金融市场自适应Farmer-Joshi代理模型的校准
Calibrating an adaptive Farmer-Joshi agent-based model for financial markets
摘要：
我们复制有争议的校准的农民和乔希代理的金融市场的模型，使用遗传算法和Nelder-Mead阈值接受算法法布雷蒂。Farmer-Joshi模型的新颖之处在于，动态仅由贸易进入和退出门槛驱动。我们恢复了已知的主张，即在金融市场中观察到的一些重要的程式化事实在校准下不容易找到--特别是那些与价格波动的绝对值的自相关性和足够的峰度有关的事实。然而，而不是有关的校准方法的关注，这里的新颖之处在于，我们扩展了农民乔希模型，包括代理适应使用布洛克和霍姆斯的方法，以策略适应的基础上交易策略盈利能力。我们称之为自适应Farmer-Joshi模型：该模型允许交易代理在策略之间切换，通过支持在一段时间内更有利可图的策略，这些策略由固定利润监控时间范围的自由参数确定。在自适应模型中，我们能够校准和恢复额外的程式化的事实，尽管明显的简并。这是通过将贸易进入水平与贸易战略转换的相互作用相结合来实现的。我们用这一点来论证，对于跨天的低频交易，根据每日采样数据进行校准，反馈可以通过基于中期盈利能力的策略消亡来解释;我们发现，平均交易监测范围约为两到三个月（或40到60天）的交易。
英文摘要：
We replicate the contested calibration of the Farmer and Joshi agent based model of financial markets using a genetic algorithm and a Nelder-Mead with threshold accepting algorithm following Fabretti. The novelty of the Farmer-Joshi model is that the dynamics are driven by trade entry and exit thresholds alone. We recover the known claim that some important stylized facts observed in financial markets cannot be easily found under calibration -- in particular those relating to the auto-correlations in the absolute values of the price fluctuations, and sufficient kurtosis. However, rather than concerns relating to the calibration method, what is novel here is that we extended the Farmer-Joshi model to include agent adaptation using an Brock and Hommes approach to strategy fitness based on trading strategy profitability. We call this an adaptive Farmer-Joshi model: the model allows trading agents to switch between strategies by favouring strategies that have been more profitable over some period of time determined by a free-parameter fixing the profit monitoring time-horizon. In the adaptive model we are able to calibrate and recover additional stylized facts, despite apparent degeneracy's. This is achieved by combining the interactions of trade entry levels with trade strategy switching. We use this to argue that for low-frequency trading across days, as calibrated to daily sampled data, feed-backs can be accounted for by strategy die-out based on intermediate term profitability; we find that the average trade monitoring horizon is approximately two to three months (or 40 to 60 days) of trading.
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