nep-cmp New Economics Papers
on Computational Economics
Issue of 2019‒02‒11
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Pricing options and computing implied volatilities using neural networks By Shuaiqiang Liu; Cornelis W. Oosterlee; Sander M. Bohte
  2. A Study on Neural Network Architecture Applied to the Prediction of Brazilian Stock Returns By Leonardo Felizardo; Afonso Pinto
  3. From behavioural simulation to computer models: how simulation can be used to improve healthcare management and policy By Guillaume Lamé; Rebecca K. Simmons
  4. Investigating Limit Order Book Characteristics for Short Term Price Prediction: a Machine Learning Approach By Faisal I Qureshi
  5. Heterogeneity, distribution and financial fragility of non-financial firms: an agent-based stock-flow consistent (AB-SFC) model By Ítalo Pedrosa; Dany Lang
  6. Development of an agent-based speculation game for higher reproducibility of financial stylized facts By Kei Katahira; Yu Chen; Gaku Hashimoto; Hiroshi Okuda
  7. Learning Choice Functions By Karlson Pfannschmidt; Pritha Gupta; Eyke H\"ullermeier
  8. Big Data, Data Mining, Machine Learning und Predictive Analytics: Ein konzeptioneller Überblick By Brühl, Volker
  9. The Redistributive Effects of a Minimum Wage Increase in New Zealand A Microsimulation Analysis By Alinaghi, Nazila; Creedy, John; Gemmell, Norman
  10. Performance expectations of professional sport teams and in-season head coach dismissals: Evidence from the English Premier League and the French Ligue 1 using Monte Carlo simulation By Yvon Rocaboy; Marek Pavlik
  11. Strong convergence rates for numerical approximations of fractional Brownian motion By Philipp Harms
  12. Lattice investment projects support process model with corruption By O. A. Malafeyev; S. A. Nemnyugin
  13. Local cost for global benefit: The case of wind turbines By Frondel, Manuel; Kussel, Gerhard; Sommer, Stephan; Vance, Colin

  1. By: Shuaiqiang Liu; Cornelis W. Oosterlee; Sander M. Bohte
    Abstract: This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent's iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly.
    Date: 2019–01
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1901.08943&r=all
  2. By: Leonardo Felizardo; Afonso Pinto
    Abstract: In this paper we present a statistical analysis about the characteristics that we intend to influence in the performance of the neural networks in terms of assertiveness in the prediction of Brazilian stock returns. We created a population of architectures for analysis and extracted the sample that had the best assertive performance. It was verified how the characteristics of this sample stand out and affect the neural networks. In addition, we make inferences about what kind of influence the different architectures have on the performance of neural networks. In the study, the prediction of the return of a Brazilian stock traded on the stock exchange of S\~ao Paulo to measure the error committed by the different architectures of constructed neural networks. The results are promising and indicate that some aspects of the neural network architecture have a significant impact on the assertiveness of the model.
    Date: 2019–01
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1901.09143&r=all
  3. By: Guillaume Lamé (THIS Institute (The Healthcare Improvement Studies Institute) - Department of Public Health and Primary Care, University of Cambridge - CAM - University of Cambridge [UK], CAM - University of Cambridge [UK]); Rebecca K. Simmons (THIS Institute (The Healthcare Improvement Studies Institute) - Department of Public Health and Primary Care, University of Cambridge - CAM - University of Cambridge [UK], CAM - University of Cambridge [UK])
    Abstract: Simulation is a technique that evokes or replicates substantial aspects of the real world, in order to experiment with a simplified imitation of an operations system, for the purpose of better understanding and/ or improving that system. Simulation provides a safe environment for investigating individual and organisational behaviour and a risk-free testbed for new policies and procedures. Therefore, it can complement or replace direct field observations and trial-and-error approaches, which can be time consuming, costly and difficult to carry out. However, simulation has low adoption as a research and improvement tool in healthcare management and policy-making. The literature on simulation in these fields is dispersed across different disciplinary traditions and typically focuses on a single simulation method. In this article, we examine how simulation can be used to investigate, understand and improve management and policy-making in healthcare organisations. We develop the rationale for using simulation and provide an integrative overview of existing approaches, using examples of in vivo behavioural simulations involving live participants, pure in silico computer simulations and intermediate approaches (virtual simulation) where human participants interact with computer simulations of health organisations. We also discuss the combination of these approaches to organisational simulation and the evaluation of simulation-based interventions.
