nep-cmp New Economics Papers
on Computational Economics
Issue of 2016‒09‒11
nine papers chosen by
Stan Miles
Thompson Rivers University

  1. Comparing the Impacts of Financial Regulation in Australia and the United States via Simulation with Country-specific Financial CGE Models By J. Nassios; James A. Giesecke; Maureen T. Rimmer; Peter B. Dixon
  2. Large scale simulation of synthetic markets By Luca Gerardo-Giorda; Guido Germano; Enrico Scalas
  3. Poverty and Shared Prosperity Implications of Reducing Trade Costs Through Deep Integration in Eastern and Southern Africa By Edward J. Balistreri; Maryla Maliszewska; Israel Osorio-Rodarte; David G. Tarr; Hidemichi Yonezawa
  4. Economic Based Neural Control Switching of TCR and TSC for Optimal Reactive Power Flow and Harmonic Minimization with Fuzzy-Genetic By Mirzaei, Farzad; Ashkaboosi, Farnoosh; Mahdavi, Sadegh
  5. Taxing financial transactions in fundamentally heterogeneous markets By Edoardo Gaffeo; Massimo Molinari
  6. Genetic Algorithm Learning in a New Keynesian Macroeconomic Setup By Hommes, C.H.; Makarewicz, T.A.; Massaro, D.; Smits, T.
  7. Networks of Heterogeneous Expectations in an Asset Pricing Market By Makarewicz, T.A.
  8. A General Endogenous Grid Method for Multi-Dimensional Models with Non-Convexities and Constraints By Jeppe Druedahl; Thomas Høgholm Jørgensen
  9. Algorithmic and High-Frequency Trading Strategies: A Literature Review By Alexandru Mandes

  1. By: J. Nassios; James A. Giesecke; Maureen T. Rimmer; Peter B. Dixon
    Abstract: Beginning with Johansen (1960), computable general equilibrium (CGE) models have been widely applied to study the impact of a variety of economic issues of interest to policy makers. These include changes in taxes and tariffs, changes in labour force demographics and skill levels, the impact of epidemics and terrorist attacks, the impact of drought and water policy reform, and the economic costs of climate change mitigation (Dixon and Parmenter (1996); Dixon and Rimmer (2002); Adams (2007); Giesecke et al. (2015); Wittwer and Dixon (2015)). Despite the efficacy of CGE models as tools in policy analysis, key linkages between the real and financial economies are often treated implicitly; for example, the current account deficit is assumed to be financed in full by a foreign agent, e.g., via a small country assumption. In an explicit sense we may ask how the foreign investor chooses to finance a deficit, e.g., do they prefer to purchase domestic agent bonds, equity or a combination of the two instruments? What are the associated implications for relative rates]ofreturn across the suite of domestic financial instruments, and how do changes in relative returns affect domestic agent investment decisions, nominal exchange rates, and the real economy? This paper seeks to address such questions via the development of a theory of the financial sector for a traditional dynamic CGE model of the U.S. (USAGE 2.0). We begin with a brief synopsis of the construction of a financial database for the United States (U.S.), which documents the stocks and transactional flows of 5 financial instruments across 11 distinct agents. The financial database derived herein and the approach documented in Dixon et al. (2015), are then used to develop a new financial CGE model of the U.S. called USAGE2F. Explicit recognition of financial stocks and flows broadens the scope of CGE analyses to include the effects of changes in capital adequacy requirements of key financial agents, e.g., the commercial banks, as we illustrate with an example. The results are subsequently compared to findings of a similar policy scenario in Australia, which are outlined in Giesecke et al. (2016). This analysis serves to illustrate how the impacts of regulatory change (in this case, a rise in capital adequacy ratios) can be affected by jurisdiction]specific differences in the structure of the financial sector.
