We estimate investors' sentiment from option and stock prices by anchoring objective beliefs to a neoclassical pricing kernel. Our estimates of sentiment correlate well with other sentiment measures such as the Baker–Wurgler index, the Yale/Shiller crash confidence index and the Duke/CFO...

We develop statistical methods to detect informed trading in options markets. We apply these methods to 31 companies from various sectors over 14 years analyzing approximately 9.6 million option prices. We find that option informed trading tends to cluster prior to certain events, takes place...

This appendix extends the empirical results in Chesney, Crameri, and Mancini (2011). Informed trading activities on put and call options are analyzed for 19 companies in the banking and insurance sectors from January 1996 to September 2009. Our empirical findings suggest that certain events such...

We propose a novel class of models in which the crash hazard rate is determined by a function of a non-local estimation of mispricing. Rooted in behavioral finance, the non-local estimation embodies in particular the characteristic of "anchoring" on past price levels and the "probability...

This online appendix extends the empirical analysis in the main paper, and provides diagnostic tests, robustness checks and additional regression results.The paper "Estimating Sentiment, Risk Aversion, and Time Preference from Behavioral Pricing Kernel Theory" to which these Appendices apply is...

Following Levy and Roll [2010], we posit that the market portfolio is the efficient tangent Markowitz portfolio, i.e., it is mean-variance efficient. We then reverse engineer the expected returns and variance terms with constraints imposed by empirical data on a hierarchy of asset baskets. This...