nep-cmp New Economics Papers
on Computational Economics
Issue of 2020‒02‒17
eleven papers chosen by
Stan Miles
Thompson Rivers University

  1. On Calibration Neural Networks for extracting implied information from American options By Shuaiqiang Liu; \'Alvaro Leitao; Anastasia Borovykh; Cornelis W. Oosterlee
  2. A Branch-Price-and-Cut Algorithm for the Capacitated Multiple Vehicle Traveling Purchaser Problem with Unitary Demand By Nicola Bianchessi; Stefan Irnich; Christian Tilk
  3. Deep combinatorial optimisation for optimal stopping time problems and stochastic impulse control. Application to swing options pricing and fixed transaction costs options hedging By Thomas Deschatre; Joseph Mikael
  4. Pricing commodity swing options By Roberto Daluiso; Emanuele Nastasi; Andrea Pallavicini; Giulio Sartorelli
  5. The Macroeconomics of Automation: Data, Theory, and Policy Analysis By Jaimovich, Nir; Saporta-Eksten, Itay; Siu, Henry E.; Yedid-Levi, Yaniv
  6. Reducing the income tax burden for households with children: An assessment of the child tax credit reform in Austria By Christl, Michael; De Poli, Silvia; Varga, Janos
  7. K-DSGE: A Dynamic Stochastic General Equilibrium Model for Saudi Arabia By Jorge Blazquez; Marzio Galeotti; Baltasar Manzano; Axel Pierru; Shreekar Pradhan
  8. Artificial Intelligence Platforms – A New Research Agenda for Digital Platform Economy By Mucha, Tomasz; Seppälä, Timo
  9. Partial Identification and Inference for Dynamic Models and Counterfactuals By Myrto Kalouptsidi; Yuichi Kitamura; Lucas Lima; Eduardo Souza-Rodrigues
  10. Economic Production as Life: A Classical Approach to Computational Social Science By Oriol Valles Codina
  11. The Global Impact of Brexit Uncertainty By Tarek Hassan; Laurence van Lent; Stephan Hollander; Ahmed Tahoun

  1. By: Shuaiqiang Liu; \'Alvaro Leitao; Anastasia Borovykh; Cornelis W. Oosterlee
    Abstract: Extracting implied information, like volatility and/or dividend, from observed option prices is a challenging task when dealing with American options, because of the computational costs needed to solve the corresponding mathematical problem many thousands of times. We will employ a data-driven machine learning approach to estimate the Black-Scholes implied volatility and the dividend yield for American options in a fast and robust way. To determine the implied volatility, the inverse function is approximated by an artificial neural network on the computational domain of interest, which decouples the offline (training) and online (prediction) phases and thus eliminates the need for an iterative process. For the implied dividend yield, we formulate the inverse problem as a calibration problem and determine simultaneously the implied volatility and dividend yield. For this, a generic and robust calibration framework, the Calibration Neural Network (CaNN), is introduced to estimate multiple parameters. It is shown that machine learning can be used as an efficient numerical technique to extract implied information from American options.
    Date: 2020–01
  2. By: Nicola Bianchessi (University of Milan); Stefan Irnich (Johannes Gutenberg University Mainz); Christian Tilk (Johannes Gutenberg University Mainz)
    Abstract: The multiple vehicle traveling purchaser problem (MVTPP) consists of simultaneously selecting suppliers and routing a fleet of homogeneous vehicles to purchase different products at the selected suppliers so that all product demands are fulfilled and traveling and purchasing costs are minimized. We consider variants of the MVTPP in which the capacity of the vehicles can become binding and the demand for each product is one unit. Corresponding solution algorithms from the literature are either branch-and-cut or branch-and-price algorithms, where in the latter case the route-generation subproblem is solved on an expanded graph by applying standard dynamic-programming techniques. Our branch-price-and-cut algorithm employs a novel labeling algorithm that works directly on the original network and postpones the purchasing decisions until the route has been completely defined. Moreover, we define a new branching rule generally applicable in case of unitary product demands, introduce a new family of valid inequalities to apply when suppliers can be visited at most once, and show how product incompatibilities can be handled without considering additional resources in the pricing problem. In comprehensive computational experiments with standard benchmark sets we prove that the new branch-price-and-cut approach is highly competitive.
    Keywords: vehicle routing, multiple vehicle traveling purchaser problem, unitary demand, incompatible products, column generation, dynamic-programming labeling algorithm
    Date: 2020–01–23
  3. By: Thomas Deschatre; Joseph Mikael
    Abstract: A new method for stochastic control based on neural networks and using randomisation of discrete random variables is proposed and applied to optimal stopping time problems. Numerical tests are done on the pricing of American and swing options. An extension to impulse control problems is described and applied to options hedging under fixed transaction costs. The proposed algorithms seem to be competitive with the best existing algorithms both in terms of precision and in terms of computation time.
