Much attention is paid to portfolio variance, but skewness is also important for both portfolio design and asset pricing. We revisit the empirical research on systematic skewness that we initiated 25 years ago. In an out-of-sample test, we find that the risk premium associated with skewness is...

AI/ML models are used for many financial applications ranging from portfolio selection to efficient credit allocation. However, the drawback to applying these models in practice is that performance (i.e., predictive power) is generally inversely related to model complexity. In this chapter, we...

Financial models are an inescapable feature of modern financial markets. Yet it was over reliance on these models and the failure to test them properly that is now widely recognized as one of the main causes of the financial crisis of 2007-2011. Since this crisis, there has been an increase in...

We use option prices to test if monetary policy has a significant impact on stock market participants' expectations. We find that, with loose monetary policy, traded equity put (call) options are significantly cheaper (more expensive) versus tight monetary policy. Higher call option prices...

Hard-information based financial ratios such as financial leverage, debt service coverage ratio, return on assets, etc are important considerations in corporate default risk. The relationship between the usefulness of such financial metrics and soft information such as ESG scores or corporate...

If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model which incorporates conditional skewness. Our results show that conditional skewness helps explain the cross-sectional variation of...

Using account level credit-card data from six major commercial banks from January 2009 to December 2013, we apply machine-learning techniques to combined consumer-tradeline, credit-bureau, and macroeconomic variables to predict delinquency. In addition to providing accurate measures of loss...