|
on Computational Economics |
|
Issue of 2024‒01‒29
sixteen papers chosen by |
| By: | Baumgärtner, Martin; Zahner, Johannes |
| Abstract: | Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes embeddings - a language model trained using machine learning techniques - to locate words and documents in a multidimensional vector space. To accomplish this, we utilize a text corpus that is unparalleled in size and diversity in the central bank communication literature, as well as introduce a novel approach to text quantification from computational linguistics. This allows us to provide high-quality central bank-specific textual representations and demonstrate their applicability by developing an index that tracks deviations in the Fed's communication towards inflation targeting. Our findings indicate that these deviations in communication significantly impact monetary policy actions, substantially reducing the reaction towards inflation deviation in the US. |
| Keywords: | Word Embedding, Neural Network, Central Bank Communication, Natural Language Processing, Transfer Learning |
| JEL: | C45 C53 E52 Z13 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:imfswp:280939&r=cmp |
| By: | Mingxuan He |
| Abstract: | I study the price dynamics of non-fungible tokens (NFTs) and propose a deep learning framework for dynamic valuation of NFTs. I use data from the Ethereum blockchain and OpenSea to train a deep learning model on historical trades, market trends, and traits/rarity features of Bored Ape Yacht Club NFTs. After hyperparameter tuning, the model is able to predict the price of NFTs with high accuracy. I propose an application framework for this model using zero-knowledge machine learning (zkML) and discuss its potential use cases in the context of decentralized finance (DeFi) applications. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.05346&r=cmp |
| By: | Ashish Dhiman; Yibei Hu |
| Abstract: | We apply a physics-informed deep-learning approach the PINN approach to the Black-Scholes equation for pricing American and European options. We test our approach on both simulated as well as real market data, compare it to analytical/numerical benchmarks. Our model is able to accurately capture the price behaviour on simulation data, while also exhibiting reasonable performance for market data. We also experiment with the architecture and learning process of our PINN model to provide more understanding of convergence and stability issues that impact performance. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.06711&r=cmp |
| By: | Gadat, Sébastien; Doury, Antoine; Somot, Samuel |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:128959&r=cmp |
| By: | Daube, Carl Heinz |
| Abstract: | The aim of this working paper is to provide a brief introduction to artificial intelligence and highlight specific potential applications in financial and investment decision-making. On the one hand, it is about where AI is already being used today in many areas of the financial industry. On the other hand, the aim is to show examples of what will be possible in the near future and where AI might lead to better, more sound decisions |
| Abstract: | Ziel dieses Working Papers ist es, eine kurze Einführung in die Künstliche Intelligenz zu geben und konkrete Einsatzmöglichkeiten in der Finanz- und Investitionsentscheidung aufzuzeigen. Dabei geht es zum einen darum, wo KI heute schon in vielen Bereichen der Finanzindustrie zum Einsatz kommt. Zum anderen geht es darum exemplarisch aufzuzeigen, was in naher Zukunft möglich sein wird und wo es auf der Basis von KI zu besseren, fundierteren Entscheidungen kommen könnte. |
| Keywords: | AI, Artificial Intelligence, investment decision, finance decision |
| JEL: | G00 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:280899&r=cmp |
| By: | Gemma Wright; Katrin Gasior; Joonas Ollonqvist; Wynnona Steyn; Winile Ngobeni; Helen Barnes; Michael Noble; David McLennan; Jukka Pirttilä; Ada Jansen |
| Abstract: | In this paper we explore options for augmenting South Africa's personal income tax revenue using two microsimulation models: PITMOD simulates the personal income tax system and is underpinned by a dataset comprising a full extract of anonymized individual-level administrative tax data; and SAMOD simulates personal income tax and social benefits using a nationally representative survey. We explore policy reforms at both the upper and lower ends of the income distribution of tax-registered individuals and assess the impacts on revenue and measures of progressivity. |
| Keywords: | Microsimulation, Personal income tax, Income distribution, South Africa |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp-2023-147&r=cmp |
| By: | Bavaro, Michele; Boscolo, Stefano; Tedeschi, Simone |
| Abstract: | This paper utilises the Italian Treasury Dynamic Microsimulation Model (T-DYMM) to project individual and household economic trends up to 2070, focusing on the intergenerational transmission of wealth inequality. To analyse the impact of intergenerational transfers (IGTs) on wealth inequality, various scenarios are compared to a baseline. Results suggest that net wealth inequality will remain stable until 2040, when it is expected to rise progressively, especially due to the rising role of IGTs. Demographic factors like increased life expectancy and declining fertility are the main explanations for this phenomenon. Finally, while some assumptions, like accounting for no behavioural adjustments in response to tax changes, have limitations, this study provides valuable insights into potential effects and timelines for inheritance tax reforms on long-term inequality transmission. |
