pwc-archive/papers-with-abstracts huggingface dataset:
Estimation of Integrated Volatility Functionals with Kernel Spot Volatility Estimators
Authors: José E. Figueroa-López, Jincheng Pang, Bei Wu
Date: 2024-07-13 00:00:00

For a multidimensional It\^o semimartingale, we consider the problem of estimating integrated volatility functionals. Jacod and Rosenbaum (2013) studied a plug-in type of estimator based on a Riemann sum approximation of the integrated functional and a spot volatility estimator with a forward uniform kernel. Motivated by recent results that show that spot volatility estimators with general two-side kernels of unbounded support are more accurate, in this paper, an estimator using a general kernel spot volatility estimator as the plug-in is considered. A biased central limit theorem for estimating the integrated functional is established with an optimal convergence rate. Unbiased central limit theorems for estimators with proper de-biasing terms are also obtained both at the optimal convergence regime for the bandwidth and when applying undersmoothing. Our results show that one can significantly reduce the estimator's bias by adopting a general kernel instead of the standard uniform kernel. Our proposed bias-corrected estimators are found to maintain remarkable robustness against bandwidth selection in a variety of sampling frequencies and functions.
Regularizing stock return covariance matrices via multiple testing of correlations
Authors: Richard Luger
Date: 2024-07-12 00:00:00
Tasks: Portfolio Optimization

This paper develops a large-scale inference approach for the regularization of stock return covariance matrices. The framework allows for the presence of heavy tails and multivariate GARCH-type effects of unknown form among the stock returns. The approach involves simultaneous testing of all pairwise correlations, followed by setting non-statistically significant elements to zero. This adaptive thresholding is achieved through sign-based Monte Carlo resampling within multiple testing procedures, controlling either the traditional familywise error rate, a generalized familywise error rate, or the false discovery proportion. Subsequent shrinkage ensures that the final covariance matrix estimate is positive definite and well-conditioned while preserving the achieved sparsity. Compared to alternative estimators, this new regularization method demonstrates strong performance in simulation experiments and real portfolio optimization.
An Introduction to Causal Discovery
Authors: Martin Huber
Date: 2024-07-11 00:00:00
Tasks: Causal Discovery, Causal Inference, Survey

In social sciences and economics, causal inference traditionally focuses on assessing the impact of predefined treatments (or interventions) on predefined outcomes, such as the effect of education programs on earnings. Causal discovery, in contrast, aims to uncover causal relationships among multiple variables in a data-driven manner, by investigating statistical associations rather than relying on predefined causal structures. This approach, more common in computer science, seeks to understand causality in an entire system of variables, which can be visualized by causal graphs. This survey provides an introduction to key concepts, algorithms, and applications of causal discovery from the perspectives of economics and social sciences. It covers fundamental concepts like d-separation, causal faithfulness, and Markov equivalence, sketches various algorithms for causal discovery, and discusses the back-door and front-door criteria for identifying causal effects. The survey concludes with more specific examples of causal discovery, e.g. for learning all variables that directly affect an outcome of interest and/or testing identification of causal effects in observational data.
Comparative analysis of Mixed-Data Sampling (MIDAS) model compared to Lag-Llama model for inflation nowcasting
Authors: Adam Bahelka, Harmen de Weerd
Date: 2024-07-11 00:00:00
Tasks: model, regression, Time Series, Time Series Forecasting

Inflation is one of the most important economic indicators closely watched by both public institutions and private agents. This study compares the performance of a traditional econometric model, Mixed Data Sampling regression, with one of the newest developments from the field of Artificial Intelligence, a foundational time series forecasting model based on a Long short-term memory neural network called Lag-Llama, in their ability to nowcast the Harmonized Index of Consumer Prices in the Euro area. Two models were compared and assessed whether the Lag-Llama can outperform the MIDAS regression, ensuring that the MIDAS regression is evaluated under the best-case scenario using a dataset spanning from 2010 to 2022. The following metrics were used to evaluate the models: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), correlation with the target, R-squared and adjusted R-squared. The results show better performance of the pre-trained Lag-Llama across all metrics.
Production function estimation using subjective expectations data
Authors: Agnes Norris Keiller, Aureo de Paula, John Van Reenen
Date: 2024-07-10 00:00:00

