Consortium for Data Analytics in Risk - CDAR

Tomorrow, 11 am @ UC Berkeley ”Minimum Conditional Expected Drawdown Portfolios” by Alex Papanicolaou. From the abstract:"In this talk, I will present ongoing work aimed at computations for Conditional Expected Drawdown, a recently developed extreme risk measure on maximum drawdown, look at risk-based asset allocation under CED and how it compares with other risk measures, CED risk attribution, and more" http://bit.ly/2o2cLuL

Feb 21, 11am @ UC Berkeley**
Paul Kaplan, Morningstar: A Popularity Asset Pricing Model

Abstract: This paper presents a formal model for theory of popularity as laid out informally by Idzorek and Ibbotson in their seminal paper, “Dimensions of Popularity (Journal of Portfolio Management, 2014). The paper does this by extending the capital asset pricing model (CAPM) to include security characteristics that different investors regard differently. This leads to an equilibrium in which: 1) The expected excess return on each security is a linear function of its beta and its popularity loadings which measure the popularity of the security based on its characteristics relative to the those of the beta-adjusted market portfolio; 2) Each investor holds a different portfolio based on his attitudes toward security characteristics; and 3) The market portfolio is not on the efficient frontier. I call this extended model the Popularity Asset Pricing Model, or PAPM for short.

**CDAR hosts Risk Seminars and other learning opportunities that are free to the public. More info @ http://cdar.berkeley.edu/events/

Upcoming Events – CDAR This paper presents a formal model for theory of popularity as laid out informally by Idzorek and Ibbotson in their seminal paper, “Dimensions of Popularity (Journal of Portfolio Management, 2014). The paper does this by extending the capital asset pricing model (CAPM) to include security characteri...

November 29 @ 11:00 am - 1:00 pm, 639 Evans Hall at UC Berkeley***

CDAR Co-Director Robert M. Anderson speaks on PCA with Model Misspecification.

Principal Component Analysis (PCA) relies on the assumption that the data being analyzed is IID over the estimation window. PCA is frequently applied to financial data, such as stock returns, despite the fact that these data exhibit obvious and substantial changes in volatility. We show that the IID assumption can be substantially weakened; we require only that the return data is generated by a single distribution with a possibly variable scale parameter.

***CDAR hosts Risk Seminars (open to the public) every Tuesday. For more info, visit: http://cdar.berkeley.edu/fall-2016-risk-seminars/

November 13 @ 8:00 am - November 16 @ 5:00 pm

Integral Development Corp.'s Alex Papanicolaou, a CDAR Postdoctoral Researcher, will speak at The 2016 INFORMS Annual Meeting at the Music City Center & Omni Nashville Hotel.

will feature plenaries and keynotes; panel discussions; tutorials; and thousands of oral and poster presentations from leading academics, industry experts, students, and representatives of government agencies. Alex will be presenting on Machine Learning for Finance.