摘要翻译:
介绍了基于Agent的计算经济市场(ABCEM)模型的仿真工具SABCEMM(基于Agent的计算经济市场模型仿真器)。我们的仿真工具是用C++实现的,我们可以轻松地在数百万个代理中运行ABCEM模型。面向对象的软件设计使得ABCEM模型的构建块能够独立地实现,例如agent类型和市场机制。通过使用基于XML的SABCEMM配置文件重组现有的构建块,用户可以在统一的环境中设计和比较ABCEM模型。我们介绍了一个抽象的ABCEM模型类,我们的仿真工具是建立在它的基础上的。此外,我们还介绍了SABCEMM的软件体系结构和计算方面。在这里,我们关注SABCEMM的效率与我们的模拟运行时间。我们展示了不同的随机数发生器对ABCEM模型运行时间的巨大影响。代码和文档发布在GitHub上https://GitHub.com/sabcemm/sabcemm,因此读者可以复制所有结果。
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英文标题:
《SABCEMM-A Simulator for Agent-Based Computational Economic Market Models》
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作者:
Torsten Trimborn, Philipp Otte, Simon Cramer, Max Beikirch, Emma
Pabich, Martin Frank
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最新提交年份:
2018
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分类信息:
一级分类:Quantitative Finance 数量金融学
二级分类:Computational Finance 计算金融学
分类描述:Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法,包括蒙特卡罗,偏微分方程,格子和其他数值方法,并应用于金融建模
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一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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一级分类:Quantitative Finance 数量金融学
二级分类:General Finance 一般财务
分类描述:Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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一级分类:Quantitative Finance 数量金融学
二级分类:Trading and Market Microstructure 交易与市场微观结构
分类描述:Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构,流动性,交易和拍卖设计,自动化交易,基于代理的建模和做市
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英文摘要:
We introduce the simulation tool SABCEMM (Simulator for Agent-Based Computational Economic Market Models) for agent-based computational economic market (ABCEM) models. Our simulation tool is implemented in C++ and we can easily run ABCEM models with several million agents. The object-oriented software design enables the isolated implementation of building blocks for ABCEM models, such as agent types and market mechanisms. The user can design and compare ABCEM models in a unified environment by recombining existing building blocks using the XML-based SABCEMM configuration file. We introduce an abstract ABCEM model class which our simulation tool is built upon. Furthermore, we present the software architecture as well as computational aspects of SABCEMM. Here, we focus on the efficiency of SABCEMM with respect to the run time of our simulations. We show the great impact of different random number generators on the run time of ABCEM models. The code and documentation is published on GitHub at https://github.com/SABCEMM/SABCEMM, such that all results can be reproduced by the reader.
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PDF链接:
https://arxiv.org/pdf/1801.01811