53 citations · 81 across the 5 of their papers we have counts for
9 papers
Unbiased Simulation Estimators for Multivariate Jump-Diffusions
Guanting Chen, Alex Shkolnik, Kay Giesecke
We develop and analyze a class of unbiased Monte Carlo estimators for multivariate jump-diffusion processes with state-dependent drift, volatility, jump intensity and jump size. A…
Explainable Clustering and Application to Wealth Management Compliance
Enguerrand Horel, Kay Giesecke, Victor Storchan +1
Many applications from the financial industry successfully leverage clustering algorithms to reveal meaningful patterns among a vast amount of unstructured financial data. However,…
Computationally Efficient Feature Significance and Importance for Machine Learning Models
Enguerrand Horel, Kay Giesecke
We develop a simple and computationally efficient significance test for the features of a machine learning model. Our forward-selection approach applies to any model specification,…
Significance Tests for Neural Networks
Enguerrand Horel, Kay Giesecke
We develop a pivotal test to assess the statistical significance of the feature variables in a single-layer feedforward neural network regression model. We propose a gradient-based…
Sensitivity based Neural Networks Explanations
Enguerrand Horel, Virgile Mison, Tao Xiong +2
Although neural networks can achieve very high predictive performance on various different tasks such as image recognition or natural language processing, they are often considered…
Deep Learning for Mortgage Risk
Justin Sirignano, Apaar Sadhwani, Kay Giesecke
We develop a deep learning model of multi-period mortgage risk and use it to analyze an unprecedented dataset of origination and monthly performance records for over 120 million mo…
Fluctuation Analysis for the Loss From Default
Konstantinos Spiliopoulos, Justin A. Sirignano, Kay Giesecke
We analyze the fluctuation of the loss from default around its large portfolio limit in a class of reduced-form models of correlated firm-by-firm default timing. We prove a weak co…
Large Portfolio Asymptotics for Loss From Default
Kay Giesecke, Konstantinos Spiliopoulos, Richard B. Sowers +1
We prove a law of large numbers for the loss from default and use it for approximating the distribution of the loss from default in large, potentially heterogenous portfolios. The…
Default clustering in large portfolios: Typical events
Kay Giesecke, Konstantinos Spiliopoulos, Richard B. Sowers
We develop a dynamic point process model of correlated default timing in a portfolio of firms, and analyze typical default profiles in the limit as the size of the pool grows. In o…