pwc-archive/papers-with-abstracts huggingface dataset:
Permutation invariant Gaussian matrix models for financial correlation matrices
Authors: George Barnes, Sanjaye Ramgoolam, Michael Stephanou
Date: 2023-06-07 00:00:00
Tasks: Anomaly Detection

We construct an ensemble of correlation matrices from high-frequency foreign exchange market data, with one matrix for every day for 446 days. The matrices are symmetric and have vanishing diagonal elements after subtracting the identity matrix. For this case, we construct the general permutation invariant Gaussian matrix model, which has 4 parameters characterised using the representation theory of symmetric groups. The permutation invariant polynomial functions of the symmetric, diagonally vanishing matrices have a basis labelled by undirected loop-less graphs. Using the expectation values of the general linear and quadratic permutation invariant functions of the matrices in the dataset, the 4 parameters of the matrix model are determined. The model then predicts the expectation values of the cubic and quartic polynomials. These predictions are compared to the data to give strong evidence for a good overall fit of the permutation invariant Gaussian matrix model. The linear, quadratic, cubic and quartic polynomial functions are then used to define low-dimensional feature vectors for the days associated to the matrices. These vectors, with choices informed by the refined structure of small non-Gaussianities, are found to be effective as a tool for anomaly detection in market states: statistically significant correlations are established between atypical days as defined using these feature vectors, and days with significant economic events as recognized in standard foreign exchange economic calendars. They are also shown to be useful as a tool for ranking pairs of days in terms of their similarity, yielding a strongly statistically significant correlation with a ranking based on a higher dimensional proxy for visual similarity.
Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis
Authors: Hung Tran, Tien Mai
Date: 2023-06-07 00:00:00
Tasks: Discrete Choice Models, Multiple-choice

In many choice modeling applications, people demand is frequently characterized as multiple discrete, which means that people choose multiple items simultaneously. The analysis and prediction of people behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such that the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms.
Evaluating the Impact of Regulatory Policies on Social Welfare in Difference-in-Difference Settings
Authors: Dalia Ghanem, Désiré Kédagni, Ismael Mourifié
Date: 2023-06-07 00:00:00
Tasks: counterfactual, Informativeness

Quantifying the impact of regulatory policies on social welfare generally requires the identification of counterfactual distributions. Many of these policies (e.g. minimum wages or minimum working time) generate mass points and/or discontinuities in the outcome distribution. Existing approaches in the difference-in-difference literature cannot accommodate these discontinuities while accounting for selection on unobservables and non-stationary outcome distributions. We provide a unifying partial identification result that can account for these features. Our main identifying assumption is the stability of the dependence (copula) between the distribution of the untreated potential outcome and group membership (treatment assignment) across time. Exploiting this copula stability assumption allows us to provide an identification result that is invariant to monotonic transformations. We provide sharp bounds on the counterfactual distribution of the treatment group suitable for any outcome, whether discrete, continuous, or mixed. Our bounds collapse to the point-identification result in Athey and Imbens (2006) for continuous outcomes with strictly increasing distribution functions. We illustrate our approach and the informativeness of our bounds by analyzing the impact of an increase in the legal minimum wage using data from a recent minimum wage study (Cengiz, Dube, Lindner, and Zipperer, 2019).
Semiparametric Efficiency Gains From Parametric Restrictions on Propensity Scores
Authors: Haruki Kono
Date: 2023-06-07 00:00:00

We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit representations of efficiency bounds, we propose a novel framework that enables us to see whether knowing a parametric model is valuable in terms of efficiency even when it is high-dimensional. In addition to the intuitive fact that knowing the parametric model does not help much if it is sufficiently flexible, we discover that the efficiency gain can be nearly zero even though the parametric assumption significantly restricts the space of possible propensity scores.
Semiparametric Discrete Choice Models for Bundles
Authors: Fu Ouyang, Thomas T. Yang
Date: 2023-06-07 00:00:00
Tasks: Discrete Choice Models, valid