| Keywords: | intergenerational transfers, inheritance, taxation, wealth inequality, capital income, dynamic microsimulation. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:amz:wpaper:2024-01&r=cmp |
| By: | Morgan Blangeois (CleRMa - Clermont Recherche Management - ESC Clermont-Ferrand - École Supérieure de Commerce (ESC) - Clermont-Ferrand - UCA - Université Clermont Auvergne) |
| Abstract: | This article delves into the revolution of generative artificial intelligence (AI) in digital service companies (DSCs), focusing on foundational models like GPT-4. It examines the debates surrounding these technologies, particularly their implications in terms of misinformation through hallucinations, the social biases they carry, and their influence on the digital transformation of organizations. It begins with an exploration of the emergence of generative AI, highlighting technological advances and their practical impacts. The article assesses the current capabilities of generative AI and its potential, highlighting its role in redefining the job market, especially in terms of skills. The core of the study focuses on the challenges and strategic opportunities for DSCs. Examining how generative AI transforms traditional functions and creates new strategies, the article underscores the importance for DSCs to reconsider their position in the IT value chain. We explore how the adoption of open-innovation and business model innovations can facilitate the adaptation of DSCs to these challenges and conclude with a call for empirical studies to further explore these themes. |
| Abstract: | Cet article se penche sur la révolution de l'intelligence artificielle (IA) générative dans les entreprises de services du numérique (ESN), en mettant l'accent sur les modèles de fondation comme GPT-4. Il examine les débats entourant ces technologies, notamment leurs implications en termes de désinformation par les hallucinations, les biais sociaux qu'ils véhiculent, et leur influence sur la transformation numérique des organisations. Il débute par une exploration de l'émergence de l'IA générative, soulignant les avancées technologiques et leurs impacts pratiques. L'article évalue les capacités actuelles de l'IA générative et son potentiel, en mettant en évidence son rôle dans la redéfinition du marché du travail, notamment sur le plan des compétences. Le cœur de l'étude se concentre sur les défis et les opportunités stratégiques pour les ESN. En examinant la manière dont l'IA générative transforme les fonctions traditionnelles et crée de nouvelles stratégies, l'article souligne l'importance pour les ESN de reconsidérer leur position dans la chaîne de valeur informatique. Nous explorons comment l'adoption de l'open-innovation et les innovations des modèles d'affaires peuvent favoriser l'adaptation des ESN à ces enjeux et faisons appel en conclusion à des études empiriques pour approfondir ces thématiques. |
| Keywords: | Business Model, Digital Services Companies, Open Innovation, Artificial Intelligence, Strategy, Modèles d'affaires, Entreprises des Services du Numérique, Innovation Ouverte, Intelligence Artificielle, Stratégie |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04355219&r=cmp |
| By: | Daube, Carl Heinz |
| Abstract: | The aim of this working paper is to provide a brief introduction to artificial intelligence and highlight specific potential applications in financial and investment decision-making. On the one hand, it is about where AI is already being used today in many areas of the financial industry. On the other hand, the aim is to show examples of what will be possible in the near future and where AI might lead to better, more sound decisions. |
| Abstract: | Ziel dieses Working Papers ist es, eine kurze Einführung in die Künstliche Intelligenz zu geben und konkrete Einsatzmöglichkeiten in der Finanz- und Investitionsentscheidung aufzuzeigen. Dabei geht es zum einen darum, wo KI heute schon in vielen Bereichen der Finanzindustrie zum Einsatz kommt. Zum anderen geht es darum exemplarisch aufzuzeigen, was in naher Zukunft möglich sein wird und wo es auf der Basis von KI zu besseren, fundierteren Entscheidungen kommen könnte. |
| Keywords: | Künstliche Intelligenz, Anwendung KI, Finanzentscheidung, Investitionsentscheidung |
| JEL: | G00 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:280891&r=cmp |
| By: | Dingquan Miao; Ravi Kanbur; Jukka Pirttilä |
| Abstract: | The COVID-19 pandemic increased public debt and changed the income distribution in many countries. We use a numerical simulation approach to derive optimal nonlinear marginal tax rates for the pre-crisis and crisis periods. We contribute to the literature by examining optimal tax rates numerically for a developing country and by investigating how the tax rates should be changed as a response to a crisis. Our results indicate that the actual extent of redistribution, especially via direct transfers to low-income individuals, should be considerably higher than what the present system offers. |
| Keywords: | COVID-19, Pandemic, Optimal tax, Income tax, Simulation, Welfare impact |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp-2023-149&r=cmp |
