nep-cmp New Economics Papers
on Computational Economics
Issue of 2016‒02‒23
ten papers chosen by
Stan Miles
Thompson Rivers University

  1. Market structure or traders’ behavior? An assessment of flash crash phenomena and their regulation based on a multi-agent simulation By Nathalie Oriol; Iryna Veryzhenko
  2. Allocating tools to index positions in tool magazines using tabu search By Ghosh, Diptesh
  3. Coordinating Cross-Country Congestion Management By Friedrich Kunz; Alexander Zerrahn
  4. ABC and Hamiltonian Monte-Carlo methods in COGARCH models By J. Miguel Marín; M. T. Rodríguez-Bernal; E. Romero
  5. GenSVM: A Generalized Multiclass Support Vector Machine By van den Burg, G.J.J.; Groenen, P.J.F.
  6. Identifying Telecommunication strategies and investment Opportunities in Latin American Countries Based on Clustering Analysis By Peñas-de Pablo, José Miguel; Portilla-Figueras, José Antonio; Navío-Marco, Julio; Salcedo-Sanz, Sancho
  7. An integrated algorithm for the optimal design of stated choice experiments with partial profiles By CUERVO, Daniel Palhazi; KESSELS, Roselinde; GOOS, Peter; SÖRENSEN, Kenneth
  8. Growth, Water Resilience, and Sustainability: A DSGE Model Applied to South Africa By Li, Chuan-Zhong; Bali Swain, Ranjula
  9. Modelling intensities of order flows in a limit order book By Ioane Muni Toke; Nakahiro Yoshida
  10. Modeling Economic Choice under Radical Uncertainty: Machine Learning Approaches By Gerunov, Anton