We propose two approaches to estimate semiparametric discrete choice models for bundles. Our first approach is a kernel-weighted rank estimator based on a matching-based identification strategy. We establish its complete asymptotic properties and prove the validity of the nonparametric bootstrap for inference. We then introduce a new multi-index least absolute deviations (LAD) estimator as an alternative, of which the main advantage is its capacity to estimate preference parameters on both alternative- and agent-specific regressors. Both methods can account for arbitrary correlation in disturbances across choices, with the former also allowing for interpersonal heteroskedasticity. We also demonstrate that the identification strategy underlying these procedures can be extended naturally to panel data settings, producing an analogous localized maximum score estimator and a LAD estimator for estimating bundle choice models with fixed effects. We derive the limiting distribution of the former and verify the validity of the numerical bootstrap as an inference tool. All our proposed methods can be applied to general multi-index models. Monte Carlo experiments show that they perform well in finite samples.
International Spillovers of ECB Interest Rates: Monetary Policy & Information Effects
Authors: Santiago Camara
Date: 2023-06-07 00:00:00

This paper shows that disregarding the information effects around the European Central Bank monetary policy decision announcements biases its international spillovers. Using data from 23 economies, both Emerging and Advanced, I show that following an identification strategy that disentangles pure monetary policy shocks from information effects lead to international spillovers on industrial production, exchange rates and equity indexes which are between 2 to 3 times larger in magnitude than those arising from following the standard high frequency identification strategy. This bias is driven by pure monetary policy and information effects having intuitively opposite international spillovers. Results are present for a battery of robustness checks: for a sub-sample of ``close'' and ``further away'' countries, for both Emerging and Advanced economies, using local projection techniques and for alternative methods that control for ``information effects''. I argue that this biases may have led a previous literature to disregard or find little international spillovers of ECB rates.
Sustainability criterion implied externality pricing for resource extraction
Authors: Daniel Grainger
Date: 2023-06-06 00:00:00
Tasks: Form

A dynamic model is constructed that generalises the Hartwick and Van Long (2020) endogenous discounting setup by introducing externalities and asks what implications this has for optimal natural resource extraction with constant consumption. It is shown that a modified form of the Hotelling and Hartwick rule holds in which the externality component of price is a specific function of the instantaneous user costs and cross price elasticities. It is demonstrated that the externality adjusted marginal user cost of remaining natural reserves is equal to the marginal user cost of extracted resources invested in human-made reproducible capital. This lends itself to a discrete form with a readily intuitive economic interpretation that illuminates the stepwise impact of externality pricing on optimal extraction schedules.
An Empirical Study of Obstacle Preemption in the Supreme Court
Authors: Gregory M. Dickinson
Date: 2023-06-05 00:00:00

The Supreme Court's federal preemption decisions are notoriously unpredictable. Traditional left-right voting alignments break down in the face of competing ideological pulls. The breakdown of predictable voting blocs leaves the business interests most affected by federal preemption uncertain of the scope of potential liability to injured third parties and unsure even of whether state or federal law will be applied to future claims. This empirical analysis of the Court's decisions over the last fifteen years sheds light on the Court's unique voting alignments in obstacle preemption cases. A surprising anti-obstacle preemption coalition is forming as Justice Thomas gradually positions himself alongside the Court's liberals to form a five-justice voting bloc opposing obstacle preemption.
Calibrating Chevron for Preemption
Authors: Gregory M. Dickinson
Date: 2023-06-05 00:00:00

Now almost three decades since its seminal Chevron decision, the Supreme Court has yet to articulate how that case's doctrine of deference to agency statutory interpretations relates to one of the most compelling federalism issues of our time: regulatory preemption of state law. Should courts defer to preemptive agency interpretations under Chevron, or do preemption's federalism implications demand a less deferential approach? Commentators have provided no shortage of possible solutions, but thus far the Court has resisted all of them. This Article makes two contributions to the debate. First, through a detailed analysis of the Court's recent agency-preemption decisions, I trace its hesitancy to adopt any of the various proposed rules to its high regard for congressional intent where areas of traditional state sovereignty are at risk. Recognizing that congressional intent to delegate preemptive authority varies from case to case, the Court has hesitated to adopt an across-the-board rule. Any such rule would constrain the Court and risk mismatch with congressional intent -- a risk it accepts under Chevron generally but which it finds particularly troublesome in the delicate area of federal preemption. Second, building on this previously underappreciated factor in the Court's analysis, I suggest a novel solution of variable deference that avoids the inflexibility inherent in an across-the-board rule while providing greater predictability than the Court's current haphazard approach. The proposed rule would grant full Chevron-style deference in those cases where congressional delegative intent is most likely -- where Congress has expressly preempted some state law and the agency interpretation merely resolves preemptive scope -- while withholding deference in those cases where Congress has remained completely silent as to preemption and delegative intent is least likely.
Trade-off between manipulability and dictatorial power: a proof of the Gibbard-Satterthwaite Theorem
Authors: Agustin G. Bonifacio
Date: 2023-06-07 00:00:00

