nep-cmp New Economics Papers
on Computational Economics
Issue of 2016‒06‒18
twelve papers chosen by
Stan Miles
Thompson Rivers University

  1. The Problem of Calibrating a Simple Agent-Based Model of High-Frequency Trading By Donovan Platt; Tim Gebbie
  2. A parameter tuning method to analyse the influence of algorithmic parameters in combination with instance characteristics on the quality of a Pareto front By JANSSENS, Jochen; SÖRENSEN, Kenneth; JANSSENS, Gerrit K.
  3. Cowboying Stock Market Herds with Robot Traders By Galimberti, Jaqueson; Suhadolnik, Nicolas; Da Silva, Sergio
  4. Tax-benefit microsimulation modelling in Mozambique : A feasibility study By Antonio Cruz; Helen Barnes; Gemma Wright; Michell Mpike; Vanda Castelo; Michael Noble; Finório Castigo
  5. Detecting Convergence Clubs By Fuat C. Beylunioglu; Thanasis Stengos; Ege Yazgan
  6. Optimal fishing mortalities with age-structured bioeconomic model - a case of NEA mackerel By Ni, Yuanming; Steinshamn, Stein I.
  7. Predicting Financial Distress in Indonesian Manufacturing Industry By Muhammad Rifqi; Yoshio Kanazaki
  8. The Aino 2.0 model By Kilponen, Juha; Orjasniemi, Seppo; Ripatti, Antti; Verona, Fabio
  9. Congestion, Agglomeration, and the Structure of Cities By Brinkman, Jeffrey
  10. Analyzing the Impact of Electricity Market Structure Changes and Mergers: The Importance of Forward Commitments By Brown, David P.; Eckert, Andrew
  11. Local Operators in Kinetic Wealth Distribution By M. Andrecut
  12. Shadow banking, financial regulation and animal spirits: An ACE approach By Krug, Sebastian; Wohltmann, Hans-Werner

  1. By: Donovan Platt; Tim Gebbie
    Abstract: Agent-based models, particularly those applied to financial markets, demonstrate the ability to produce realistic, simulated system dynamics, comparable to those observed in empirical investigations. Despite this, they remain fairly difficult to calibrate due to their tendency to be computationally expensive, even with recent advances in technology. For this reason, financial agent-based models are frequently validated by demonstrating an ability to reproduce well-known log return time series and central limit order book stylized facts, as opposed to being rigorously calibrated to transaction data. We thus apply an established financial agent-based model calibration framework to a simple model of high- and low-frequency trader interaction and demonstrate possible inadequacies of a stylized fact-centric approach to model validation. We further argue for the centrality of calibration to the validation of financial agent-based models and possible pitfalls of current approaches to financial agent-based modeling.
    Date: 2016–06
  2. By: JANSSENS, Jochen; SÖRENSEN, Kenneth; JANSSENS, Gerrit K.
    Abstract: In this paper, the eff?ect of the algorithmic parameters and instance characteristics on the quality of a Pareto front is analysed. The algorithm for which the parameters are evaluated is the variable neighborhood tabu search that is used to solve a multi-objective microzone-based vehicle routing problem. To evaluate the eff?ectiveness of the parameter sett?ings that are used to tune the aforementioned mentioned algorithm, diff?erent performance indices, which evaluate the quality of the Parote set, are used. ?The Promethee method is employed to select the combination of sett?ings that is deemed “the best” by the decision maker.
    Keywords: Multi-criteria decision making, Promethee method, GDSS
    Date: 2016–05
  3. By: Galimberti, Jaqueson; Suhadolnik, Nicolas; Da Silva, Sergio
    Abstract: One explanation for large stock market fluctuations is its tendency to herd behavior. We put forward an agent-based model where instabilities are the result of liquidity imbalances amplified by local interactions through imitation, and calibrate the model to match some key statistics of actual daily returns.We show that an “aggregate market-maker” type of liquidity injection is not successful in stabilizing prices due to the complex nature of the stock market. To offset liquidity shortages, we propose the use of locally triggered contrarian rules, and show that these mechanisms are effective in preventing extreme returns in our artificial stock market.