  1. By: Nathalie Oriol (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - CNRS - Centre National de la Recherche Scientifique - UNS - Université Nice Sophia Antipolis); Iryna Veryzhenko (LIRSA - Laboratoire Interdisciplinaire de Recherche en Sciences de l'Action - Conservatoire National des Arts et Métiers [CNAM])
    Abstract: This paper aims at studying the flash crash caused by an operational shock with different market participants. We reproduce this shock in artificial market framework to study market quality in different scenarios, with or without strategic traders. We show that traders’ srategies influence the magnitude of the collapse. But, with the help of zero-intelligence traders framework, we show that despite theabsence of market makers, the order-driven market is resilient and favors a price recovery. We find that a short-sales ban imposed by regulator reduces short-term volatility.
    Keywords: flash crash, limit order book, technical trading,Agent-based modeling, zero-intelligence trader
    Date: 2015–05–01
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-01254435&r=cmp
  2. By: Ghosh, Diptesh
    Abstract: The arrangement of tools in tool slots of a tool magazine is an important problem in automated machining environments. This problem is called the indexing problem and has been widely studied in the literature. In this paper we propose a tabu search algorithm to solve the indexing problem. We perform computational experiments on instances which are of reasonable sizes.
    URL: http://d.repec.org/n?u=RePEc:iim:iimawp:14247&r=cmp
  3. By: Friedrich Kunz; Alexander Zerrahn
    Abstract: We employ a detailed two-stage model to simulate the operation of the Central Eastern European electricity market and network. Implementing different cases of coordination in congestion management between national transmission system operators, numerical results show the beneficial impact of closer cooperation. Specific steps comprise the sharing of network and dispatch information, cross-border counter-trading, and multilateral redispatch in a flow-based congestion management framework. Efficiency gains are accompanied by distributional effects. Closer economic cooperation becomes especially relevant against the background of changing spatial generation patterns, deeper international integration ofnational systems, and spillovers of national developments to adjacent systems.
    Keywords: Electricity, congestion management, network modeling, Europe
    JEL: C63 L51 L94
    Date: 2016
    URL: http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1551&r=cmp
  4. By: J. Miguel Marín; M. T. Rodríguez-Bernal; E. Romero
    Abstract: The analysis of financial series, assuming calendar effects and unequally spaced times over continuous time, can be studied by means of COGARCH models based on Lévy processes. In order to estimate the COGARCH model parameters, we propose to use two different Bayesian approaches. First, we suggest to use a Hamiltonian Montecarlo (HMC) algorithm that improves the performance of standard MCMC methods. Secondly, we introduce an Approximate Bayesian Computational (ABC) methodology which allows to work with analytically infeasible or computationally expensive likelihoods. After a simulation and comparison study for both methods, HMC and ABC, we apply them to model the behaviour of some NASDAQ time series and we discuss the results.
    Keywords: Approximate Bayesian Computation methods (ABC) , Bayesian inference , COGARCH model , Continuous-time GARCH process , Hamiltonian Monte Carlo methods (HMC) , Lévy process
    Date: 2016–01
    URL: http://d.repec.org/n?u=RePEc:cte:wsrepe:ws1601&r=cmp
  5. By: van den Burg, G.J.J.; Groenen, P.J.F.
    Abstract: __Abstract__ Traditional extensions of the binary support vector machine (SVM) to multiclass problems are either heuristics or require solving a large dual optimization problem. Here, a generalized multiclass SVM called GenSVM is proposed, which can be used for classification problems where the number of classes K is larger than or equal to 2. In the proposed method, classification boundaries are constructed in a K - 1 dimensional space. The method is based on a convex loss function, which is flexible due to several different weightings. An iterative majorization algorithm is derived that solves the optimization problem without the need of a dual formulation. The method is compared to seven other multiclass SVM approaches on a large number of datasets. These comparisons show that the proposed method is competitive with existing methods in both predictive accuracy and training time, and that it significantly outperforms several existing methods on these criteria.
    Keywords: Support Vector Machines (SVMs), Multiclass Classification, Iterative Majorization, MM Algorithm, Classifier Comparison
    Date: 2014–12–18
    URL: http://d.repec.org/n?u=RePEc:ems:eureir:77638&r=cmp
  6. By: Peñas-de Pablo, José Miguel; Portilla-Figueras, José Antonio; Navío-Marco, Julio; Salcedo-Sanz, Sancho
    Abstract: In this paper we present a methodology for feature selection and clustering over variables describing countries’ economies and ICT indicators to study and identify investment opportunities, based on similarities between European and Latin American countries. We address two different problems. First, the work is based on a feature selection problem carried out with the Coral Reef Optimization algorithm. The CRO is a novel bio-inspired based on the simulation of reef formation and coral reproduction. On the other hand, the K-Means++ method is a high-performance robust tool designed to solve clustering problems. Together, both algorithms are able to successfully identify investment opportunities in Latin America and quantify the potential of the telecommunications industry in both regional areas. The work considers different economical and ICT’s variables from different European and Latin America countries datasets (mainly Agenda 21 and other available and global sources) for the period 2002-2012.
    Keywords: ICT Market,Coral Reef Optimization algorithm,K-Means++ Clustering,Investment Opportunities,Europe,Latin America
    Date: 2015
    URL: http://d.repec.org/n?u=RePEc:zbw:itse15:127175&r=cmp
  7. By: CUERVO, Daniel Palhazi; KESSELS, Roselinde; GOOS, Peter; SÖRENSEN, Kenneth
    Abstract: Stated choice experiments are conducted to identify the attributes that drive people's preferences when choosing between competing options of products or services. They are widely used in transportation in order to support the decision making of companies and governmental authorities. A large number of attributes might increase the complexity of the choice task in a choice experiment, and have a detrimental effect on the quality of the results obtained. In order to reduce the cognitive effort required by the experiment, researchers may resort to experimental designs where the levels of some attributes are held constant within a choice situation. These designs are called partial profile designs. In this paper, we propose an integrated algorithm for the generation of D-optimal designs for stated choice experiments with partial profiles. This algorithm optimizes the set of constant attributes and the levels of the varying attributes simultaneously. An extensive computational experiment shows that the designs produced by the integrated algorithm outperform those obtained by existing algorithms, and match the optimal designs that have been analytically derived for a number of benchmark instances. We also evaluate the performance of the algorithm under varying experimental conditions and study the structure of the designs generated.
    Keywords: Stated choice experiments, Multinomial logit model, Partial profiles, (Bayesian) D-optimality, Utility-neutral designs, Coordinate-exchange algorithm
    Date: 2015–01
    URL: http://d.repec.org/n?u=RePEc:ant:wpaper:2015004&r=cmp
  8. By: Li, Chuan-Zhong (Department of Economics); Bali Swain, Ranjula (Department of Economics)
    Abstract: In this paper, we analyze a dynamic stochastic general equilibrium model on how water resilience a¤acts economic growth and dynamic welfare with special reference to South Africa. While water may become a limiting factor for future development in general, as a drought prone and water poor country with rapid population growth, South Africa may face more serious challenges for sustainable development in the future. Using the model, we conduct numerical simulation for di¤erent parameter configurations with varying discount rate, climate change assumption, and the degree of uncertainty in future precipitation. We find that with sufficient capital accumulation, development can still be made sustainable despite of increased future water scarcity and decreased long-run sustianable welfare; While sto- chastic variation in precipitation has a negatively e¤ect on water resilience and the expected dynamic welfare, the effect is mitigated by persistence in the precipitation pattern. With heavier time discounting and lower capital formation, however, the current welfare may not be sustained.
    Keywords: Water resilience; growth; dynamic Welfare; sustainability
    JEL: D60 O40 O55 Q25
    Date: 2014–12–19
    URL: http://d.repec.org/n?u=RePEc:hhs:uunewp:2014_011&r=cmp
  9. By: Ioane Muni Toke; Nakahiro Yoshida
    Abstract: We propose a parametric model for the simulation of limit order books. We assume that limit orders, market orders and cancellations are submitted according to point processes with state-dependent intensities. We propose new functional forms for these intensities, as well as new models for the placement of limit orders and cancellations. For cancellations, we introduce the concept of "priority index" to describe the selection of orders to be cancelled in the order book. Parameters of the model are estimated using likelihood maximization. We illustrate the performance of the model by providing extensive simulation results, with a comparison to empirical data and a standard Poisson reference.
    Date: 2016–02
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1602.03944&r=cmp
  10. By: Gerunov, Anton
    Abstract: This paper utilizes a novel data on consumer choice under uncertainty, obtained in a laboratory experiment in order to gain substantive knowledge of individual decision-making and to test the best modeling strategy. We compare the performance of logistic regression, discriminant analysis, naïve Bayes classifier, neural network, decision tree, and Random Forest (RF) to discover that the RF model robustly registers the highest classification accuracy. This model also reveals that apart from demographic and situational factors, consumer choice is highly dependent on social network effects.
    Keywords: choice, decision-making, social network, machine learning
    JEL: D12 D81
    Date: 2016–01
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:69199&r=cmp

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