连续双拍卖代理模型中基于微观结构的下单

IF 0.3 Q4 BUSINESS, FINANCE
Alexandru Mandes
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引用次数: 8

摘要

这一贡献提出了一种新的订单放置策略,该策略可用于在基于代理的模型框架内模拟连续双拍卖金融市场。考虑相关市场微观结构因素和代理的内在特征,以风险调整后的执行成本最小为优化问题给出下单决策。这个订单提交过程比以前更现实,有助于提高日内市场动态的保真度。结果表明,与随机提交策略相反,高频风格化的事实,如市场价格影响函数的凹形和非价差限价单的幂律衰减相对价格分布被复制。因此,在当前高频交易影响争论激烈的背景下,所得模型可以作为高频交易策略的现实测试环境。不仅可以分析单个交易策略的影响,还可以分析多个共存策略的相互依赖关系和全局紧急行为。此外,尚未在实际市场条件下经受考验的创新监管政策,也可以接受检验。增强的内容可用,详见PDF。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Microstructure-Based Order Placement in a Continuous Double Auction Agent Based Model
This contribution proposes a novel order placement strategy which can be used for simulating continuous double auction financial markets, within an agent-based model framework. The order placement decision is given by an optimization problem which minimizes the risk adjusted execution cost, taking into consideration relevant market microstructure factors and intrinsic agent characteristics. This order submission process is more realistic than has been done previously and contributes to a higher fidelity of the intraday market dynamics. The results show that, as opposed to random submission strategies, high-frequency stylized facts such as the concave shape of the market price impact function and the power-law decaying relative price distribution of off-spread limit orders are replicated. Therefore, the resulting model can be used as a realistic test environment for high-frequency trading strategies, in the context of the current, heated debate over the impact of high-frequency trading. Not only the impact of individual trading strategies can be analyzed, but also the interdependencies and the global emergent behavior of multiple coexistent strategies. Moreover, innovative regulatory policies, which have not been tested yet under real market conditions, could be inspected.Enhanced content available, see PDF for details.
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来源期刊
Algorithmic Finance
Algorithmic Finance BUSINESS, FINANCE-
CiteScore
0.40
自引率
0.00%
发文量
6
期刊介绍: Algorithmic Finance is both a nascent field of study and a new high-quality academic research journal that seeks to bridge computer science and finance. It covers such applications as: High frequency and algorithmic trading Statistical arbitrage strategies Momentum and other algorithmic portfolio management Machine learning and computational financial intelligence Agent-based finance Complexity and market efficiency Algorithmic analysis of derivatives valuation Behavioral finance and investor heuristics and algorithms Applications of quantum computation to finance News analytics and automated textual analysis.
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