New Economics Papers
on Computational Economics
Issue of 2014‒03‒15
ten papers chosen by

  1. A Historical CGE Simulation of the South African Economy from 2006–2013: Analysing Changes in the Use of Primary Factors by Industries By Heinrich R. Bohlmann and Martin C. Breitenbach
  2. Diffusion and adoption of dynamic electricity tariffs: An agent-based modeling approach By Anna Kowalska-Pyzalska; Katarzyna Maciejowska; Katarzyna Sznajd-Weron; Rafal Weron
  3. Multi-period Trading Prediction Markets with Connections to Machine Learning By Jinli Hu; Amos Storkey
  4. A Multi-Factor Model of Heterogeneous Traders in a Dynamic Stock Market By Pyo, Dong-Jin
  5. CGE modeling of the impact of skilled labor movements in ASEAN Economic Community focusing on telecommunication industry By Sudtasan, Tatcha; Suriya, Komsan
  6. A reinforcement learning extension to the Almgren-Chriss model for optimal trade execution By Dieter Hendricks; Diane Wilcox
  7. A Simulation of the Illegal Coal Mining in Quang Ninh Province, Vietnam using Vensim By Phan, Tuan
  8. To bail-out or to bail-in? Answers from an agent-based model By Peter Klimek; Sebastian Poledna; J. Doyne Farmer; Stefan Thurner
  9. A consentaneous agent based and stochastic model of the financial markets By V. Gontis; A. Kononovicius
  10. A review of electricity price forecasting: The past, the present and the future By Rafal Weron

