nep-cmp New Economics Papers
on Computational Economics
Issue of 2021‒01‒18
27 papers chosen by

  1. COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction By Farnoush Ronaghi; Mohammad Salimibeni; Farnoosh Naderkhani; Arash Mohammadi
  2. Day-ahead electricity prices prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling By Wei Li; Denis Mike Becker
  3. Exact simulation of two-parameter Poisson-Dirichlet random variables By Dassios, Angelos; Zhang, Junyi
  4. A General and Efficient Method for Solving Regime-Switching DSGE Models By Julien Albertini; Stéphane Moyen
  5. A macroeconomic evaluation of a carbon tax in overseas territories: A CGE model for Reunion Island By Sabine Garabedian; Avotra Narindranjanahary; Olivia Ricci; Sandrine Selosse
  6. Efficient Solution and Computation of Models With Occasionally Binding Constraints By Gregor Boehl
  7. Efficiency and risks in global value chains in the context of COVID-19 By Christine Arriola; Sophie Guilloux-Nefussi; Seung-Hee Koh; Przemyslaw Kowalski; Elena Rusticelli; Frank van Tongeren
  8. Computing Synthetic Controls Using Bilevel Optimization By Malo, Pekka; Eskelinen, Juha; Zhou, Xun; Kuosmanen, Timo
  9. Modeling asset allocation strategies and a new portfolio performance score By Apostolos Chalkis; Ioannis Z. Emiris
  10. Insurance valuation: A two-step generalised regression approach By Karim Barigou; Valeria Bignozzi; Andreas Tsanakas
  11. Fiscal DSGE Model for Latvia By Patrick Grüning; Ginters Buss
  12. The COVID-19 Resilience of a Continental Welfare Regime - Nowcasting the Distributional Impact of the Crisis By Denisa Sologon; Cathal O’Donoghue; Iryna Kyzyma; Jinjing Li; Jules Linden; Raymond Wagener
  13. Global Robust Bayesian Analysis in Large Models By Paul Ho
  14. Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network By Toru Kitagawa; Guanyi Wang
  15. Baseline Results from the EU28 EUROMOD: 2016-2019 By Kneeshaw, Jack; De Agostini, Paola; Chatsiou, Kakia; Gasior, Katrin; Jara Tamayo, Holguer Xavier; Leventi, Chrysa; Manios, Kostas; Paulus, Alari; Popova, Daria; Tasseva, Iva Valentinova
  16. A Machine-Learning History of English Caselaw and Legal Ideas Prior to the Industrial Revolution I: Generating and Interpreting the Estimates By Peter Grajzl; Peter Murrell
  17. Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model By Christoph Schlembach; Sascha L. Schmidt; Dominik Schreyer; Linus Wunderlich
  18. Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques By Alaeddine Mihoub; Hosni Snoun; Moez Krichen; Montassar Kahia; Riadh Bel Hadj Salah
  19. Daily tracker of global economic activity: a close-up of the COVID-19 pandemic By Diaz, Elena Maria; Pérez Quirós, Gabriel
  20. Text analysis in financial disclosures By Sridhar Ravula
  21. Stabilisation et relance macroéconomiques post COVID-19 dans la CEMAC : Quels instruments pour quels effets dans un modèle DSGE ? By Mvondo, Thierry
  22. Singapore; Financial Sector Assessment Program; Technical Note-Financial Stability Analysis and Stress Testing By International Monetary Fund
  23. Joint Taxation in Spain and its Effects on Social Welfare: a Microsimulation Analysis By Badenes-Plá, Nuria; Blanco Palmero, Patricia; Gambau-Suelves, Borja; Navas Román, María; Villazán Pellejero, Noemí
  24. Mechanismen der Veränderung personalpolitischer Konstellationen: Ergebnisse von Simulationsstudien By Martin, Albert
  25. Assessing Dutch Fiscal and Debt Sustainability By Benjamin Carton; Armand Fouejieu
  26. Frictional Spatial Equilibrium By Benoît Schmutz; Modibo Sidibé
  27. Living with Reduced Income: an Analysis of Household Financial Vulnerability Under COVID-19 By Midões, Catarina; Seré, Mateo

  1. By: Farnoush Ronaghi; Mohammad Salimibeni; Farnoosh Naderkhani; Arash Mohammadi
    Abstract: The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction (COVID19 PRIMO) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (COVID19-HPSMP), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion centre that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.