| By: | Jackson, Emerson Abraham |
| Abstract: | This theoretical review explores the evolution of artificial intelligence (AI) and its impact on teaching and learning in the digital age. Investigating AI's integration into educational settings, the paper synthesises theoretical frameworks, empirical studies, and emerging trends. Drawing on constructivist, socio-cultural, and cognitive learning theories, the review analyses AI's implications for educational practices. It traces the historical development of AI in education, highlighting key milestones and the evolution of AI technologies. The paper adopts a theoretical framework to comprehensively analyse AI's impact, focusing on intelligent tutoring systems, adaptive learning platforms, virtual reality, natural language processing, and gamification. Theoretical foundations underscore AI's role in active learning, personalised environments, social interaction, and cognitive load management. The review addresses challenges, including equity, ethical considerations, and the evolving role of educators. It emphasises the need for clear ethical guidelines, professional development for educators, and ongoing research to navigate the evolving landscape of AI in education. Theoretical implications suggest a nuanced synthesis of technology and pedagogy, acknowledging the dynamic interplay between the two, and call for continued research to address technical challenges, ethical considerations, and effective strategies for professional development in this dynamic intersection of technology and education. |
| Abstract: | Cette revue théorique explore l'évolution de l'intelligence artificielle (IA) et son impact sur l'enseignement et l'apprentissage à l'ère numérique. En examinant l'intégration de l'IA dans les environnements éducatifs, l'article synthétise des cadres théoriques, des études empiriques et des tendances émergentes. S'appuyant sur les théories constructivistes, socio-culturelles et d'apprentissage cognitif, la revue analyse les implications de l'IA pour les pratiques éducatives. Elle retrace le développement historique de l'IA dans l'éducation, mettant en évidence des jalons clés et l'évolution des technologies d'IA. L'article adopte un cadre théorique pour analyser de manière exhaustive l'impact de l'IA, en se concentrant sur les systèmes de tutorat intelligents, les plateformes d'apprentissage adaptatif, la réalité virtuelle, le traitement du langage naturel et la ludification. Les fondements théoriques soulignent le rôle de l'IA dans l'apprentissage actif, les environnements personnalisés, l'interaction sociale et la gestion de la charge cognitive. La revue aborde des défis tels que l'équité, les considérations éthiques et le rôle en évolution des éducateurs. Elle souligne la nécessité de lignes directrices éthiques claires, de développement professionnel pour les éducateurs et de recherches continues pour naviguer dans le paysage en évolution de l'IA dans l'éducation. Les implications théoriques suggèrent une synthèse nuancée de la technologie et de la pédagogie, reconnaissant l'interaction dynamique entre les deux, et appellent à des recherches continues pour relever les défis techniques, les considérations éthiques et les stratégies efficaces de développement professionnel dans cette intersection dynamique de la technologie et de l'éducation. |
| Keywords: | Artificial Intelligence, Theoretical Review, Teaching and Learning, Digital Age |
| JEL: | A22 I21 O33 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:280893&r=cmp |
| By: | Jalal Etesami; Ali Habibnia; Negar Kiyavash |
| Abstract: | We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the limitations of traditional econometric models in capturing high-dimensional, nonlinear, and time-varying interconnections among series. This framework employs an information-theoretic measure rooted in a generalized version of Granger-causality, which is applicable to both linear and nonlinear dynamics. Our framework offers advancements in measuring systemic risk and establishes meaningful connections with established econometric models, including vector autoregression and switching models. We evaluate the efficacy of our proposed model through simulation experiments and empirical analysis, reporting promising results in recovering simulated time-varying networks with nonlinear and multivariate structures. We apply this framework to identify and monitor the evolution of interconnectedness and systemic risk among major assets and industrial sectors within the financial network. We focus on cryptocurrencies' potential systemic risks to financial stability, including spillover effects on other sectors during crises like the COVID-19 pandemic and the Federal Reserve's 2020 emergency response. Our findings reveals significant, previously underrecognized pre-2020 influences of cryptocurrencies on certain financial sectors, highlighting their potential systemic risks and offering a systematic approach in tracking evolving cross-sector interactions within financial networks. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.16707&r=cmp |
| By: | Keith Head (UBC - University of British Columbia, CEPR - Center for Economic Policy Research - CEPR); Thierry Mayer (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, CEPII - Centre d'études prospectives et d'informations internationales, CEPR - Center for Economic Policy Research - CEPR) |
