Investigating competition in financial markets: a sparse autologistic model for dynamic network data

Investigating competition in financial markets: a sparse autologistic model for dynamic network data
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调查金融市场的竞争:动态网络数据的稀疏自逻辑模型

DOI:
10.1080/02664763.2017.1357684
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发表时间:
2017
影响因子:
1.5
通讯作者:
Boyd, Naomi
Boyd, Naomi
中科院分区:
数学4区
文献类型:
--
作者:
Betancourt, Brenda;Rodríguez, Abel;Boyd, Naomi

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我们开发了一个稀疏的自定义模型来研究多样化和脱媒策略在金融交易网络演变中的影响。为了在模型估计中引入稀疏性并解决有关潜在过程的实质性问题,该模型包含了非正则化惩罚。这使得实现对于复杂的动态网络是可行的,其中随着时间的推移,参数的数量大大大于观测的数量。我们使用该模型来描述纽约商品交易所天然气期货市场的交易者行为,我们发现非中介化而不是多样化或动量倾向于驱动市场微观结构。
We develop a sparse autologistic model for investigating the impact of diversification and disintermediation strategies in the evolution of financial trading networks. In order to induce sparsity in the model estimates and address substantive questions about the underlying processes the model includes anregularization penalty. This makes implementation feasible for complex dynamic networks in which the number of parameters is considerably greater than the number of observations over time. We use the model to characterize trader behavior in the NYMEX natural gas futures market, where we find that disintermediation and not diversification or momentum tend to drive market microstructure.
指数随机图模型 (ERGM) 的扩展
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