    Keywords: Herding, Robot trading, Financial regulation, Agent-based model
    JEL: C63 G02
    Date: 2016
  4. By: Antonio Cruz; Helen Barnes; Gemma Wright; Michell Mpike; Vanda Castelo; Michael Noble; Finório Castigo
    Abstract: This paper assesses the feasibility of developing a tax and benefit microsimulation model in Mozambique. Mozambique.s National Development Strategy 2015.35 commits to providing social security to three-quarters of poor and vulnerable households by 2035. Tax.benefit microsimulation can be used to explore ways in which this goal could be achieved as well as the distributional impact of implementing more comprehensive social security arrangements.The paper presents an account of Mozambique.s tax and benefit arrangements as well as a possible underpinning dataset.the Household Budget Survey (Inquérito ao Orçamento Familiar).for a tax and benefit microsimulation model.
    Keywords: Fiscal policy, Revenue, Taxation
    Date: 2016
  5. By: Fuat C. Beylunioglu (Istanbul Bilgi University); Thanasis Stengos (Department of Economics and Finance, University of Guelph); Ege Yazgan (Istanbul Bilgi University)
    Abstract: The convergence hypothesis, which is developed in the context of growth economics, asserts that the income differences across countries are transitory, and developing countries will eventually attain the level of income of developed ones. On the other hand convergence clubs hypothesis claim that the convergence can only be realized across groups of countries that share some common characteristics. In this study, we propose a new method to find convergence clubs that combine pairwise method of testing convergence with maximal clique algorithm. Unlike many of those already developed in the literature, this new method aims to find convergence clubs endogenously without depending on priori classifications. In a Monte Carlo simulation study, the success of the method in finding convergence clubs, is compared with a similar algorithm. Simulation results indicated that the proposed method perform better than the compared algorithm in most cases. In addition to the Monte Carlo, a new empirical evidence on the existence of convergence clubs is presented in the context of real data applications
    Keywords: Growth Economics, Convergence Hypothesis, Convergence Clubs, Maximal Clique Algorithm.
    JEL: C32 O47
    Date: 2016
  6. By: Ni, Yuanming (Dept. of Business and Management Science, Norwegian School of Economics); Steinshamn, Stein I. (Dept. of Business and Management Science, Norwegian School of Economics)
    Abstract: The effects of random environmental impacts on optimal exploitation of a fish population are investigated using both optimization and simulation, based on a discrete-time age-structured bioeconomic model. The optimization problem is solved as a non-linear programming problem in GAMS. First, a basic model structure and 6 different scenarios, dealing with two interactions between fish and environment, are introduced. Based on the simplest scenario, eight different parameter combinations are tested. Then the optimization problem is solved for each of the 6 scenarios for a period of 100 years in order to gain long term insights. The main finding is that higher volatility from the environment leads to higher net profits but together with a lower probability of actually hitting the mean values. Simulations are conducted with different fixed fishing mortality levels under 6 scenarios. It seems that a constant fishing mortality around 0.06 is optimal. In the end, a comparison is made between historical and optimal harvest for a period of 40 years. It turns out that in more than 70% of the time, the optimal exploitation offered by our optimization model dominates the historical one, leading to 43% higher net profit and 34% lower fishing cost on average.
    Keywords: Random environmental impacts; optimal exploitation; non-linear programming
    JEL: C61 Q00 Q20 Q22 Q50
    Date: 2016–05–31
  7. By: Muhammad Rifqi; Yoshio Kanazaki
    Abstract: We attempt to develop and evaluate financial distress prediction models using financial ratios derived from financial statements of companies in Indonesian manufacturing industry. The samples are manufacturing companies listed in Indonesian Stock Exchange during 2003-2011. The models employ two kinds of methods: traditional statistical modeling (Logistic Regression and Discriminant Analysis) and modern modeling tool (Neural Network). We evaluate 23 financial ratios (that measure a company fs liquidity, profitability, leverage, and cash position) and are able to identify a set of ratios that significantly contribute to financial distress condition of the companies in sample group. By utilizing those ratios, prediction models are developed and evaluated based on accuracy and error rates to determine the best model. The result shows that the ratios identified by logistic regression and the model built on that basis is more appropriate than those derived from discriminant analysis. The research also shows that although the best performing prediction model is a neural network model, but we have no solid proof of neural network fs absolute superiority over traditional modeling methods.
    Keywords: financial distress, prediction model, discriminant analysis, logistic regression, neural network.
    Date: 2016–06
  8. By: Kilponen, Juha; Orjasniemi, Seppo; Ripatti, Antti; Verona, Fabio
    Abstract: This paper presents Aino 2.0 – the dynamic stochastic general equilibrium (DSGE) model currently used at the Bank of Finland for forecasting and policy analysis. The paper provides a detailed theoretical description of the model, its estimation and how it can be used to interpret the evolution of the Finnish economy between 1995 and 2014, including the rise and fall of the electronics industry, the global financial crisis, and the stagnant growth performance since the end of the financial crisis.