Standard methods for estimating production functions in the Olley and Pakes (1996) tradition require assumptions on input choices. We introduce a new method that exploits (increasingly available) data on a firm's expectations of its future output and inputs that allows us to obtain consistent production function parameter estimates while relaxing these input demand assumptions. In contrast to dynamic panel methods, our proposed estimator can be implemented on very short panels (including a single cross-section), and Monte Carlo simulations show it outperforms alternative estimators when firms' material input choices are subject to optimization error. Implementing a range of production function estimators on UK data, we find our proposed estimator yields results that are either similar to or more credible than commonly-used alternatives. These differences are larger in industries where material inputs appear harder to optimize. We show that TFP implied by our proposed estimator is more strongly associated with future jobs growth than existing methods, suggesting that failing to adequately account for input endogeneity may underestimate the degree of dynamic reallocation in the economy.
Reduced-Rank Matrix Autoregressive Models: A Medium $N$ Approach
Authors: Alain Hecq, Ivan Ricardo, Ines Wilms
Date: 2024-07-10 00:00:00
Tasks: Time Series

Reduced-rank regressions are powerful tools used to identify co-movements within economic time series. However, this task becomes challenging when we observe matrix-valued time series, where each dimension may have a different co-movement structure. We propose reduced-rank regressions with a tensor structure for the coefficient matrix to provide new insights into co-movements within and between the dimensions of matrix-valued time series. Moreover, we relate the co-movement structures to two commonly used reduced-rank models, namely the serial correlation common feature and the index model. Two empirical applications involving U.S.\ states and economic indicators for the Eurozone and North American countries illustrate how our new tools identify co-movements.
A Short Note on Event-Study Synthetic Difference-in-Differences Estimators
Authors: Diego Ciccia
Date: 2024-07-05 00:00:00

I propose an event study extension of Synthetic Difference-in-Differences (SDID) estimators. I show that, in simple and staggered adoption designs, estimators from Arkhangelsky et al. (2021) can be disaggregated into dynamic treatment effect estimators, comparing the lagged outcome differentials of treated and synthetic controls to their pre-treatment average. Estimators presented in this note can be computed using the sdid_event Stata package.
Online Distributional Regression
Authors: Simon Hirsch, Jonathan Berrisch, Florian Ziel
Date: 2024-06-26 00:00:00
Tasks: Management, regression

Large-scale streaming data are common in modern machine learning applications and have led to the development of online learning algorithms. Many fields, such as supply chain management, weather and meteorology, energy markets, and finance, have pivoted towards using probabilistic forecasts, which yields the need not only for accurate learning of the expected value but also for learning the conditional heteroskedasticity and conditional distribution moments. Against this backdrop, we present a methodology for online estimation of regularized, linear distributional models. The proposed algorithm is based on a combination of recent developments for the online estimation of LASSO models and the well-known GAMLSS framework. We provide a case study on day-ahead electricity price forecasting, in which we show the competitive performance of the incremental estimation combined with strongly reduced computational effort. Our algorithms are implemented in a computationally efficient Python package.
Population Concentration in High-Complexity Regions within City during the heat wave
Authors: Hyoji Choi, Jonghyun Kim, Donghyeon Yu, Bogang Jun
Date: 2024-07-13 00:00:00

This study investigates the impact of the 2018 summer heat wave on urban mobility in Seoul and the role of economic complexity in the region's resilience. Findings from subway and mobile phone data indicate a significant decrease in the floating population during extreme heat wave, underscoring the thermal vulnerability of urban areas. However, urban regions with higher complexity demonstrate resilience, attracting more visitors despite high temperatures. Our results suggest the centrality of economic complexity in urban resilience against climate-induced stressors. Additionally, it implies that high-complexity small businesses' clusters can serve as focal points for sustaining urban vitality in the face of thermal shocks within city. In the long run perspective, our results imply the possibility that people are more concentrated in high complexity region in the era of global warming.
Monopoly Unveiled: Telecom Breakups in the US and Mexico
Authors: Fausto Hernández Trillo, C. Vladimir Rodríguez-Caballero, Daniel Ventosa-Santaulària
Date: 2024-07-12 00:00:00
Tasks: Time Series

