze family business financial ratios, highlighting methodological issues.
problem Asymmetry, non-normality, and non-linearity in financial ratios of family businesses.
method Compositional data analysis (CoDa) and classical analysis strategies.
result Results are sensitive to the methodology used, emphasizing the need for appropriate methodologies.
New process capability index for non-normal data.
problem Measuring process capability when data does not follow normal distributions.
method Developed a new multivariate non-parametric PCI using Support Vector Data Description (SVDD).
result Demonstrated improved accuracy in process capability measurement for non-normal data.
Robust GQDA improves classification accuracy in non-Normal data.
problem Non-robustness of GQDA under data contamination.
method Introduced robust estimators for mean vector and dispersion matrix.
result Robust GQDA classifiers perform significantly better in real data applications.
This study improves hyperparameter optimization for categorical and non-normal data.
problem Bayesian hyperparameter optimization struggles with categorical hyperparameters and non-normal data.
method Integrates conformalized quantile regression to address estimation weaknesses and provides robust calibration guarantees.
result Quantile surrogate architectures and acquisition functions yield superior performance compared to existing methods.
The paper introduces a dynamic MVP model using high-frequency financial data.
problem Capturing the dynamics of minimum variance portfolio weights in financial markets.
method Imposes autoregressive structure on MVP processes and uses CLIME and LASSO for estimation.
result Proposes DR-MVP model with established asymptotic properties.
The paper improves the empirical bootstrap method for non-normal estimators.
problem Theoretical properties of empirical bootstrap for non-asymptotically normal estimators.
method Establishing limiting distribution, deriving consistency conditions, proposing alternative methods.
result The empirical bootstrap method can be asymptotically consistent under stability conditions.
Develops methods for reliable inference on batched bandit data.
problem Need for reliable inference methods based on adaptively-collected data from bandit algorithms.
method Introduces Batched OLS (BOLS) estimator for reliable inference on bandit data.
result BOLS is asymptotically normal and robust to non-stationarity in the baseline reward.
For test configurations, the Donaldson-Futaki invariant F_1 is well-known. In this note, its refinement will be discussed. Then we see that Li-Xu's pathology doesn't occur, since their example of a non-normal test configuration, with trivial normalization, actually has non-vanishing F_1 in this refined sense.
Proposes a new RNN structure to improve expressivity without sacrificing stability.
problem Exploding and vanishing gradient problems in RNNs and reduced expressivity.
method Introduces a non-normal RNN structure using Schur decomposition and splitting.
result Enhances expressivity while maintaining stability and training speed.
Study on neural networks with non-normal interactions reveals unique spectral properties.
problem Understanding episodic memory encoding in the brain.
method Developed a neural network model with non-Hermitian couplings and applied random matrix theory.
result Spectral density of the model is non-uniform and can transition to chaos, providing computational benefits.
This paper deals with stability in the numerical solution of the prominent Heston partial differential equation from mathematical finance. We study the well-known central second-order finite difference discretization, which leads to large semi-discrete systems with non-normal matrices A. By employing the logarithmic sp…
The paper develops methods for conditional inference on the asset with the highest Sharpe ratio.
problem Performing inference on the asset with the highest Sharpe ratio among correlated assets.
method Conditional inference procedure using multivariate Sharpe ratio standard error, alternative tests, and asymptotic adjustments.
result The conditional inference procedure achieves nominal type I rate and maintains near-nominal rejection rates under the conditional null.
Investigates if adding cryptocurrencies to German portfolios diversifies better, finding mixed results.
problem Improving diversification in German investor portfolios using cryptocurrencies.
method Portfolio analysis with descriptive statistics, graphical methods, and econometric spanning tests, using a customized EWCI.
result Cryptocurrencies can improve diversification in some windows but not as a normal case.
Extends corner structure study to general case, constructs normal Trans-Sasakian structures.
problem Extending corner structure study to general case without conditions.
method Extends corner structure to general case, constructs Trans-Sasakian structures from non-normal corner structures.
result Constructs normal Trans-Sasakian structures from non-normal corner structures.
New algorithms for efficient matrix profile computation using various Euclidean distances.
problem Efficiently computing matrix profile for all-pairs-similarity search on time series.
method Proposed AAMP, ACAMP, and extended algorithms for p-norm distance.
result AAMP and ACAMP algorithms outperform existing methods for specific Euclidean distances.
Jordan algebras in information geometry linked to metrics on probability distributions.
problem Understanding Jordan algebras in information geometry.
method Inspired by Kirillov's coadjoint orbits, a pseudo-Riemannian metric is constructed on Jordan algebra leaves.
result Not all points in the dual space lie on a leaf, and the metric structure depends on the cone of positive functionals.
This paper introduces compositional data analysis for financial ratios, improving industry-level analysis.
problem Statistical issues with standard financial ratios at industry level.
method Compositional data analysis techniques for financial ratios.
result Improved analysis of financial ratios using compositional data methods.
We study in this paper previously defined by V.N. Berestovskii and C.P. Plaut δ-homogeneous spaces in the case of Riemannian manifolds. Every such manifold has non-negative sectional curvature. The universal covering of any δ-homogeneous Riemannian manifolds is itself δ-homogeneous. In turn, every simply connecte…
Characterizes metrics with finite total Q-curvature and introduces new volume entropy.
problem Understanding metrics with finite total Q-curvature and their geometric properties.
method Characterization of metrics through total Q-curvature and introduction of new volume entropy.
result Controlled volume growth for complete metrics with finite total Q-curvature and bounded scalar curvature.
We propose a unified methodology to input non-linear views from any number of users in fully general non-normal markets, and perform, among others, stress-testing, scenario analysis, and ranking allocation. We walk the reader through the theory and we detail an extremely efficient algorithm to easily implement this met…
New method improves deep learning model robustness and accuracy for long sequences.
problem Challenges in learning long-range sequence tasks using state-space models.
method Proposes a perturb-then-diagonalize (PTD) methodology to address ill-posed diagonalization problems in SSMs.
result Demonstrates improved robustness and accuracy of S5-PTD model on Long-Range Arena benchmark.
Study maximum likelihood estimators for a jump Heston model, proving consistency and normality.
problem Estimating drift parameters in a jump-type Heston model with non-Gaussian jumps.
method Asymptotic analysis of maximum likelihood estimators based on continuous observations.
result Strong consistency and asymptotic normality for most parameter values, weak consistency and mixed normality for one.