By endowing the class of tops-only and efficient social choice rules with a dual order structure that exploits the trade-off between different degrees of manipulability and dictatorial power rules allow agents to have, we provide a proof of the Gibbard-Satterthwaite Theorem.
Information aggregation with delegation of votes
Authors: Amrita Dhillon, Grammateia Kotsialou, Dilip Ravindran, Dimitrios Xefteris
Date: 2023-06-06 00:00:00

Liquid democracy is a system that combines aspects of direct democracy and representative democracy by allowing voters to either vote directly themselves, or delegate their votes to others. In this paper we study the information aggregation properties of liquid democracy in a setting with heterogeneously informed truth-seeking voters -- who want the election outcome to match an underlying state of the world -- and partisan voters. We establish that liquid democracy admits equilibria which improve welfare and information aggregation over direct and representative democracy when voters' preferences and information precisions are publicly or privately known. Liquid democracy also admits equilibria which do worse than the other two systems. We discuss features of efficient and inefficient equilibria and provide conditions under which voters can more easily coordinate on the efficient equilibria in liquid democracy than the other two systems.
Complex Isotropic α-Stable-Rician Model for Heterogeneous SAR Images
Authors: Mutong Li, Ercan Engin Kuruoglu
Date: 2023-06-07 00:00:00
Tasks: parameter estimation

This article introduces a novel probability distribution model, namely Complex Isotropic {\alpha}-Stable-Rician (CI{\alpha}SR), for characterizing the data histogram of synthetic aperture radar (SAR) images. Having its foundation situated on the L\'evy {\alpha}-stable distribution suggested by a generalized Central Limit Theorem, the model promises great potential in accurately capturing SAR image features of extreme heterogeneity. A novel parameter estimation method based on the generalization of method of moments to expectations of Bessel functions is devised to resolve the model in a relatively compact and computationally efficient manner. Experimental results based on both synthetic and empirical SAR data exhibit the CI{\alpha}SR model's superior capacity in modelling scenes of a wide range of heterogeneity when compared to other state-of-the-art models as quantified by various performance metrics. Additional experiments are conducted utilizing large-swath SAR images which encompass mixtures of several scenes to help interpret the CI{\alpha}SR model parameters, and to demonstrate the model's potential application in classification and target detection.
Model-Based Deep Learning
Authors: Nir Shlezinger, Yonina C. Eldar
Date: 2023-06-05 00:00:00
Tasks: Deep Learning, model, Specificity, Super-Resolution

Signal processing traditionally relies on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex or dynamic behavior. More recently, deep learning approaches that use deep neural networks are becoming increasingly popular. Deep learning systems do not rely on mathematical modeling, and learn their mapping from data, which allows them to operate in complex environments. However, they lack the interpretability and reliability of model-based methods, typically require large training sets to obtain good performance, and tend to be computationally complex. Model-based signal processing methods and data-centric deep learning each have their pros and cons. These paradigms can be characterized as edges of a continuous spectrum varying in specificity and parameterization. The methodologies that lie in the middle ground of this spectrum, thus integrating model-based signal processing with deep learning, are referred to as model-based deep learning, and are the focus here. This monograph provides a tutorial style presentation of model-based deep learning methodologies. These are families of algorithms that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches. Such model-based deep learning methods exploit both partial domain knowledge, via mathematical structures designed for specific problems, as well as learning from limited data. We accompany our presentation with running examples, in super-resolution, dynamic systems, and array processing. We show how they are expressed using the provided characterization and specialized in each of the detailed methodologies.
Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder
Authors: Tengjiao He, Wenguang Wang
Date: 2023-06-04 00:00:00
Tasks: Anomaly Detection, Video Anomaly Detection