    Date: 2020–01
  4. By: Roberto Daluiso; Emanuele Nastasi; Andrea Pallavicini; Giulio Sartorelli
    Abstract: In commodity and energy markets swing options allow the buyer to hedge against futures price fluctuations and to select its preferred delivery strategy within daily or periodic constraints, possibly fixed by observing quoted futures contracts. In this paper we focus on the natural gas market and we present a dynamical model for commodity futures prices able to calibrate liquid market quotes and to imply the volatility smile for futures contracts with different delivery periods. We implement the numerical problem by means of a least-square Monte Carlo simulation and we investigate alternative approaches based on reinforcement learning algorithms.
    Date: 2020–01
  5. By: Jaimovich, Nir (University of Zurich); Saporta-Eksten, Itay (Tel Aviv University); Siu, Henry E. (University of British Columbia); Yedid-Levi, Yaniv (Interdisciplinary Center (IDC) Herzliya)
    Abstract: The U.S. economy has experienced a significant drop in the fraction of the population employed in middle wage, "routine task-intensive" occupations. Applying machine learning techniques, we identify characteristics of those who used to be employed in such occupations and show they are now less likely to work in routine occupations. Instead, they are either non-participants in the labor force or working at occupations that tend to occupy the bottom of the wage distribution. We then develop a quantitative, heterogeneous agent, general equilibrium model of labor force participation, occupational choice, and capital investment. This allows us to quantify the role of advancement in automation technology in accounting for these labor market changes. We then use this framework as a laboratory to evaluate various public policies aimed at addressing the disappearance of routine employment and its consequent impacts on inequality.
    Keywords: polarization, automation, routine employment, labor force participation, universal basic income, unemployment insurance, retraining
    JEL: E22 E24 J23 J24
    Date: 2020–01
  6. By: Christl, Michael; De Poli, Silvia; Varga, Janos
    Abstract: This paper analyses the impact of the implementation of a child tax credit in Austria in 2019, not only on micro, but also on macro level by using a dynamic scoring methodology. First, we assess the fiscal and distributional impact of this reform using the microsimulation model EUROMOD. Second, we estimate labour supply impacts of the reform based on a structural discrete choice framework. Third, we evaluate the macroeconomic impacts of the reform, by calibrating and shocking QUEST, the DSGE model of the European Commission, with the micro-based results for the implicit tax rate, the non-participation and the labour supply elasticities. We show that the child tax credit reform in Austria reduces inequality, lowers the poverty rate in general, but by definition only for households with children. Overall the reform has a positive impact on labour supply, both on the extensive and on the intensive margin, especially for women. On the macro-level (and in the long-run), our model suggests a positive impact on employment. Additionally, we find that parts of the tax decrease can be potentially captured by the employer, meaning that gross wages would fall slightly. However, we find small but positive effects on GDP, investment and consumption, although the longrun macroeconomic effects depend crucially on how the government compensates the missing tax revenues after the reform. Accounting for these feedback effects at the micro level with a new methodology, we show that the second round effects are important to take into account, because they provide insights into the medium-term distributional impact of the reform.
    Keywords: EUROMOD,tax credit,reform,DSGE,labour supply,microsimulation,discrete choice
    JEL: H24 H31 I38
    Date: 2020
  7. By: Jorge Blazquez; Marzio Galeotti; Baltasar Manzano; Axel Pierru; Shreekar Pradhan (King Abdullah Petroleum Studies and Research Center)
    Abstract: This paper describes a dynamic stochastic general equilibrium (DSGE) model of the Saudi Arabian economy, developed by KAPSARC researchers. The K-DSGE model is to be used for simulations and experiments to assess the impact of economic reforms within the Saudi Vision 2030 framework. The model will also complement the suite of models currently used at KAPSARC for macroeconomic analysis, to assess the impact of the Kingdom’s public policies.
    Keywords: Saudi Arabia, Economic Modeling, Saudi Vision 2030
    Date: 2019–06–01
  8. By: Mucha, Tomasz; Seppälä, Timo
    Abstract: Abstract Three out of nine of S&P500 digital platform companies stand out as building own artificial intelligence (AI) platforms. There is overwhelming empirical evidence of AI technologies are being central to running a digital platform business. However, the current research agenda is not directing researchers to study AI technologies in the context of digital platforms. We have divided the proposed AI platforms research agenda as follows: The first set of questions we propose relates to an overall conceptualization of AI platforms. Thereafter, we recognize specific aspects of AI platforms, which need to be investigated in detail to gain understanding that is more complete. The second set of questions we propose relates to understanding the dynamics between AI platforms and the broader socio-economic context. This topic might be particularly relevant to economies of countries without indigenous AI platforms. Our paper builds on the proposition that AI is a general-purpose technology, which by itself carries properties of a digital platform.
