麻豆AV

This research grant is awarded jointly to听Patrick Augustin (顿别蝉补耻迟别濒蝉),听Gunnar Grass (HEC Montreal),听Marti G. Subrahmanyam,听Charles E. Merril (New York Stern School of Business)

础产蝉迟谤补肠迟:听

In this project, we propose a framework for identifying option trades that maximize expected returns under liquidity听constraints for investors with private but noisy information. Our primary objective is to improve the detection of听illegal insider trading by understanding how insiders can benefit from trading differentially depending on the type听and quality of their private information, i.e., the precision of its signal. We first implement our approach numerically听for a wide range of option trading strategies and various private signals and then apply it empirically to document听suspicious activity in option markets prior to large changes in stock prices associated with different types of听corporate news events. In most instances, an investor with a private signal about an upcoming change in the price of听the underlying stock maximizes his profits by acquiring options trading near the money, or by pursuing trading听strategies involving more than one security. Differences in trading as a function of signal and parameter uncertainty听improve our understanding of how informed investors trade in the options market.

is interested in encouraging academic research on exchange-traded derivatives and central clearing house policy issues. The Foundation Board has identified the following topics as those of greater interest within three broad-based categories of research: Exchange-Traded Derivative Market Activity; Structure of Derivative Markets; Global Regulation of Derivative Markets. 听

听