    Date: 2021–01
  2. By: Wei Li; Denis Mike Becker
    Abstract: The availability of accurate day-ahead electricity price forecasts is pivotal for electricity market participants. In the context of trade liberalisation and market harmonisation in the European markets, accurate price forecasting becomes even more difficult to obtain. The increasing power market integration has complicated the forecasting process, where electricity forecasting requires considering features from both the local market and ever-growing coupling markets. In this paper, we apply state-of-the-art deep learning models, combined with feature selection algorithms for electricity price prediction under the consideration of market coupling. We propose three hybrid architectures of long-short term memory (LSTM) deep neural networks and compare the prediction performance, in terms of various feature selections. In our empirical study, we construct a broad set of features from the Nord Pool market and its six coupling countries for forecasting the Nord Pool system price. The results show that feature selection is essential to achieving accurate prediction. Superior feature selection algorithms filter meaningful information, eliminate irrelevant information, and further improve the forecasting accuracy of LSTM-based deep neural networks. The proposed models obtain considerably accurate results.
    Date: 2021–01
  3. By: Dassios, Angelos; Zhang, Junyi
    Abstract: Consider a random vector (V1, . . . , Vn) where {Vk}k=1,...,n are the first n components of a two-parameter Poisson-Dirichlet distribution P D(α, θ). In this paper, we derive a decomposition for the components of the random vector, and propose an exact simulation algorithm to sample from the random vector. Moreover, a special case arises when θ/α is a positive integer, for which we present a very fast modified simulation algorithm using a compound geometric representation of the decomposition. Numerical examples are provided to illustrate the accuracy and effectiveness of our algorithms.
    Keywords: two-parameter Poisson-Dirichlet distribution; exact simulation; subordinator
    JEL: C1
    Date: 2020–12–17
  4. By: Julien Albertini (GATE Lyon Saint-Étienne - Groupe d'analyse et de théorie économique - CNRS - Centre National de la Recherche Scientifique - Université de Lyon - UJM - Université Jean Monnet [Saint-Étienne] - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UL2 - Université Lumière - Lyon 2 - ENS Lyon - École normale supérieure - Lyon); Stéphane Moyen (Deutsche Bundesbank - Deutsche Bundesbank)
    Abstract: This paper provides a general representation of endogenous and threshold-based regime switching models and develops an efficient numerical solution method. The regime-switching is triggered endogenously when some variables cross threshold conditions that can themselves be regime-dependent. We illustrate our approach using a RBC model with state-dependent government spending policies. It is shown that regime-switching models involve strong non linearities and discontinuities in the dynamics of the model. However, our numerical solution based on simulation and projection methods with regime-dependent policy rules is accurate, and fast enough, to efficiently take into all these challenging aspects. Several alternative specifications to the model and the method are studied.
    Keywords: Regime-switching,RBC model,simulation,accuracy
    Date: 2020
  5. By: Sabine Garabedian; Avotra Narindranjanahary; Olivia Ricci; Sandrine Selosse (CMA - Centre de Mathématiques Appliquées - MINES ParisTech - École nationale supérieure des mines de Paris - PSL - Université Paris sciences et lettres)
    Abstract: Reunion Island, similar to most insular regions, is ruled by a carbon-based economy that is heavily dependent on fossil fuels. In recent years, the energy transition towards a low-carbon economy has become the watchword of this French overseas region, with the objective of a 100% renewable energy mix by 2030. Reducing fossil fuel use while maintaining economic growth is an important issue for all countries but is even more important for island territories with structural and geographical handicaps. Energy transition and drastic greenhouse gas emission reductions represent costs and opportunities that need to be quantified. This research paper assesses the environmental and macroeconomic effects of the carbon price policy introduced in France to meet the target of the Paris Agreement. The acceptability of the tax significantly depends on the possibility of recycling tax revenues. Different schemes for recycling tax revenues are considered in simulations. The methodology used is a computable general equilibrium (CGE) model for Reunion Island (GetRun-NRJ) that takes into account all island specificities. The results show that the carbon tax enables substitutions between fossil and renewable energy production and reduces CO 2 emissions. However, the tax has negative effects on the aggregate economy. The implemented tax revenue recycling compensation mechanisms mitigate the negative impacts, but the results differ significantly, as the recycling schemes do not support the same economic actors.
