We develop a methodology to measure the expected loss of commercial banks in a market downturn, which we call stressed expected loss (SEL). We simulate a market downturn as a negative shock on interest rate and credit market risk factors that reflect the b ...
This article shows that the inability to use monetary policy for macroeconomic stabilization leaves a government more vulnerable to a rollover crisis. We study a sovereign default model with self-fulfilling rollover crises, foreign currency debt, and nomin ...
Many transportation markets are characterized by oligopolistic competition. In these markets customers, suppliers and regulators make decisions that are influenced by the preferences and the decisions of all other agents. In particular, capturing and under ...
This thesis consists of three applications of machine learning techniques to empirical asset pricing.
In the first part, which is co-authored work with Oksana Bashchenko, we develop a new method that detects jumps nonparametrically in financial time series ...
The green bond market's rapid growth has alerted issuers and investors to this sustainable area of investment. This study ascertains whether green bonds are priced lower than conventional bonds-whether a negative green bond premium exists in both Chinese a ...
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 ...
Classical theory asserts that the formation of prices is the result of aggregated decisions of
economics agent such as households or corporation. However central banks are very important
agents that have often been neglected in asset pricing models. Centra ...
We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. Our method learns the features necessary for an effective low-dimensi ...
This thesis uses machine learning techniques and text data to investigate the relationships that arise between the Fed and financial markets, and their consequences for asset prices.The first chapter, entitled Market Expectations and the Impact of Unconv ...
