In this paper, I develop a model in which risk-averse investors possess private information regarding both a stock's expected payoff and its risk. These investors trade in the stock and a derivative whose payoff is driven by the stock's risk. In equilibrium, the derivative is used to speculate...

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory...

Data obtained from monthly Gallup/UBS surveys from 1998-2007 and from a special supplement to the Michigan Surveys of Consumer Attitudes, run in 22 monthly surveys between 2000-2005, are used to analyze stock market beliefs and portfolio choices of household investors. We show that the key...

We develop a structural econometric model to elicit household-specific expectations about future financial asset returns and risk attitudes by using data on observed portfolio holdings and self-assessed willingness to bear financial risk. Our framework assumes that household portfolios are...

We investigate financial experts’ beliefs about climate risk pricing and analyze how those beliefs influence stock return expectations. In a comprehensive survey, we elicit experts’ beliefs using both structured and open-ended questions. We establish that most experts share the view that...

We investigate financial experts' beliefs about climate risk pricing and analyze how those beliefs influence stock return expectations. In a comprehensive survey, we elicit experts' beliefs using both structured and open-ended questions. We establish that most experts share the view that climate...

Financial volatility risk is addressed through a multiple round evolutionary quantum game equilibrium leading to Multifractal Self-Organized Criticality (MSOC) in the financial returns and in the risk dynamics. The model is simulated and the results are compared with financial volatility data