  1. By: Heinrich R. Bohlmann and Martin C. Breitenbach
    Abstract: This paper uses a dynamic CGE model to help explain some apparent contradictions between changes in the structure of the South African economy and movements in related variables over the 2006 to 2013 period. Most notably, an increase in the capital-labour ratio was identified, despite a relative increase in the price of capital rentals. To calibrate this result with conventional economic theory suggests that a change in the preferred capital-labour ratio of industries must have occurred. We quantify this change and comment on what this means for policymakers trying to reduce the country’s high level of unemployment. Other changes to the economy over this period are also quantified and explained.
    Keywords: CGE Simulation, South African Economy, Analysing changes, Primary Factors
    JEL: F21 F23
    Date: 2014
  2. By: Anna Kowalska-Pyzalska; Katarzyna Maciejowska; Katarzyna Sznajd-Weron; Rafal Weron
    Abstract: This paper proposes an agent-based modeling (ABM) approach to study the diffusion and adoption of dynamic electricity tariffs. We discuss the difference between opinions and decisions of electricity consumers regarding dynamic pricing. By means of a simple ABM, we provide a plausible explanation for the observed in retail electricity markets discrepancy between the relatively high willingness to adopt dynamic tariffs and the actual low adoption rate.
    Keywords: Dynamic electricity tariffs; Intetnion-behavior gap; Innovation diffusion; Agent-based model
    JEL: C63 O33 Q48 Q55
    Date: 2014–01–22
  3. By: Jinli Hu; Amos Storkey
    Abstract: We present a new model for prediction markets, in which we use risk measures to model agents and introduce a market maker to describe the trading process. This specific choice on modelling tools brings us mathematical convenience. The analysis shows that the whole market effectively approaches a global objective, despite that the market is designed such that each agent only cares about its own goal. Additionally, the market dynamics provides a sensible algorithm for optimising the global objective. An intimate connection between machine learning and our markets is thus established, such that we could 1) analyse a market by applying machine learning methods to the global objective, and 2) solve machine learning problems by setting up and running certain markets.
    Date: 2014–03
  4. By: Pyo, Dong-Jin
    Keywords: Heterogeneous traders; Agent-based Stock Market Model
    JEL: G11 G12 G17
    Date: 2014–03–06
  5. By: Sudtasan, Tatcha; Suriya, Komsan
    Abstract: This paper investigates the impact of skilled labor movements in ASEAN Economic Community (AEC) on nationwide economy of Thailand using Computable General Equilibrium model. The paper mainly focuses on the labor movement in telecommunication industry. The model consists of three steps. First, it simulates the impact of raising minimum wage to THB300 and raising salary of bachelor graduates to THB15,000 across-the-board and over the country according to the Raising Income Policy (RIP) of the Thai government. Second, it figures out the impact of the skilled labor movement in telecommunication sector among AEC member countries. Last, it includes the impact of skilled labor movements in 8 occupations that are allowed by the AEC agreement. The results reveal that the RIP causes negative impact to the Thai economy due to the rising costs of production that cannot be compensated by the increasing consumption. Inward skilled labor movement to Thailand in the telecommunication sector leads to the increasing income of engineers and related skilled workers in the country. This yields the positive impact to the economy due to the increasing income of the middle-class people while costs of production do not increase much. The inward skilled labor movements in all 8 occupations will even yield more positive impacts to the Thai economy. However, the positive impacts of the skilled labor movements in AEC cannot compensate the negative impacts of the RIP applied earlier. Therefore, Thailand cannot expect that AEC will boost its economy up to the level before the implementation of RIP.
    Keywords: Computable general equilibrium model; telecommunication industry; ASEAN Economic Community; labor movement; wage policy
    JEL: C68 F15 L96
    Date: 2014–02–24
  6. By: Dieter Hendricks; Diane Wilcox
    Abstract: Reinforcement learning is explored as a candidate machine learning technique to enhance existing analytical solutions for optimal trade execution with elements from the market microstructure. Given a volume-to-trade, fixed time horizon and discrete trading periods, the aim is to adapt a given volume trajectory such that it is dynamic with respect to favourable/unfavourable conditions during realtime execution, thereby improving overall cost of trading. We consider the standard Almgren-Chriss model with linear price impact as a candidate base model. This model is popular amongst sell-side institutions as a basis for arrival price benchmark execution algorithms. By training a learning agent to modify a volume trajectory based on the market's prevailing spread and volume dynamics, we are able to improve post-trade implementation shortfall by up to 10.3% on average compared to the base model, based on a sample of stocks and trade sizes in the South African equity market.
    Date: 2014–03
  7. By: Phan, Tuan
    Abstract: Using Vensim PLE, this paper provides a simulation of the illegal coal mining in Quang Ninh province, Vietnam. Examining the three main loops including need for income effect, government enforcement and coal management effects and other effects (illegal density, technology, community and psychological effects), the paper sketches several scenarios under different levels of the key variables. Obtaining these results, the paper suggests a better scene in terms of socio-economic and environmental sustainability basing on the two major components. First, the government authorities should urge the enforcement and revise the coal management. Second, the community should have more active activities to abolish the illegal mining trend and raise effectively warnings about the danger of the illegal mining. Those parallel implementations shall create a surprisingly positive effect on the reduction of illegal coal mining in the province.
    Keywords: illegal coal mining, simulation, Vietnam
    JEL: Q32 Q38
    Date: 2008–06–06
  8. By: Peter Klimek; Sebastian Poledna; J. Doyne Farmer; Stefan Thurner
    Abstract: Since beginning of the 2008 financial crisis almost half a trillion euros have been spent to financially assist EU member states in taxpayer-funded bail-outs. These crisis resolutions are often accompanied by austerity programs causing political and social friction on both domestic and international levels. The question of how to resolve failing financial institutions under which economic preconditions is therefore a pressing and controversial issue of vast political importance. In this work we employ an agent-based model to study the economic and financial ramifications of three highly relevant crisis resolution mechanisms. To establish the validity of the model we show that it reproduces a series of key stylized facts if the financial and real economy. The distressed institution can either be closed via a purchase & assumption transaction, it can be bailed-out using taxpayer money, or it may be bailed-in in a debt-to-equity conversion. We find that for an economy characterized by low unemployment and high productivity the optimal crisis resolution with respect to financial stability and economic productivity is to close the distressed institution. For economies in recession with high unemployment the bail-in tool provides the most efficient crisis resolution mechanism. Under no circumstances do taxpayer-funded bail-out schemes outperform bail-ins with private sector involvement.
    Date: 2014–03
  9. By: V. Gontis; A. Kononovicius
    Abstract: We consider a three state agent based herding model of the financial markets. From this agent based model we derive a set of stochastic differential equations, which describes underlying macroscopic dynamics of the financial markets. The obtained solution is then subjected to the exogenous noise, which shapes instantaneous return fluctuations. We test both Gaussian and q-Gaussian noise as a source of the short term fluctuations. The resulting model of the return in the financial markets with the same set of parameters reproduces empirical probability and spectral densities of absolute return observed in New York, Warsaw and NASDAQ OMX Vilnius Stock Exchanges. Our result confirms the prevalent idea in behavioral finance that herding interactions may be dominant over agent rationality and contribute towards bubble formation.
    Date: 2014–03
  10. By: Rafal Weron
    Abstract: A variety of methods and ideas have been tried for electricity price forecasting (EPF), with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered.
    Keywords: Electricity price forecasting, Day-ahead market, Seasonality, Autoregression, Neural network, Factor model, Forecasts combination
    JEL: C22 C24 C38 C53 Q47
    Date: 2014–03–10

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