    Date: 2020–12
  6. By: Gregor Boehl
    Abstract: Structural macroeconometric analysis and new HANK-type models with extremely high dimensionality require fast and robust methods to efficiently deal with occasionally binding constraints, especially since major developed economies have again hit the zero lower bound on nominal interest rates. This paper shows that a dynamic economic system with OBCs, depending on the expected duration of the constraint, can be represented in closed form. Combined with a set of simple equilibrium conditions, this can be exploited to avoid matrix inversions and simulations at runtime for significant gains in computational speed. An efficient implementation is provided in Python programming language. Benchmarking results show that for medium-scale models with an OBC more than 150,000 state space evaluations can be done per second. Even large HANK-type models with almost 1000 variables require only 0.1 ms per evaluation.
    Keywords: Occasionally Binding Constraints, Effective Lower Bound, Computational Methods
    Date: 2021–01
  7. By: Christine Arriola; Sophie Guilloux-Nefussi; Seung-Hee Koh; Przemyslaw Kowalski; Elena Rusticelli; Frank van Tongeren
    Abstract: The COVID-19 outbreak and the resulting disruptions in supply chains of some manufacturing and medical products have renewed the debate on costs and benefits of globalisation and, particularly, on risks associated with international fragmentation of production in global value chains (GVCs). While GVCs helped addressing supply shortages in several cases already during the early stages of the COVID-19 pandemic, much of the policy debate has concentrated on whether the gains from expanding international specialisation in GVCs are worth the associated risks of transmission of shocks and even whether governments should use policy tools to ‘re-localise’ GVCs. But re-localising may also mean less diversification and thereby limit the scope for cushioning shocks. This paper builds on on-going OECD analysis and aims at providing empirical evidence to inform and guide discussion on these questions. First, it reviews briefly the key issues and lessons learnt from the past, and identifies the main features of world trade and GVC participation that influence exposures to risks in supply chains. Subsequently, it presents key results of a set of economic model simulations conducted using the OECD’s computable general equilibrium (CGE) trade model METRO to shed light on the consequences of a stylised re-localisation policy scenario. In this scenario, countries are less exposed to foreign shocks, but they are also less efficient and less able to cushion shocks through trade. Quantitatively, the latter effect tends to dominate: re-localising GVCs would make the economy in most countries both less efficient and less stable. The economic case for policy-induced reshoring of GVCs is therefore weak. There is nevertheless scope for governments to join efforts with businesses to improve risk preparedness.
    Keywords: diversification, global value chains, relocalisation, shocks, trade
    JEL: F13 F23 F60
    Date: 2020–12–16
  8. By: Malo, Pekka; Eskelinen, Juha; Zhou, Xun; Kuosmanen, Timo
    Abstract: The synthetic control method (SCM) is a major innovation in the estimation of causal effects of policy interventions and programs in a comparative case study setting. In this paper, we demonstrate that the data-driven approach to SCM requires solving a bilevel optimization problem. We show how the SCM problem can be solved using iterative algorithms based on Tykhonov descent or KKT approximations.
    Keywords: Causal effects; Comparative case studies; Policy impact assessment
    JEL: C54 C63
    Date: 2020–11
  9. By: Apostolos Chalkis; Ioannis Z. Emiris
    Abstract: We discuss a powerful, geometric representation of financial portfolios and stock markets, which identifies the space of portfolios with the points lying in a simplex convex polytope. The ambient space has dimension equal to the number of stocks, or assets. Although our statistical tools are quite general, in this paper we focus on the problem of portfolio scoring. Our contribution is to introduce an original computational framework to model portfolio allocation strategies, which is of independent interest for computational finance. To model asset allocation strategies, we employ log-concave distributions centered on portfolio benchmarks. Our approach addresses the crucial question of evaluating portfolio management, and is relevant to the individual private investors as well as financial organizations. We evaluate portfolio performance, in a certain time period, by providing a new portfolio score, based on the aforementioned framework and concepts. In particular, it relies on the expected proportion of allocations that the portfolio outperforms when a certain set of strategies take place in that time period. We also discuss how this set of strategies -- and the knowledge one may have about them -- could vary in our framework, and we provide additional versions of our score in order to obtain a more complete picture of its performance. In all cases, we show that the score computations can be performed efficiently. Last but not least, we expect this framework to be useful in portfolio optimization and in automatically identifying extreme phenomena in a stock market.
