This thesis consists of three applications of machine learning techniques to risk management. The first chapter proposes a deep learning approach to estimate physical forward default intensities of companies. Default probabilities are computed using artifi ...
The COVID-19 pandemic has demonstrated the importance and value of multi-period asset allocation strategies responding to rapid changes in market behavior. In this article, we formulate and solve a multi-stage stochastic optimization problem, choosing the ...
When activist shareholders file Schedule 13D filings, the average excess return on target stocks is 6% and stock price volatility drops by about 10%. Prior to filing days, volatility (price) information is reflected in option (stock) prices. Using a compre ...
Process industry firms have thrived in recent decades, but changes in the markets are currently putting both growth and profitability at risk. In this context, inventory management is increasingly viewed as an essential lever for creating a sustainable com ...
This dissertation consists of three chapters. The first chapter examines whether the availability of credit default swaps (CDS) has consequences for creditor governance. CDSs offer creditors the opportunity to hedge credit risk and may impact their willing ...
Electronic intermediaries have become pervasive in sales transactions for many durables, such as cars, power tools, and apartments. Yet only recently have they successfully tackled the challenge of enabling parties to share such goods. A key impediment to ...
We introduce a hybrid discrete choice framework to model the decisions of investors in stock markets. More specifically, we model the decision to buy or sell stocks using a binary logit model with latent classes, characterizing the perception of risk. The ...
The last 2 decades have witnessed a dramatic increase in the use of patent citation data in social science research. Facilitated by digitization of the patent data and increasing computing power, a community of practice has grown up that has developed meth ...
Elliptical distributions are useful for modelling multivariate data, multivariate normal and Student t distributions being two special classes. In this paper, we provide a definition for the elliptical tempered stable (ETS) distribution based on its charac ...
The dramatic rise of time-series data produced in a variety of contexts, such as stock markets, mobile sensing, sensor networks, data centre monitoring, etc., has fuelled the development of large-scale distributed real-time computation systems (e.g., Apach ...