This paper posits the decline in market capitalization following a monopoly breakup serves as a means to gauge how financial markets assess market power. Our research, which employs univariate structural time series models to estimate the firm's value without the breakup and juxtapose it with actual post-divestiture values, reveals a staggering drop in AT&T's value by 65% and AMX's by 32% from their pre-breakup levels. These findings underscore the contemporary valuation of monopoly rents as perceived by financial markets, highlighting the significant impact of monopoly breakup on market capitalization and the need for a deeper understanding of these dynamics.
Cultural Transmission, Property Rights, and Treatment of the Elderly
Authors: Matthew J. Baker, Joyce P. Jacobsen
Date: 2024-07-12 00:00:00

We examine how production and the development of property rights interact with cultural transmission to shape the treatment of the elderly across societies. Our model posits that respect for the elderly arises endogenously: parents invest in cultivating cultural values in their children, who later reciprocate in proportion to this investment. We show that this model is functionally equivalent to one in which cultural goods are transferred by the elderly. We focus on the distinct roles of property rights, finding that while insecure output rights may promote elderly welfare, secure rights over productive resources can have comparable benefits. The model reveals a nonlinear relationship between cultural sophistication, property rights, and economic factors such as the capital and land intensity of production, driving variations in elderly well-being across societies. Finally, we consider how the model suggests demographic, technological, and policy changes influence elderly well-being across the spectrum of development.
Modelling shock propagation and resilience in financial temporal networks
Authors: Fabrizio Lillo, Giorgio Rizzini
Date: 2024-07-12 00:00:00

Modelling how a shock propagates in a temporal network and how the system relaxes back to equilibrium is challenging but important in many applications, such as financial systemic risk. Most studies so far have focused on shocks hitting a link of the network, while often it is the node and its propensity to be connected that are affected by a shock. Using as starting point the configuration model, a specific Exponential Random Graph model, we propose a vector autoregressive (VAR) framework to analytically compute the Impulse Response Function (IRF) of a network metric conditional to a shock on a node. Unlike the standard VAR, the model is a nonlinear function of the shock size and the IRF depends on the state of the network at the shock time. We propose a novel econometric estimation method that combines the Maximum Likelihood Estimation and Kalman filter to estimate the dynamics of the latent parameters and compute the IRF, and we apply the proposed methodology to the dynamical network describing the electronic Market of Interbank Deposit (e-MID).
Valuation of travel time savings in the presence of simultaneous activities
Authors: Jacek Pawlak, John Polak
Date: 2024-07-11 00:00:00

Time sharing between activities remains an indispensable part of everyday activity pattern. However, the issue has not yet been fully acknowledged within the existing time allocation models, potentially resulting in inaccuracies in valuing travel time savings. Therefore this study is aimed at addressing this gap by investigating the potential impact of introducing time sharing within such a framework, as well as factors determining it as represented by travel activities. In doing so, time constraint in the time allocation model of Small was modified to enable sharing the same time interval between different activities. The resulting expression indicated that such an augmentation could lead to lower estimates of value of time as a resource. On the other hand, empirical research based on the data from the National Passenger Survey 2004 used for calibrating cross-nested logit model indicated a number of factors affecting the choice of travel activities. It was discovered that significant include possession of equipment allowing particular activities, e.g. newspaper, paperwork or ICT devices, companionship, gender, length of the journey, frequency of using the service, possibility of working on the train, journey planning in advance, first class travel, termination of the trip in central London, peak-time travel and availability of seating.
Herding Unmasked: Insights into Cryptocurrencies, Stocks and US ETFs
Authors: An Pham Ngoc Nguyen, Martin Crane, Thomas Conlon, Marija Bezbradica
Date: 2024-07-10 00:00:00
Tasks: Community Detection

Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.
Incentivizing Agents through Ratings
Authors: Peiran Xiao
Date: 2024-07-15 00:00:00

I study the optimal design of ratings to motivate agent investment in quality when transfers are unavailable. The principal designs a rating scheme that maps the agent's quality to a (possibly stochastic) score. The agent has private information about his ability, which determines his cost of investment, and chooses the quality level. The market observes the score and offers a wage equal to the agent's expected quality. For example, a school incentivizes learning through a grading policy that discloses the student's quality to the job market. I reduce the principal's problem to the design of an interim wage function of quality. When restricted to deterministic ratings, I provide necessary and sufficient conditions for the optimality of simple pass/fail tests and lower censorship. In particular, when the principal's objective is expected quality, pass/fail tests are optimal if agents' abilities are concentrated towards the top of the distribution, while pass/lower censorship is optimal if abilities are concentrated towards the mode. The results generalize existing results in optimal delegation with voluntary participation, as pass/fail tests (lower censorship) correspond to take-it-or-leave-it offers (threshold delegation). Additionally, I provide sufficient conditions for deterministic ratings to remain optimal when stochastic ratings are allowed. For quality maximization, pass/fail tests remain optimal if the ability distribution becomes increasingly more concentrated towards the top.
The feasibility of sound zone control using an array of parametric array loudspeakers
Authors: Tao Zhuang, Jia-Xin Zhong, Jing Lu
Date: 2024-07-14 00:00:00