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-01900536&r=all
  4. By: Faisal I Qureshi
    Abstract: With the proliferation of algorithmic high-frequency trading in financial markets, the Limit Order Book has generated increased research interest. Research is still at an early stage and there is much we do not understand about the dynamics of Limit Order Books. In this paper, we employ a machine learning approach to investigate Limit Order Book features and their potential to predict short term price movements. This is an initial broad-based investigation that results in some novel observations about LOB dynamics and identifies several promising directions for further research. Furthermore, we obtain prediction results that are significantly superior to a baseline predictor.
    Date: 2018–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1901.10534&r=all
  5. By: Ítalo Pedrosa (Federal University of Rio de Janeiro - UFJR (.)); Dany Lang (CEPN - Centre d'Economie de l'Université Paris Nord - UP13 - Université Paris 13 - USPC - Université Sorbonne Paris Cité - CNRS - Centre National de la Recherche Scientifique)
    Abstract: In Minsky's Financial Instability Hypothesis (FIH), financial fragility of non-financial firms tends to increase endogenously over the cycle along with the macroeconomic leverage ratio. This analysis has been criticized for two main complementary reasons: firstly, it does not duly consider the aggregate pro-cyclicallity of profits; secondly, due to an overly aggregate analysis, some inferences about the relation between aggregate leverage and systemic fragility are potentially misleading. In this paper, we take these criticisms into account by building an agent-based stock-flow consistent model which integrates the real and financial sides of the economy in a fundamentally dynamic environment. We calibrate and simulate our model and show that the dynamics generated are in line with empirical evidence both at the micro and the macro levels. We create a financial fragility index and examine how systemic financial fragility relates to the aggregate leverage along the cycle. We show that our model yields both Min-skian regimes, in which the aggregate leverage increases along with investment, and Steindlian regimes, where investment brings leverage down. Our key findings are that the sensitivity of financial fragility to aggregate leverage is not as big as assumed in the literature; and that the distribution of profits amongst firms does matter for the stability of the system, both statically (immediately for financial fragility) and dynamically (because of the dynamics of leverage).
    Keywords: financial fragility,firms,leverage,cash flow,distribution
    Date: 2018–11–28
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:hal-01937186&r=all
  6. By: Kei Katahira; Yu Chen; Gaku Hashimoto; Hiroshi Okuda
    Abstract: Simultaneous reproduction of all financial stylized facts is so difficult that most existing stochastic process-based and agent-based models are unable to achieve the goal. In this study, by extending the decision-making structure of Minority Game, we propose a novel agent-based model called "Speculation Game," for a better reproducibility of the stylized facts. The new model has three distinct characteristics comparing with preceding agent-based adaptive models for the financial market: the enabling of nonuniform holding and idling periods, the inclusion of magnitude information of price change in history, and the implementation of a cognitive world for the evaluation of investment strategies with capital gains and losses. With these features, Speculation Game succeeds in reproducing 10 out of the currently well studied 11 stylized facts under a single parameter setting.
    Date: 2019–02
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1902.02040&r=all
  7. By: Karlson Pfannschmidt; Pritha Gupta; Eyke H\"ullermeier
    Abstract: We study the problem of learning choice functions, which play an important role in various domains of application, most notably in the field of economics. Formally, a choice function is a mapping from sets to sets: Given a set of choice alternatives as input, a choice function identifies a subset of most preferred elements. Learning choice functions from suitable training data comes with a number of challenges. For example, the sets provided as input and the subsets produced as output can be of any size. Moreover, since the order in which alternatives are presented is irrelevant, a choice function should be symmetric. Perhaps most importantly, choice functions are naturally context-dependent, in the sense that the preference in favor of an alternative may depend on what other options are available. We formalize the problem of learning choice functions and present two general approaches based on two representations of context-dependent utility functions. Both approaches are instantiated by means of appropriate neural network architectures, and their performance is demonstrated on suitable benchmark tasks.
    Date: 2019–01
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1901.10860&r=all
  8. By: Brühl, Volker
    Abstract: Mit der fortschreitenden Digitalisierung von Wirtschaft und Gesellschaft wächst die Bedeutung von Big Data Analytics, maschinellem Lernen und Künstlicher Intelligenz für die Analyse und Pognose ökonomischer Trends. Allerdings werden in wirtschaftspolitischen Diskussionen diese Begriffe häufig verwendet, ohne dass jeweils klar zwischen den einzelnen Methoden und Disziplinen differenziert würde. Daher soll nachfolgend ein konzeptioneller Überblick über die Gemeinsamkeiten, Unterschiede und Interdependenzen der vielfältigen Begrifflichkeiten im Bereich Data Science gegeben werden. Denn gerade für Entscheidungsträger aus Wirtschaft und Politik kann eine grundlegende Einordnung der Konzepte eine sachgerechte Diskussion über politische Weichenstellungen erleichtern.