    Date: 2020–12
  10. By: Karim Barigou (SAF); Valeria Bignozzi; Andreas Tsanakas
    Abstract: Current approaches to fair valuation in insurance often follow a two-step approach, combining quadratic hedging with application of a risk measure on the residual liability, to obtain a cost-of-capital margin. In such approaches, the preferences represented by the regulatory risk measure are not reflected in the hedging process. We address this issue by an alternative two-step hedging procedure, based on generalised regression arguments, which leads to portfolios that are neutral with respect to a risk measure, such as Value-at-Risk or the expectile. First, a portfolio of traded assets aimed at replicating the liability is determined by local quadratic hedging. Second, the residual liability is hedged using an alternative objective function. The risk margin is then defined as the cost of the capital required to hedge the residual liability. In the case quantile regression is used in the second step, yearly solvency constraints are naturally satisfied; furthermore, the portfolio is a risk minimiser among all hedging portfolios that satisfy such constraints. We present a neural network algorithm for the valuation and hedging of insurance liabilities based on a backward iterations scheme. The algorithm is fairly general and easily applicable, as it only requires simulated paths of risk drivers.
    Date: 2020–12
  11. By: Patrick Grüning (Bank of Lithuania & Vilnius University); Ginters Buss (Latvijas Banka)
    Abstract: We develop a fiscal dynamic stochastic general equilibrium (DSGE) model for policy simulation and scenario analysis purposes tailored to Latvia, a small open economy in a monetary union. The fiscal sector elements comprise government investment, government consumption, government transfers that are asymmetrically directed to both optimizing and hand-to-mouth households, cyclical unemployment benefits, foreign ownership of government debt, import content in public consumption and investment, and fiscal rules for each fiscal instrument. The model features a search-and-matching labour market friction with pro-cyclical labour costs, a financial accelerator mechanism, and import content in final goods. We estimate the model using Latvian data, study the new channels in the model, and provide a comprehensive analysis on the macroeconomic effects of the fiscal elements. A particular finding is that having foreign ownership of government debt generally breaks the Ricardian equivalence paradigm.
    Keywords: Small open economy, Fiscal policy, Fiscal rules, Bayesian estimation
    JEL: E0 E2 E3 F4 H2 H3 H6
    Date: 2020–12–14
  12. By: Denisa Sologon; Cathal O’Donoghue; Iryna Kyzyma; Jinjing Li; Jules Linden; Raymond Wagener
    Abstract: We evaluate the Covid-19 resilience of a Continental welfare regime by nowcasting the implications of the shock and its associated policy responses on the distribution of household incomes. Our approach relies on a dynamic microsimulation approach that combines a household income generation model estimated on the latest EU-SILC wave with novel nowcasting techniques to calibrate the simulations using external macro controls reflecting the macroeconomic climate during the crisis. We focus on Luxembourg, a country that introduced minor tweaks to the existing tax-benefit system which already contained instruments with a strong social insurance focus that gave certainty during the crisis. The income-support policy changes were effective in cushioning household incomes and mitigating an increase in income inequality in the early stages of the pandemic. The share of labour incomes dropped, but was compensated by an increase in benefits, reflecting the cushioning effect of the transfer system. Overall market incomes dropped and became more unequal. Their disequalizing evolution was, however, overpowered by an increase in tax-benefit redistribution. Net redistribution increased, driven by an increase in the generosity of benefits and larger access to benefits. These changes are mainly explained by the labour market shock, signalling the automatic stabilizers embedded in the pre-COVID system. The system was well-equipped ahead of the crisis to cushion household incomes against job losses. The methodology is scalable to other countries and well-designed to explore the impact of later stages in the COVID crisis, both economy-wide and sector-specific. The model is a real-time analysis and decision support tool to monitor the recovery, with high applicability for policymakers.
