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The Gini score is a popular tool in statistical modeling and machine learning for model validation and model selection. It is a purely rank based score that allows one to assess risk rankings. The Gini score for statistical modeling has…

Machine Learning · Statistics 2025-11-20 Alexej Brauer , Mario V. Wüthrich

I demonstrate that with the market return determined by the equilibrium returns of the CAPM, expected returns of an asset are affected by the risks of all assets jointly. Another implication is that the range of feasible market returns will…

General Finance · Quantitative Finance 2021-05-24 Andreas Krause

We introduce a directed, weighted random graph model, where the edge-weights are independent and beta-distributed with parameters depending on their endpoints. We will show that the row- and column-sums of the transformed edge-weight matrix…

Statistics Theory · Mathematics 2017-08-09 Marianna Bolla , Ahmed Elbanna , Jozsef Mala

We consider a financial market in which two securities are traded: a stock and an index. Their prices are assumed to satisfy the Black-Scholes model. Besides assuming that the index is a tradable security, we also assume that it is…

Portfolio Management · Quantitative Finance 2011-09-26 Vladimir Vovk

Frazzini and Pedersen (2014) Betting Against Beta (BAB) factor is based on the idea that high beta assets trade at a premium and low beta assets trade at a discount due to investor funding constraints. However, as argued by Campbell and…

Portfolio Management · Quantitative Finance 2024-09-04 Miguel C. Herculano

We discuss the foundations of factor or regression models in the light of the self-consistency condition that the market portfolio (and more generally the risk factors) is (are) constituted of the assets whose returns it is (they are)…

Physics and Society · Physics 2009-11-13 Y. Malevergne , D. Sornette

Behavioural economics provides labels for patterns in human economic behaviour. Probability weighting is one such label. It expresses a mismatch between probabilities used in a formal model of a decision (i.e. model parameters) and…

Theoretical Economics · Economics 2020-05-04 Ole Peters , Alexander Adamou , Mark Kirstein , Yonatan Berman

A novel approach for comparing quality attributes of different products when there is considerable product-related variability is proposed. In such a case, the whole range of possible realizations must be considered. Looking, for example,…

Methodology · Statistics 2024-08-30 Gerhard Gössler , Vera Hofer , Hans Manner , Walter Goessler

We discuss a weighted estimation of correlation and covariance matrices from historical financial data. To this end, we introduce a weighting scheme that accounts for similarity of previous market conditions to the present one. The…

Statistical Finance · Quantitative Finance 2010-07-01 Michael C. Münnix , Rudi Schäfer , Oliver Grothe

Consider a linear regression model with regression parameter beta and normally distributed errors. Suppose that the parameter of interest is theta = a^T beta where a is a specified vector. Define the parameter tau = c^T beta - t where c and…

Statistics Theory · Mathematics 2017-10-18 Paul Kabaila , Gayan Dharmarathne

In this paper, we build on using the class of f-divergence induced coherent risk measures for portfolio optimization and derive its necessary optimality conditions formulated in CAPM format. We derive a new f-Beta similar to the Standard…

Portfolio Management · Quantitative Finance 2023-05-15 Rui Ding

Class-level evaluation can conceal substantial performance disparities across subconcepts within the same class, causing models that perform well on average to fail on specific subpopulations. Prior work has shown that common evaluation…

Machine Learning · Computer Science 2026-04-30 Taylor Maxson , Roberto Corizzo , Yaning Wu , Nathalie Japkowicz , Colin Bellinger

A statistical estimation model with qualitative input provides a mechanism to fuse human intuition in the form of qualitative information into a statistical model. We investigate the statistical properties of this model and devise a…

Applications · Statistics 2025-10-21 Seksan Kiatsupaibul , Pariyakorn Maneekul

Statistical inference in high-dimensional settings is challenging when standard unregularized methods are employed. In this work, we focus on the case of multiple correlated proportions for which we develop a Bayesian inference framework.…

Methodology · Statistics 2025-06-23 Max Westphal

In this paper we consider an extension of the beta regression model proposed by Ferrari and Cribari-Neto (2004). We extend their model in two different ways, first, we let the regression structure be nonlinear, second, we allow a regression…

Methodology · Statistics 2009-10-24 Alexandre B. Simas , Wagner Barreto-Souza , Andréa V. Rocha

Beta regression is often used to model the relationship between a dependent variable that assumes values on the open interval (0,1) and a set of predictor variables. An important challenge in beta regression is to find residuals whose…

Methodology · Statistics 2017-04-11 Gustavo H. A. Pereira

In actuarial practice the dependency between contract limitations (deductibles, copayments) and health care expenditures are measured by the application of the Monte Carlo simulation technique. We propose, for the same goal, an alternative…

Methodology · Statistics 2020-11-19 Fabio Baione , Davide Biancalana , Paolo De Angelis

We compute the dependence on the classical action "gauge" parameters of the beta functions of the standard topological sigma model in flat space. We thus show that their value is a "gauge" artifact indeed. We also show that previously…

High Energy Physics - Theory · Physics 2009-10-30 Luis Alvarez-Consul , C. P. Martin

The paper covers the new model of wage distribution in typical group of people. The model provides the opportunity to reparameterize applicable income distribution model: Pareto, logarithmically normal, logarithmically logistic, Dagum etc.…

General Finance · Quantitative Finance 2013-09-17 Dmitry Schmerling

Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's…

Machine Learning · Statistics 2018-06-20 Yixin Wang , Alp Kucukelbir , David M. Blei