Paper Info
Title
Weighted Quantile Regression Forests for Bimodal Distribution Modeling: A Loss Given Default Case.
Abstract
Due to various regulations (e.g., the Basel III Accord), banks need to keep a specified amount of capital to reduce the impact of their insolvency. This equity can be calculated using, e.g., the Internal Rating Approach, enabling institutions to develop their own statistical models. In this regard, one of the most important parameters is the loss given default, whose correct estimation may lead to a healthier and riskless allocation of the capital. Unfortunately, since the loss given default distribution is a bimodal application of the modeling methods (e.g., ordinary least squares or regression trees), aiming at predicting the mean value is not enough. Bimodality means that a distribution has two modes and has a large proportion of observations with large distances from the middle of the distribution; therefore, to overcome this fact, more advanced methods are required. To this end, to model the entire loss given default distribution, in this article we present the weighted quantile Regression Forest algorithm, which is an ensemble technique. We evaluate our methodology over a dataset collected by one of the biggest Polish banks. Through our research, we show that weighted quantile Regression Forests outperform "single" state-of-the-art models in terms of their accuracy and the stability.
Year
DOI
Venue
2020
10.3390/e22050545
ENTROPY
Keywords
DocType
Volume
bimodal distribution,loss given default,machine learning,weighted quantile regression forests
Journal
22
Issue
ISSN
Citations 
5
1099-4300
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Michal Gostkowski100.34
Krzysztof Gajowniczek2196.14