I am not a fan of articles where the authors use widgets and other unrelatable examples to illustrate complex concepts. Here I will illustrate the use of copulas in finance using the example of risk aggregation to drive through the points. First, though, it is important to briefly explain the risk aggregation problem. There are plenty of texts available on the internet which go in great detail about the inner workings of copulas. My goal is to help the reader develop an intuitive understanding of how copulas are used in finance. Continue reading Understanding Correlations and Copulas in Finance: An Application in Risk Portfolio Aggregation using R
Category: Topics in Risk Management
Machine Learning and Credit Risk (part 6) – Multi-class Linear Discriminate Analysis
Linear Discriminate Analysis (LDA) is another method which should be familiar to statisticians and economists. LDA is a dimensionality reduction technique which has found its use in machine learning because of how well it functions as a classifier. Its primary goal is to project data onto a lower dimensional space. Continue reading Machine Learning and Credit Risk (part 6) – Multi-class Linear Discriminate Analysis
Machine Learning and Credit Risk (part 5) – Neural Networks
Neural networks models are a flexible class of machine learning algorithms which can be used for both supervised as well as unsupervised learning and can approximate discrete or continuous functions. They are loosely modeled on the functions of human brains and attempt to allow computers to learn in manner similar to humans. Continue reading Machine Learning and Credit Risk (part 5) – Neural Networks
Machine Learning and Credit Risk (part 4) – Support vector Machines
Support Vector Machines (SVM) algorithms are some of the best “out-of-box” machine learning tools available. They are not only used for both linear and nonlinear classifications but can also be extended from binary classification to support multi-class classification. Continue reading Machine Learning and Credit Risk (part 4) – Support vector Machines