    Keywords: Capital adequacy ratio, financial stability, financial CGE model
    JEL: E17 E44 G21 C68
    Date: 2016–08
  2. By: Luca Gerardo-Giorda; Guido Germano; Enrico Scalas
    Abstract: High-frequency trading has been experiencing an increase of interest both for practical purposes within financial institutions and within academic research; recently, the UK Government Office for Science reviewed the state of the art and gave an outlook analysis. Therefore, models for tick-by-tick financial time series are becoming more and more important. Together with high-frequency trading comes the need for fast simulations of full synthetic markets for several purposes including scenario analyses for risk evaluation. These simulations are very suitable to be run on massively parallel architectures. Aside more traditional large-scale parallel computers, high-end personal computers equipped with several multi-core CPUs and general-purpose GPU programming are gaining importance as cheap and easily available alternatives. A further option are FPGAs. In all cases, development can be done in a unified framework with standard C or C++ code and calls to appropriate libraries like MPI (for CPUs) or CUDA for (GPGPUs). Here we present such a prototype simulation of a synthetic regulated equity market. The basic ingredients to build a synthetic share are two sequences of random variables, one for the inter-trade durations and one for the tick-by-tick logarithmic returns. Our extensive simulations are based on several distributional choices for the above random variables, including Mittag-Leffler distributed inter-trade durations and alpha-stable tick-by-tick logarithmic returns.
    JEL: J1
    Date: 2015
  3. By: Edward J. Balistreri (Division of Economics and Business, Colorado School of Mines); Maryla Maliszewska (The World Bank); Israel Osorio-Rodarte; David G. Tarr; Hidemichi Yonezawa (ETH-Zurich)
    Abstract: Evidence indicates that trade costs are a much more substantial barrier to trade than tariffs, especially in sub-Saharan Africa. We decompose trade costs into: (i) trade facilitation; (ii) non-tariff barriers; and (iii) the costs of business services. Our paper is the first CGE-microsimulation model to assess the poverty and shared prosperity impacts of the reduction of trade costs. We examine policies to reduce trade costs in: (i) the "Tripartite" FTA among COMESA, SADC and the East African Customs Union (EACU); (ii) within the EACU alone; and (iii) unilaterally by the EACU. Our CGE model contains imperfect competition and foreign direct investment, which allows us to assess the poverty effects of services liberalization. We find that there are significant reductions in the poverty headcount, the percentage of the population living in poverty and increases in the incomes of the bottom forty percent of the population for all six of our African regions from deep integration in the Tripartite FTA or comparable unilateral reforms by the EACU. Despite the uniform increases in income for the poorest 40 percent, we find that trade facilitation tends to increase the share of income captured by the poorest 40 percent of the population, while services reform decreases the share. We find that the estimated gains vary considerably across countries and reforms. Thus, countries would have an interest in negotiating for different reforms in different agreements.
    Keywords: poverty head count, shared prosperity, microsimulation, CGE, trade facilitation, trade costs, services liberalization, non-tariff barriers, regional integration, Tripartite Free Trade, foreign direct investment
    JEL: F14 F15 F17 O55 F55
    Date: 2016–09
  4. By: Mirzaei, Farzad; Ashkaboosi, Farnoosh; Mahdavi, Sadegh
    Abstract: Optimal Reactive Power Flow (ORPF) for improving voltage profile and power loss reduction is very important in power system planning; though its method, constraints, and quality of compensation are very effective. Value of compensator, transformer tap ratio, and generator voltages are assumed as controlling variables. Usually this optimization is accompanied by harmonic production. The most important parameter of reactive power compensators is minimum production of harmonics. Nowadays by considering the improvement of power systems in power quality and the importance of harmonics in power quality, compensators by minimum harmonic distortion should be designed. In this paper, ORPF is executed in two stages. At First stage, a genetic algorithm with a fuzzy fitness model employed to solve this multi objective optimization problem. The entire discrete controlling variable is assumed discretely as their natures in all steps of this stage. Outputs of this stage are values of controlling variable that include compensations values. In Second stage, compensation considering the minimum harmonic production is applied. The issue of harmonic reduction in determining the fire angle of TCR and TSC, that are very important in FACTs, is proposed. Determination of optimum angles for minimizing the total harmonic distortion (THD) is investigated and finally for faster control and decision, Artificial Neural Network (ANN) has been used and satisfactory results have been obtained and to have minimum THD, existence of maximum possible capacitors, if bank of capacitors are employed, for both negative and positive reactive power is calculated.
    Keywords: Genetic Algorithm, Fuzzy membership, ANN, ORPF, FACTs, Fire angle, THD.