    Keywords: Platforms, Digital Platform Economy, Artificial Intelligence, AI platforms, Research agenda
    JEL: M1 M21 O3 O33
    Date: 2020–02–06
  9. By: Myrto Kalouptsidi; Yuichi Kitamura; Lucas Lima; Eduardo Souza-Rodrigues
    Abstract: We provide a general framework for investigating partial identification of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures. In doing so, we derive sharp bounds for the model parameters, counterfactual behavior, and low-dimensional outcomes of interest, such as the average welfare effects of hypothetical policy interventions. We characterize the properties of the sets analytically and show that when the target outcome of interest is a scalar, its identified set is an interval whose endpoints can be calculated by solving well-behaved constrained optimization problems via standard algorithms. We obtain a uniformly valid inference procedure by an appropriate application of subsampling. To illustrate the performance and computational feasibility of the method, we consider both a Monte Carlo study of firm entry/exit, and an empirical model of export decisions applied to plant-level data from Colombian manufacturing industries. In these applications, we demonstrate how the identified sets shrink as we incorporate alternative model restrictions, providing intuition regarding the source and strength of identification.
    Keywords: Dynamic Discrete Choice, Counterfactual, Partial Identification, Subsampling, Uniform Inference, Structural Model
    JEL: C0 C18 C50 C61 L0
    Date: 2020–02–06
  10. By: Oriol Valles Codina (Department of Economics, New School for Social Research)
    Abstract: The paper proposes a simple model of multi-sectorial growth along classical, computational and evolutionary lines where equilibrium in prices and quantities is not presumed, but dynamically attained by the systematic, decentralized operation of economic competition over time. The model thus provides a parsimonious and innovative solution to the classic problem of the dynamic convergence of a competitive economy to equilibrium. The model views economic competition as a statistical process over time that results in the dynamic equalization of prices, industry supply and demand, and inter-industrial profit rates in the aggregate. It is disaggregated by firms under an evolving heterogeneous technology operating under alternative micro-foundations, without any recourse to utility or profit maximization, but instead based on reproduction as fundamental closure. In order to relate macro-level patterns to micro-level observations over time, the theoretical framework re-conceptualizes equilibrium as a stationary property of statistical distributions of micro-economic variables, which allows a more realistic empirical description of the economy beyond the conventional notion of equilibrium as a single-point, rest state. The model generates a variety of stylized facts as macro-level emergent outcomes of the competitive process: price dispersion around the competitive value, a spectrum of profitability differentials re specting cost differences, evolutionary selection of the lowest-cost firm, tent-shaped distributions of profit and growth rates that are consistent with the empirical evidence, cost-plus markup pricing, downward-sloping demand curves, and industry-level business cycles in prices and inventories.
    JEL: B51 B52 C63 D21 D58 E11 E14 E30
    Date: 2020–01
  11. By: Tarek Hassan (Boston University); Laurence van Lent (Frankfurt School of Finance and Management); Stephan Hollander (Tilburg University); Ahmed Tahoun (London Business School)
    Abstract: Using tools from computational linguistics, we construct new measures of the impact of Brexit on listed firms in the United States and around the world; these measures are based on the proportion of discussions in quarterly earnings conference calls on the costs, benefits, and risks associated with the UK’s intention to leave the EU. We identify which firms expect to gain or lose from Brexit and which are most affected by Brexit uncertainty. We then estimate effects of the different types of Brexit exposure on firm-level outcomes. We find that the impact of Brexit- related uncertainty extends far beyond British or even European firms; US and international firms most exposed to Brexit uncertainty lost a substantial fraction of their market value and have also reduced hiring and investment. In addition to Brexit uncertainty (the second moment), we find that international firms overwhelmingly expect negative direct effects from Brexit (the first moment) should it come to pass. Most prominently, firms expect difficulties from regulatory divergence, reduced labor mobility, limited trade access, and the costs of post-Brexit operational adjustments. Consistent with the predictions of canonical theory, this negative sentiment is recognized and priced in stock markets but has not yet significantly affected firm actions.
    Keywords: Brexit, uncertainty, sentiment, machine learning, cross-country effects
    JEL: D8 E22 E24 E32 E6 F0 G18 G32 G38 H32

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