    Keywords: COVID-19; nowcasting; microsimulation; income inequality; tax-benefit policy
    JEL: D31 H23 J21 J22 J31
    Date: 2020–01
  13. By: Paul Ho
    Abstract: This paper develops a tool for global prior sensitivity analysis in large Bayesian models. Without imposing parametric restrictions, the methodology provides bounds for posterior means or quantiles given any prior close to the original in relative entropy, and reveals features of the prior that are important for the posterior statistics of interest. The author develops a sequential Monte Carlo algorithm and uses approximations to the likelihood and statistic of interest to implement the calculations. Applying the methodology to the error bands for the impulse response of output to a monetary policy shock in the New Keynesian model of Smets and Wouters (2007), the author shows that the upper bound of the error bands is very sensitive to the prior but the lower bound is not, with the prior on wage rigidity playing a particularly important role.
    Keywords: Bayesian models; Monte Carlo algorithm; New Keynesian model
    Date: 2020–06–30
  14. By: Toru Kitagawa; Guanyi Wang
    Abstract: How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times. This paper develops a procedure to estimate an individualized vaccine allocation policy under limited supply, exploiting social network data containing individual demographic characteristics and health status. We model spillover effects of the vaccines based on a Heterogeneous-Interacted-SIR network model and estimate an individualized vaccine allocation policy by maximizing an estimated social welfare (public health) criterion incorporating the spillovers. While this optimization problem is generally an NP-hard integer optimization problem, we show that the SIR structure leads to a submodular objective function, and provide a computationally attractive greedy algorithm for approximating a solution that has theoretical performance guarantee. Moreover, we characterise a finite sample welfare regret bound and examine how its uniform convergence rate depends on the complexity and riskiness of social network. In the simulation, we illustrate the importance of considering spillovers by comparing our method with targeting without network information.
    Date: 2020–12
  15. By: Kneeshaw, Jack; De Agostini, Paola; Chatsiou, Kakia; Gasior, Katrin; Jara Tamayo, Holguer Xavier; Leventi, Chrysa; Manios, Kostas; Paulus, Alari; Popova, Daria; Tasseva, Iva Valentinova
    Abstract: This paper presents baseline results from the latest version of EUROMOD (version I2.0+), the tax-benefit microsimulation model for the EU. First, we briefly report the process of updating EUROMOD. We then present indicators for income inequality and risk of poverty using EUROMOD and discuss the main reasons for differences between these and EU-SILC based indicators. We further compare EUROMOD distributional indicators across all EU 28 countries and over time between 2016 and 2019. Finally, we provide estimates of marginal effective tax rates (METR) for all 28 EU countries in order to explore the effect of tax and benefit systems on work incentives at the intensive margin. Throughout the paper, we highlight both the potential of EUROMOD as a tool for policy analysis and the caveats that should be borne in mind when using it and interpreting results. This paper updates the work reported in Tammik (2019).
    Date: 2020–12–14
  16. By: Peter Grajzl; Peter Murrell
    Abstract: The history of England’s institutions has long informed research on comparative economic development. Yet to date there exists no quantitative evidence on a core aspect of England’s institutional evolution, that embodied in the accumulated decisions of English courts. Focusing on the two centuries before the Industrial Revolution, we generate and analyze the first quantitative estimates of the development of English caselaw and its associated legal ideas. We achieve this in two companion papers. In this, the first of the pair, we build a comprehensive corpus of 52,949 reports of cases heard in England's high courts before 1765. Estimating a 100-topic structural topic model, we name and interpret all topics, each of which reflects a distinctive aspect of English legal thought. We produce time series of the estimated topic prevalences. To interpret the topic timelines, we develop a tractable model of the evolution of legal-cultural ideas and their prominence in case reports. In the companion paper, we will illustrate with multiple applications the usefulness of the large amount of new information generated by our approach.