Parametric array loudspeakers (PALs) are known for producing highly directional audio beams, a feat more challenging to achieve with conventional electro-dynamic loudspeakers (EDLs). Due to their intrinsic physical mechanisms, PALs hold promising potential for spatial audio applications such as virtual reality (VR). However, the feasibility of using an array of PALs for sound zone control (SZC) has remained unexplored, mainly due to the complexity of the nonlinear demodulation process inherent in PALs. Leveraging recent advancements in PAL modeling, this work proposes an optimization algorithm to achieve the acoustic contrast control (ACC) between two target areas using a PAL array. The performance and robustness of the proposed ACC-based SZC using PAL arrays are investigated through simulations, and the results are compared with those obtained using EDL arrays. The results show that the PAL array outperforms the EDL array in SZC performance and robustness at higher frequencies and lower signal-to-noise ratio, while being comparable under other conditions. This work paves the way for high-contrast acoustic control using PAL arrays.
Squeeze-and-Excite ResNet-Conformers for Sound Event Localization, Detection, and Distance Estimation for DCASE 2024 Challenge
Authors: Jun Wei Yeow, Ee-Leng Tan, Jisheng Bai, Santi Peksi, Woon-Seng Gan
Date: 2024-07-12 00:00:00
Tasks: Sound Event Localization and Detection

This technical report details our systems submitted for Task 3 of the DCASE 2024 Challenge: Audio and Audiovisual Sound Event Localization and Detection (SELD) with Source Distance Estimation (SDE). We address only the audio-only SELD with SDE (SELDDE) task in this report. We propose to improve the existing ResNet-Conformer architectures with Squeeze-and-Excitation blocks in order to introduce additional forms of channel- and spatial-wise attention. In order to improve SELD performance, we also utilize the Spatial Cue-Augmented Log-Spectrogram (SALSA) features over the commonly used log-mel spectra features for polyphonic SELD. We complement the existing Sony-TAu Realistic Spatial Soundscapes 2023 (STARSS23) dataset with the audio channel swapping technique and synthesize additional data using the SpatialScaper generator. We also perform distance scaling in order to prevent large distance errors from contributing more towards the loss function. Finally, we evaluate our approach on the evaluation subset of the STARSS23 dataset.
DUCPS: Deep Unfolding the Cauchy Proximal Splitting Algorithm for B-Lines Quantification in Lung Ultrasound Images
Authors: Tianqi Yang, Oktay Karakuş, Nantheera Anantrasirichai, Marco Allinovi, Alin Achim
Date: 2024-07-15 00:00:00
Tasks: Computational Efficiency, Line Detection, SSIM

The identification of artefacts, particularly B-lines, in lung ultrasound (LUS), is crucial for assisting clinical diagnosis, prompting the development of innovative methodologies. While the Cauchy proximal splitting (CPS) algorithm has demonstrated effective performance in B-line detection, the process is slow and has limited generalization. This paper addresses these issues with a novel unsupervised deep unfolding network structure (DUCPS). The framework utilizes deep unfolding procedures to merge traditional model-based techniques with deep learning approaches. By unfolding the CPS algorithm into a deep network, DUCPS enables the parameters in the optimization algorithm to be learnable, thus enhancing generalization performance and facilitating rapid convergence. We conducted entirely unsupervised training using the Neighbor2Neighbor (N2N) and the Structural Similarity Index Measure (SSIM) losses. When combined with an improved line identification method proposed in this paper, state-of-the-art performance is achieved, with the recall and F2 score reaching 0.70 and 0.64, respectively. Notably, DUCPS significantly improves computational efficiency eliminating the need for extensive data labeling, representing a notable advancement over both traditional algorithms and existing deep learning approaches.