    JEL: A10 C10 D80
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:zbw:cfswop:617&r=all
  9. By: Alinaghi, Nazila; Creedy, John; Gemmell, Norman
    Abstract: This paper examines the potential effects on inequality and poverty of a minimum wage increase, based on a microsimulation model which allows for labour supply responses. It then compares these outcomes with an alternative, commonly used policy of raising government welfare benefits, similarly aimed at poverty or inequality reduction. Results suggested that, due to the composition of household incomes, a policy of increasing the minimum wage appears to have a relatively small effect on the inequality of income per adult equivalent person, using several inequality indices. The minimum wage policy is not particularly well targeted at its objective, largely due to many low-wage earners being secondary earners in higher-income households, while many low-income households have no wage earners at all. However, an ‘equivalent’ policy of raising welfare benefits does not clearly demonstrate ‘target superiority’. Results suggest that while raising benefits has a greater ability to reduce most poverty measures examined, substantially smaller inequality reductions are found to be associated with benefit increases compared to a minimum wage increase. Thus benefit increases represent a relatively effective strategy for poverty reduction, mainly by targeting sole parents, but (like minimum wages) are also relatively ineffective if inequality reduction is the objective.
    Keywords: Microsimulation, Income inequality, Poverty, Minimum wage,
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:vuw:vuwcpf:8023&r=all
  10. By: Yvon Rocaboy (Univ Rennes, CNRS, CREM - UMR 6211, F-35000 Rennes, France & Condorcet Center for Political Economy); Marek Pavlik (Masaryk University, Brno, Czech Republic)
    Abstract: Why and when are head coaches of professional football clubs dismissed, and what is the effect of coach dismissal on team performance? In this paper, we propose a new method for assessing the performance expectations of professional sport teams using Monte Carlo simulation. We investigate those questions by applying our method to the English Premier league and the French Ligue 1 football teams over the 2015-2016 and 2016-2017 seasons. We found that coach dismissal is the result of a drop in the average expected performance compared with the performance expectations at the beginning of the season. We also show that dismissing a coach may enhance performance only if the team under-performed before the dismissal. There is no obstacle to using the same method for professional teams in other sports. The method is easily reproducible and does not require much information in order to be applied. Our findings confirm and extend previous studies, and do so by using generally accessible data.
    Keywords: coach dismissal; Monte Carlo simulation; team performance; payrolls; expectations
    Date: 2019–01
    URL: http://d.repec.org/n?u=RePEc:tut:cremwp:2019-01&r=all
  11. By: Philipp Harms
    Abstract: Fractional Brownian motion can be represented as an integral over a family of Ornstein-Uhlenbeck processes. This representation naturally lends itself to numerical discretizations, which are shown in this paper to have strong convergence rates of arbitrarily high polynomial order. This explains the potential, but also some limitations of such representations as the basis of Monte Carlo schemes for fractional volatility models such as the rough Bergomi model.
    Date: 2019–02
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1902.01471&r=all
  12. By: O. A. Malafeyev; S. A. Nemnyugin
    Abstract: Lattice investment projects support process model with corruption is formulated and analyzed. The model is based on the Ising lattice model of ferromagnetic but takes deal with the social phenomenon. Set of corruption agents is considered. It is supposed that agents are placed in sites of the lattice. Agents take decision about participation in corruption activity at discrete moments of time. The decision may lead to profit or to loss. It depends on prehistory of the system. Profit and its dynamics are defined by stochastic Markov process. Stochastic nature of the process models influence of external and individual factors on agents profits. The model is formulated algorithmically and is studied by means of computer simulation. Numerical results are given which demonstrate different asymptotic state of a corruption network for various conditions of simulation.
    Date: 2019–01
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1901.08764&r=all
  13. By: Frondel, Manuel; Kussel, Gerhard; Sommer, Stephan; Vance, Colin
    Abstract: Given the rapid expansion of wind power capacities in Germany, this paper estimates the effects of wind turbines on house prices using real estate price data from Germany's leading online broker. Employing a hedonic price model whose specification is informed by machine learning techniques, our methodological approach provides insights into the sources of heterogeneity in treatment effects. We estimate an average treatment effect (ATE) of up to -7.1% for houses within a one-kilometer radius of a wind turbine, an effect that fades to zero at a distance of 8 to 9 km. Old houses and those in rural areas are affected the most, while home prices in urban areas are hardly affected. These results highlight that substantial local externalities are associated with wind power plants.
    Keywords: wind power,hedonic price model
    JEL: Q21 D12 R31
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:zbw:rwirep:791&r=all

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