    Keywords: English history, institutional development, machine learning, caselaw, idea diffusion
    JEL: C80 N00 K10 Z10 P10
    Date: 2020
  17. By: Christoph Schlembach; Sascha L. Schmidt; Dominik Schreyer; Linus Wunderlich
    Abstract: Forecasting the number of Olympic medals for each nation is highly relevant for different stakeholders: Ex ante, sports betting companies can determine the odds while sponsors and media companies can allocate their resources to promising teams. Ex post, sports politicians and managers can benchmark the performance of their teams and evaluate the drivers of success. To significantly increase the Olympic medal forecasting accuracy, we apply machine learning, more specifically a two-staged Random Forest, thus outperforming more traditional na\"ive forecast for three previous Olympics held between 2008 and 2016 for the first time. Regarding the Tokyo 2020 Games in 2021, our model suggests that the United States will lead the Olympic medal table, winning 120 medals, followed by China (87) and Great Britain (74). Intriguingly, we predict that the current COVID-19 pandemic will not significantly alter the medal count as all countries suffer from the pandemic to some extent (data inherent) and limited historical data points on comparable diseases (model inherent).
    Date: 2020–12
  18. By: Alaeddine Mihoub; Hosni Snoun; Moez Krichen (REDCAD - Unité de Recherche en développement et contrôle d'applications distribuées - ENIS - École Nationale d'Ingénieurs de Sfax | National School of Engineers of Sfax); Montassar Kahia; Riadh Bel Hadj Salah
    Abstract: The new so-called COVID-19 virus is unfortunately founded to be highly transmissible across the globe. In this study, we propose a novel approach for estimating the spread level of the virus for each country for three different dates between April and May 2020. Unlike previous studies, this investigation does not process any historical data of spread but rather relies on the socioeconomic indicators of each country. Actually, more than 1000 socioeconomic indicators and more than 190 countries were processed in this study. Concretely, data preprocessing techniques and feature selection approaches were applied to extract relevant indicators for the classification process. Countries around the globe were assigned to 4 classes of spread. To find the class level of each country, many classifiers were proposed based especially on Support Vectors Machines (SVM), Multi-Layer Perceptrons (MLP) and Random Forests (RF). Obtained results show the relevance of our approach since many classifiers succeeded in capturing the spread level, especially the RF classifier, with an F-measure equal to 93.85% for April 15th, 2020. Moreover, a feature importance study is conducted to deduce the best indicators to build robust spread level classifiers. However, as pointed out in the discussion, classifiers may face some difficulties for future dates since the huge increase of cases and the lack of other relevant factors affecting this widespread.
    Keywords: covid-19,socio-economic indicators,data preprocessing,spread level prediction,machine learning,country classification,coronavirus,SARS-CoV-2,feature importance
    Date: 2020–11–03
  19. By: Diaz, Elena Maria; Pérez Quirós, Gabriel
    Abstract: This paper develops a novel indicator of global economic activity, the GEA Tracker, which is based on commodity prices selected recursively through a genetic algorithm. The GEA Tracker allows for daily real-time knowledge of international business conditions using a minimum amount of information. We find that the GEA Tracker outperforms its competitors in forecasting stock returns, especially in emerging markets, and in predicting standard indicators of international business conditions. We show that an investor would have inexorably profited from using the forecasts provided by the GEA Tracker to weight a portfolio. Finally, the GEA Tracker allows us to present the daily evolution of global economic activity during the COVID-19 pandemic. JEL Classification: F44, G17, Q02
    Keywords: commodity prices, factor models, genetic algorithm, global economic activity, leading indicators, variable selection
    Date: 2020–12
  20. By: Sridhar Ravula