Video anomaly detection has great potential in enhancing safety in the production and monitoring of crucial areas. Currently, most video anomaly detection methods are based on RGB modality, but its redundant semantic information may breach the privacy of residents or patients. The 3D data obtained by depth camera and LiDAR can accurately locate anomalous events in 3D space while preserving human posture and motion information. Identifying individuals through the point cloud is difficult due to its sparsity, which protects personal privacy. In this study, we propose Point Spatio-Temporal Auto-Encoder (PSTAE), an autoencoder framework that uses point cloud videos as input to detect anomalies in point cloud videos. We introduce PSTOp and PSTTransOp to maintain spatial geometric and temporal motion information in point cloud videos. To measure the reconstruction loss of the proposed autoencoder framework, we propose a reconstruction loss measurement strategy based on a shallow feature extractor. Experimental results on the TIMo dataset show that our method outperforms currently representative depth modality-based methods in terms of AUROC and has superior performance in detecting Medical Issue anomalies. These results suggest the potential of point cloud modality in video anomaly detection. Our method sets a new state-of-the-art (SOTA) on the TIMo dataset.
Phen-DC 3 Induces Refolding of Human Telomeric DNA into a Chair-Type Antiparallel G-Quadruplex through Ligand Intercalation
Authors: Anirban Ghosh, Marko Trajkovski, Marie-paule Teulade-Fichou, Valérie Gabelica, Janez Plavec
Date: 2023-06-07 00:00:00
Tasks: Drug Design

Human telomeric G-quadruplex DNA structures are attractive anticancer drug targets, but the target's polymorphism complicates the drug design: different ligands prefer different folds, and very few complexes have been solved at high resolution. Here we report that Phen-DC3, one of the most prominent G-quadruplex ligands in terms of high binding affinity and selectivity, causes dTAGGG(TTAGGG)3 to completely change its fold in KCl solution from a hybrid-1 to an antiparallel chair-type structure, wherein the ligand intercalates between a two-quartet unit and a pseudo-quartet, thereby ejecting one potassium ion. This unprecedented high-resolution NMR structure shows for the first time a true ligand intercalation into an intramolecular G-quadruplex.
Quantum Brain Dynamics. A Possibility of Having a Quantum Interpretation of the Brain
Authors: Dozie Iwuh
Date: 2023-06-05 00:00:00

What Quantum Brain Dynamics (QBD) considers is not just these other functions of the brain, this is because they can be well analyzed with the workings of classical mechanics (even though they still play host to a quantum description). It rather considers two specific functions above all else consciousness and memory. QBD falls in line umbrella-covers aspects of the quantum brain analysis such as quantum-consciousness, quantum-mind and quantum-brain. The inspiration that lurks behind the Quantum Interpretation of the Brain (QIB), is traceable to the 1944 article written by E. Schrodinger, What is Life, in which he presents how a living organisms evades decay to equilibrium by the fact of negentropy, as such life which is in its ordered macroscopic state is created (in an environment of disorder), which moves against the second law of thermodynamics. The life that is created, that which is sustained, arises from an interaction that the organism engages in with the environment. This interaction is microscopic, albeit quantum, it is an interaction that underscores the reality of quantum entanglement (which also plays hosts to the superposition of quantum states). The quantum interpretation of the brain is a nascent, yet burgeoning as it might be that necessary tool required for a better articulation and comprehension of the brain.
Balancing the Benefits of Vaccination: an Envy-Free Strategy
Authors: Pedro Ribeiro de Almeida, Vitor Hirata Sanches, Carla Goldman
Date: 2023-06-07 00:00:00
Tasks: LEMMA

The Covid-19 pandemic revealed the difficulties of vaccinating a population under the circumstances marked by urgency and limited availability of doses while balancing benefits associated with distinct guidelines satisfying specific ethical criteria (J.W. Wu, S.D. John, E.Y. Adashi, Allocating Vaccines in the Pandemic: The Ethical Dimension, The Am. J. of Medicine V.33(11): 1241 - 1242 (2020)). We offer a vaccination strategy that may be useful in this regard. It relies on the mathematical concept of envy-freeness. We consider finding balance by allocating the resource among individuals that seem to be heterogeneous concerning the direct and indirect benefits of vaccination, depending on age. The proposed strategy adapts a constructive approach in the literature based on Sperner`s Lemma to point out an approximate division of doses guaranteeing that both benefits are optimized each time a batch becomes available. Applications using data about population age distributions from diverse countries suggest that, among other features, this strategy maintains the desired balance throughout the entire vaccination period.
Bachelier's Market Model for ESG Asset Pricing
Authors: Svetlozar Rachev, Nancy Asare Nyarko, Blessing Omotade, Peter Yegon
Date: 2023-06-07 00:00:00
Tasks: model

Environmental, Social, and Governance (ESG) finance is a cornerstone of modern finance and investment, as it changes the classical return-risk view of investment by incorporating an additional dimension of investment performance: the ESG score of the investment. We define the ESG price process and integrate it into an extension of Bachelier's market model in both discrete and continuous time, enabling option pricing valuation.