| Abstract: | Constant elasticity of substitution (CES) demand for monopolistically competitive firm-varieties is a standard tool for models in international trade and macroeconomics. Inter-variety substitution in this model follows a simple share proportionality rule. In contrast, the standard toolkit in industrial organization (IO) estimates a demand system in which cross-elasticities depend on similarity in observable attributes. The gain in realism from the IO approach comes at the expense of requiring richer data and greater computational challenges. This paper uses the data generating process of Berry et al. (1995), BLP, who established the modern IO method, to simulate counterfactual trade policy experiments. We use the CES model as an approximation of the more complex underlying demand system and market structure. Although the CES model omits key elements of the data generating process, the errors are offsetting, allowing it to fit BLP-based predictions closely. For aggregate outcomes, it turns out that incorporating non-unitary pass-through matters more than fixing oversimplified substitution patterns. |
| Keywords: | Constant Elasticity of Substitution, Industrial Organization, Oligopoly, Trade, Tariffs, Counterfactual analysis |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04347301&r=cmp |
| By: | Peter Christensen |
| Abstract: | Inspired by the activity signature introduced by Todorov and Tauchen (2010), which was used to measure the activity of a semimartingale, this paper introduces the roughness signature function. The paper illustrates how it can be used to determine whether a discretely observed process is generated by a continuous process that is rougher than a Brownian motion, a pure-jump process, or a combination of the two. Further, if a continuous rough process is present, the function gives an estimate of the roughness index. This is done through an extensive simulation study, where we find that the roughness signature function works as expected on rough processes. We further derive some asymptotic properties of this new signature function. The function is applied empirically to three different volatility measures for the S&P500 index. The three measures are realized volatility, the VIX, and the option-extracted volatility estimator of Todorov (2019). The realized volatility and option-extracted volatility show signs of roughness, with the option-extracted volatility appearing smoother than the realized volatility, while the VIX appears to be driven by a continuous martingale with jumps. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.02819&r=cmp |
| By: | Hu, Bo; Cartagena-Farias, Javiera; Brimblecombe, Nicola; Jadoolal, Shari; Wittenberg, Raphael |
| Abstract: | Background Health economics research and economic evaluation have increasingly taken a societal perspective, accounting for the economic impacts of informal care. Projected economic costs of informal care help researchers and policymakers understand better the long-term consequences of policy reforms and health interventions. This study makes projections of the economic costs of informal care for older people in England. Methods Data come from two national surveys: the English Longitudinal Study of Ageing (ELSA, N = 35, 425) and the Health Survey for England (N = 17, 292). We combine a Markov model with a macrosimulation model to make the projections. We explore a range of assumptions about future demographic and epidemiological trends to capture model uncertainty and take a Bayesian approach to capture parameter uncertainty. Results We estimate that the economic costs of informal care were £54.2 billion in 2019, three times larger than the expenditure on formal long-term care. Those costs are projected to rise by 87% by 2039, faster than public expenditure but slower than private expenditure on formal long-term care. These results are sensitive to assumptions about future life expectancy, fertility rates, and progression of disabilities in the population. Conclusions Prevention schemes aiming to promote healthy aging and independence will be important to alleviate the costs of informal care. The government should strengthen support for informal caregivers and care recipients to ensure the adequacy of care, protect the well-being of caregivers, and prevent the costs of informal care from spilling over to other sectors of the economy. |
| Keywords: | informal care costs; economic valuation; functional disabilities; long-term care projections; England; This study; as part of the Care and Place (CAPE) project; was supported by the School for Social Care Research (SSCR) and the National Institute for Health Research (NIHR). |
| JEL: | I11 J11 E26 E27 |
| Date: | 2023–12–12 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:121157&r=cmp |
| By: | Sudharsan Sundar; Eric Gao; Trevor Chow; Matthew Ding |
| Abstract: | It is well known that Random Serial Dictatorship is strategy-proof and leads to a Pareto-Efficient outcome. We show that this result breaks down when individuals are allowed to make transfers, and adapt Random Serial Dictatorship to encompass trades between individuals. Strategic analysis of play under the new mechanisms we define is given, accompanied by simulations to quantify the gains from trade. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.07999&r=cmp |