    Abstract: Financial disclosure analysis and Knowledge extraction is an important financial analysis problem. Prevailing methods depend predominantly on quantitative ratios and techniques, which suffer from limitations like window dressing and past focus. Most of the information in a firm's financial disclosures is in unstructured text and contains valuable information about its health. Humans and machines fail to analyze it satisfactorily due to the enormous volume and unstructured nature, respectively. Researchers have started analyzing text content in disclosures recently. This paper covers the previous work in unstructured data analysis in Finance and Accounting. It also explores the state of art methods in computational linguistics and reviews the current methodologies in Natural Language Processing (NLP). Specifically, it focuses on research related to text source, linguistic attributes, firm attributes, and mathematical models employed in the text analysis approach. This work contributes to disclosure analysis methods by highlighting the limitations of the current focus on sentiment metrics and highlighting broader future research areas
    Date: 2021–01
  21. By: Mvondo, Thierry
    Abstract: Ce papier s’intéresse à l’efficacité des mesures de stabilisation puis de relance macroéconomique post COVID-19 dans la CEMAC. A cet effet, un modèle DSGE est construit, prenant en compte les spécificités de la zone dont particulièrement une banque centrale appelée à assouplir ses conditions monétaires et, l’Etat creusant son déficit à travers l’émission de titres domestiques et étrangers ainsi que des allègements/exonérations fiscaux. Dans ce cadre, la COVID-19 est modélisée comme un processus autorégressif impactant directement certaines variables et, indirectement d’autres à travers le principe de chocs corrélés. Les simulations menées montrent que : (i) la modélisation retenue de la COVID-19 permet de répliquer les effets postulés de cette dernière dans la CEMAC ; (ii) les mesures budgétaires, quoique plus efficaces par rapport aux mesures monétaires et macroprudentielles pourraient avoir des effets distorsifs sur certaines variables financières et ; (iii) les effets obtenus prendraient 30 trimestres environ pour se réaliser complètement.
    Keywords: Choc COVID-19; Stabilisation; Relance macroéconomique; DSGE; CEMAC
    Date: 2021–01
  22. By: International Monetary Fund
    Abstract: This technical note Financial Stability Analysis and Stress Testing on Singapore contributes to the assessment of the stability and soundness of the financial sector with a comprehensive set of risk analyses. The work combines an examination of key risk indicators with detailed stress tests, which simulate the health of banks, insurers, nonfinancial corporates and households under severe yet plausible (counterfactual) adverse scenarios. Scenarios include global financial market turmoil, a major slowdown of economic activity in China, cyber-attacks and extreme flooding. The analyses include simulations of contagion within the international banking network, within the domestic banking system and between different types of financial institutions in the financial system. The stress tests reveal that the financial system is broadly resilient to severe adverse shocks; however, foreign exchange liquidity is a key vulnerability. The analyses suggest that Monetary Authority of Singapore should continue strengthening its surveillance by closing data gaps and developing its analytical tools. Further data collection on domestic interlinkages, household mortgage debt at the borrower level, insurers’ balance sheets would enhance surveillance.
    Keywords: Stress testing;Banking;Domestic systemically important banks;Insurance companies;Capital adequacy requirements;ISCR,CR,U.S. dollar,financial market,banking group,financial system,sensitivity analysis,solvency stress tests
    Date: 2019–07–15
  23. By: Badenes-Plá, Nuria; Blanco Palmero, Patricia; Gambau-Suelves, Borja; Navas Román, María; Villazán Pellejero, Noemí
    Abstract: This paper focuses on the study of the effects on social welfare generated by the scheme of joint taxation of the Spanish Personal Income Tax (PIT), whose peculiarity linked to its condition of optionality, allows the minimization of households´ tax bill. Different scenarios are simulated using the tax-benefit microsimulator of the European Commission – EUROMOD – with data from the Survey on Income and Living Conditions corresponding to 2016. In order to measure the welfare, the current PIT scheme is taken as reference and then it is compared with two alternatives, one, in which the families that currently can opt for this system are forced to pay jointly, and another, in which the only taxation scheme was individual. The results show that the Spanish system is revealed as a generator of additional welfare linked both to the circumstance of allowing an option to families, as well as to the fact of designing a specific system of joint taxation. In addition, it is shown that the policy recommendations would be different if only the study of inequality had been considered, since the net income gains of the current system offset the possible improvements in inequality of the simulated alternatives. Our results, therefore, also reinforce the convenience of adopting an approach that simultaneously considers efficiency and equity.
