We study two questions related to competition on the OTC CDS market using data collected as part of the EMIR regulation.
First, we study the competition between central counterparties through collateral requirements. We present models that successfully estimate the initial margin requirements. However, our estimations are not precise enough to use them as input to a predictive model for CCP choice by counterparties in the OTC market.
Second, we model counterpart choice on the interdealer market using a novel semi-supervised predictive task. We present our methodology as part of the literature on model interpretability before arguing for the use of conditional entropy as the metric of interest to derive knowledge from data through a model-agnostic approach. In particular, we justify the use of deep neural networks to measure conditional entropy on real-world datasets. We create the $\textit{Razor entropy}$ using the framework of algorithmic information theory and derive an explicit formula that is identical to our semi-supervised training objective. Finally, we borrow concepts from game theory to define $\textit{top-k Shapley values}$. This novel method of payoff distribution satisfies most of the properties of Shapley values, and is of particular interest when the value function is monotone submodular. Unlike classical Shapley values, top-k Shapley values can be computed in quadratic time of the number of features instead of exponential. We implement our methodology and report the results on our particular task of counterpart choice.
Finally, we present an improvement to the $\textit{node2vec}$ algorithm that could for example be used to further study intermediation. We show that the neighbor sampling used in the generation of biased walks can be performed in logarithmic time with a quasilinear time pre-computation, unlike the current implementations that do not scale well.
Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble
Miami University（迈阿密大学）；University of Pittsburgh（匹兹堡大学）；Northern Arizona University（北亚利桑那大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Trine University（特里恩大学）；Boston University（波士顿大学）
Enhancing Credit Default Prediction Using Boruta Feature Selection and DBSCAN Algorithm with Different Resampling Techniques
Department of Statistics, Western Michigan University（统计系，西部密歇根大学）；Department of Biostatistics and Data Science, Celia Scott Weatherhead School of Public Health and Tropical Medicine, Tulane University（生物统计学与数据科学系，Celia Scott Weatherhead公共卫生与热带医学学院，路易斯安那大学塔金斯分校）；School of Mathematical and Statistical Science, College of Sciences, University of Texas Rio Grande Valley（数学与统计科学学院，德克萨斯里奥格兰德谷大学）；Department of Economics, Western Michigan University（经济学系，西部密歇根大学）；Department of Economics, University of Ottawa（经济学系，渥太华大学）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Carnegie Mellon University（卡内基梅隆大学）；Ant Group（蚂蚁集团）
In financial credit scoring, loan applications may be approved or rejected. We can only observe default/non-default labels for approved samples but have no observations for rejected samples, which leads to missing-not-at-random selection bias. Machine learning models trained on such biased data are inevitably unreliable. In this work, we find that the default/non-default classification task and the rejection/approval classification task are highly correlated, according to both real-world data study and theoretical analysis. Consequently, the learning of default/non-default can benefit from rej…
STRIKE: Additive Feature-Group-Aware Stacking Framework for Credit Default Prediction
Science Academy, University of Maryland, College Park MD, USA（科学学院，马里兰大学，哥伦比亚公园MD，美国）；Department of Computer Science, University of Maryland, College Park MD, USA（计算机科学系，马里兰大学，哥伦比亚公园MD，美国）
Unifund CCR, LLC（Unifund CCR有限责任公司）；Pamplin College of Business, Virginia Polytechnic Institute（弗吉尼亚理工学院庞普林商学院）；College of Business, University of Cincinnati（辛辛那提大学商学院）