    Keywords: DSGE model, Finnish economy, small open economy, Bayesian estimation, aggregate shocks
    JEL: C11 C53 E32 E37
    Date: 2016–05–31
  9. By: Brinkman, Jeffrey (Federal Reserve Bank of Philadelphia)
    Abstract: Congestion costs in urban areas are significant and clearly represent a negative externality. Nonetheless, economists also recognize the production advantages of urban density in the form of positive agglomeration externalities. The long-run equilibrium outcomes in economies with multiple correlated but o setting externalities have yet to be fully explored in the literature. Therefore, I develop a spatial equilibrium model of urban structure that includes both congestion costs and agglomeration externalities. I then estimate the structural parameters of the model using a computational algorithm to match the spatial distribution of employment, population, land use, land rents, and commute times in the data. Policy simulations based on the estimates suggest that congestion pricing may have ambiguous consequences for economic welfare.
    Keywords: Congestion; Agglomeration; Externalities; Spatial Equilibrium; Urban Structure; Estimation
    JEL: C51 D62 R13 R40
    Date: 2016–05–10
  10. By: Brown, David P. (University of Alberta, Department of Economics); Eckert, Andrew (University of Alberta, Department of Economics)
    Abstract: We investigate how the effects of market structure changes and mergers in restructured electricity markets depend on the level of forward contracting. Following Bushnell, Mansur, and Saravia (2008), we develop a Cournot model of Alberta's wholesale electricity market that incorporates firms' forward positions. Using data from 2013 - 2014, we estimate the monthly forward positions of the five largest firms in the market, and simulate the effects of different market structure changes, including variations of a hypothetical merger with asset divestitures. We examine the sensitivity of the simulated effects of mergers and other market structure changes to assumptions regarding firms' forward commitments. We demonstrate that the wholesale market impacts of mergers and market structure changes depend critically on firms' forward commitments in the post market structure change equilibrium. Our paper demonstrates the importance of establishing a clear understanding of the size and nature of forward commitments in forecasting the effects of mergers and other market structure changes in wholesale electricity markets.
    Keywords: Electricity; Mergers; Forward Contracts; Market Power
    JEL: D43 L40 L51 L94 Q40
    Date: 2016–06–13
  11. By: M. Andrecut
    Abstract: The statistical mechanics approach to wealth distribution is based on the conservative kinetic multi-agent model for money exchange, where the local interaction rule between the agents is analogous to the elastic particle scattering process. Here, we discuss the role of a class of conservative local operators, and we show that, depending on the values of their parameters, they can be used to generate all the relevant distributions. We also show numerically that in order to generate the power-law tail an heterogeneous risk aversion model is required. By changing the parameters of these operators one can also fine tune the resulting distributions in order to provide support for the emergence of a more egalitarian wealth distribution.
    Date: 2016–05
  12. By: Krug, Sebastian; Wohltmann, Hans-Werner
    Abstract: Over the past decades, the framework for financing has experienced a fundamental shift from traditional bank lending towards a broader market-based financing of financial assets. As a consequence, regulated banks increasingly focus on coping with regulatory requirements meaning that the resulting funding gap for the real economy is left to the unregulated part of the financial system, i.e. to shadow banks highly relying on securitization and repos. Unfortunately, economic history has shown that unregulated financial intermediation exposes the economy to destabilizing externalities in terms of excessive systemic risk. The arising question is now whether and how it is possible to internalize these externalities via financial regulation. We aim to shed light on this issue by using an agent-based computational macro-model as experimental lab. The model is augmented with a shadow banking sector representing an alternative investment opportunity for the real sector which shows animal spirit-like, i.e. highly pro-cyclical and myopic, behavior in its investment decision. We find that an unilateral inclusion of shadow banks into the regulatory framework, i.e. without access to central bank liquidity, has negative effects on monetary policy goals, significantly increases the volatility in growth rates and that its disrupting character materializes in increasing default rates and a higher volatility in the credit-to-GDP gap. However, experiments with a full inclusion, i.e. with access to a lender of last resort, lead to superior outcomes relative to the benchmark without shadow banking activity. Moreover, our results highlight the central role of the access to contagion-free, alternative sources of liquidity within the shadow banking sector.
    Keywords: Shadow Banking,Financial Stability,Monetary Economics,Macroprudential Policy,Financial Regulation,Agent-based Macroeconomics
    JEL: E44 E50 G01 G28 C63
    Date: 2016

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