    Date: 2020–12–23
  24. By: Martin, Albert
    Abstract: Die vorliegende Simulationsstudie befasst sich mit der Etablierung und Veränderung der Personalpolitik von Organisationen. Das den Simulationsrechnungen zugrunde liegende Modell beschreibt und erklärt den Einfluss personalpolitischer Kraftfelder auf die personalpolitischen Orientierungen von Organisationen. Berücksichtigt werden dabei die Rückwirkungen, die von der Personalpolitik auf die sie bestimmenden Kräfte ausgehen sowie die Beharrungskräfte, die einer einmal etablierten personalpolitischen Konstellation innewohnen. Und schließlich wird den Zufallsprozessen, die das personalpolitische Geschehen wesentlich mitbestimmen, die ihnen gebührende Beachtung geschenkt. Als theoretische Grundlage dient eine erweiterte und auf die Erklärung der Personalpolitik hin ausgerichtete Version der Anreiz-Beitrags-Theorie, wonach sich, je nach den gegebenen Feldkraftkonstellationen, angepasste Sozialordnungen herausbilden. Die Elemente und Strukturen dieser Sozialordnungen sind aufeinander abgestimmt und stützen sich gegenseitig. Daraus entwickeln sich interne "Bindungskräfte", die den verschiedenen Sozialordnungen ihre je eigene Stabilität verleihen. Die in dieser Theorie thematisierten Zusammenhänge werden in dem Simulationsmodell konkretisiert und in entsprechende Rechenvorschriften transformiert. Damit wird es möglich, den Ablauf und die Wirkung der von der Theorie unterstellten Mechanismen anhand von konkret benennbaren Vorgängen zu analysieren.
    Date: 2020
  25. By: Benjamin Carton; Armand Fouejieu
    Abstract: Although the Netherlands entered the so-called Great Lockdown with a strong fiscal position, the Dutch fiscal balance is projected to deteriorate by an unprecedented magnitude, largely as a result of necessary fiscal measures deployed to weather the economic impact of the COVID-19 pandemic. This paper performs a stochastic analysis of risks to Dutch fiscal and debt sustainability over the next decade, taking into account alternative recovery scenarios and associated fiscal consolidation paths and also a range of macroeconomic shocks drawn from the historical experience of the Netherlands. The simulations show that even under significant downturn scenarios and assuming an initially less favorable fiscal position due to persistent economic effects of the pandemic, risks to the Dutch fiscal and debt sustainability would remain contained.
    Date: 2020–12–04
  26. By: Benoît Schmutz (Ecole Polytechnique and CREST); Modibo Sidibé (Duke University)
    Abstract: This paper proposes a theory of cities based on a general equilibrium search and matching model where heterogeneous firms and workers continuously decide where to locate within a set of imperfectly connected local labor markets and engage in wage bargaining using both local and remote match opportunities as threat points. The model allows us to introduce the structural origins of workers’ sorting, firms’ selection and matching-based agglomeration economies into a unified framework and discuss their relationship with the city size distribution. Simulations show that power laws in city size do not require increasing returns to scale in matching or production, but may simply result from the combination of imperfect labor mobility, positive assortative matching between labor and capital, and agglomeration economies in the matching between workers and firms. By-products include sufficient statistics to identify sorting and agglomeration using city-level variation and a rationale for the geographic diversity of urban networks.
    Keywords: city size; local labor market; frictions; on-the-job search; migration
    JEL: R1 J2 J3 J6
    Date: 2021–01–04
  27. By: Midões, Catarina; Seré, Mateo
    Abstract: The COVID-19 crisis has led to substantial reductions in earnings. We propose a new measure of financial vulnerability, computable through survey data, to determine whether households can withstand a certain income shock for a defined period of time. Using data from the ECB Household Finance and Consumption Survey (HFCS) we analyse preexisting financial vulnerability in seven EU countries. We find that income support is essential for many families: 47.2 million individuals, out of the 243 million considered, cannot afford three months of food and housing expenses without privately earned income. Differences across countries are stark, and those born outside of the EU are especially vulnerable. Through a tax-benefit microsimulation exercise, we then derive household net income when employees are laid-off and awarded the COVID-19 employment protection benefits enacted in the different countries. Our findings suggest that the COVID-19 employment protection schemes awarded are extremely effective in reducing the number of vulnerable individuals. The relative importance of rent and mortgage suspensions in alleviating vulnerability is highly country dependent.
    Date: 2020–12–21

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.