    JEL: L00
    Date: 2016–09–01
  5. By: Edoardo Gaffeo; Massimo Molinari
    Abstract: The recent global financial crisis has revived a well-honored debate on the desirability and feasibility of taxing financial activities to curb speculation and promote price stability. In this paper we apply agent-based computational techniques to explore this issue in a multi-market environment in which the processes driving the fundamental value of the securities traded in different jurisdictions are heterogeneous. A natural exemplification is to assume that security dealers have the opportunity of submitting orders by choosing among stock markets at different stages of development. We argue that the proper policy objective to be targeted is not volatility in itself, but that in excess of the discounted stream of subsequent dividends, that is price efficiency. In this case, a global coordination is incentive-compatible, given that it minimizes the distortion associated to speculative trading on the one hand, and it ensures that the loss of trading volume is lower if compared to the case of unilateral taxation on the other one. Notwithstanding a fundamental heterogeneity of the markets involved, the optimal tax rate turns out to be uniform.
    Keywords: agent-based models; financial transaction tax; heterogeneous traders
    JEL: C63 D53 G18
    Date: 2016
  6. By: Hommes, C.H. (University of Amsterdam); Makarewicz, T.A. (University of Amsterdam); Massaro, D. (University of Amsterdam); Smits, T. (SEO Economic Research)
    Abstract: In order to understand heterogeneous behaviour amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a genetic algorithms (GA) model to replicate the results from their LtF experiment. In this GA model individuals optimise an adaptive, a trend following and an anchor coefficient in a population of general prediction heuristics. We replicate experimental treatments in a New-Keynesian environment with increasing complexity and use Monte Carlo simulations to investigate how well the model explains the experimental data. We find that the model is able to replicate the three different types of behaviour in the treatments using one GA model. The research furthermore shows that heterogeneous behaviour can be explained by an adaptive, anchor and trend extrapolating component and therewith contributes to the existing literature in the way that GA can be used to explain heterogeneous behaviour in LtF experiments with different types of complexity.
    Date: 2015
  7. By: Makarewicz, T.A. (University of Amsterdam)
    Abstract: The paper studies the e ect of information networks on learning to forecast in an asset pricing market. Financial traders have heterogeneous price expectations, are influenced by friends and seem to be prone to herding. However, in laboratory experiments subjects use contrarian strategies. Theoretical literature on learning in networks is scarce and cannot explain this conundrum (Panchenko et al., 2013). The paper follows Anufriev et al. (2014) and investigates an agent-based model, in which agents forecast price with a simple general heuristic: adaptive and trend extrapolation expectations, with an additional term of (dis-)trust towards their friends' mood. Agents independently use Genetic Algorithms to optimize the parameters of the heuristic. The paper considers friendship networks of symmetric (regular lattice, fully connected) and asymmetric architecture (random, rewired, star). The main finding is that the agents learn contrarian strategies, which amplifies market turn-overs and hence price oscillations. Nevertheless, agents learn similar behavior and their forecasts remain well coordinated. The model therefore o ers a natural interpretation for the di erence between the experimental stylized facts and market surveys.
    Date: 2015
  8. By: Jeppe Druedahl (Department of Economics, University of Copenhagen); Thomas Høgholm Jørgensen (Department of Economics, University of Copenhagen)
    Abstract: The endogenous grid method (EGM) significantly speeds up the solution of stochastic dynamic programming problems by simplifying or completely eliminating rootfinding. We propose a general and parsimonious EGM extended to handle 1) multiple continuous states and choices, 2) multiple occasionally binding constraints, and 3) non-convexities such as discrete choices. Our method enjoys the speed gains of the original one-dimensional EGM, while avoiding expensive interpolation on multi-dimensional irregular endogenous grids. We explicitly define a broad class of models for which our solution method is applicable, and illustrate its speed and accuracy using a consumption-saving model with both liquid assets and illiquid pension assets and a discrete retirement choice.
    Keywords: Endogenous grid method, post-decision states, stochastic dynamic programming, continuous and discrete choices, occasionally binding constraints
    JEL: C13 C63 D91
    Date: 2016–09–05
  9. By: Alexandru Mandes (University of Giessen)
    Abstract: The advances in computer and communication technologies have created new opportunities for improving, extending the application of or even developing new trading strategies. Transformations have been observed both at the level of investment decisions, as well as at the order execution layer. This review paper describes how traditional market participants, such as market-makers and order anticipators, have been reshaped and how new trading techniques relying on ultra-low-latency competitive advantage, such as electronic “front running”, function. Also, the natural conflict between liquidity-consumers and liquidity-suppliers has been taken to another level, due to the proliferation of algorithmic trading and electronic liquidity provision strategies.
    Keywords: algorithmic trading, high-frequency trading, electronic market making
    JEL: C10 C61 C63 G19
    Date: 2016

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