Events

The Center for Responsible Business @ Haas School of Business: Corporate Sustainability and Materiality: Strategy, Practice, and Implementation

How do companies and investment funds implement corporate sustainability in a strategic and material way? This Center for Responsible Business Peterson Speaker Series will dive into case studies and learnings from academia, a company, and an investment fund. Register here. Speakers: Divya Mankikar, Head of ESG Integration, Investment Officer III, CalPERS George Serafeim, Jakurski Family Associate Professor, Harvard Business School; Senior Partner...

Alex Papanicolaou: Background Subtraction for Pattern Recognition in High Frequency Financial Data

This event is part of a series of Neyman seminars offered through the Statistics Department. Abstract: Financial markets produce massive amounts of complex data from multiple agents, and analyzing these data is important for building an understanding of markets, their formation, and the influence of different trading strategies. We utilize a signal processing approach to deal with these complexities by applying background subtraction methods to high frequency financial data to extract significant market making behavior. In foreign exchange, for prices in a single currency pair from many...

2016 International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC)

The MCQMC Conference is a bienniel meeting on Monte Carlo and quasi-Monte Carlo methods. It usually attracts 150 to 200 mathematicians, computer scientists, statisticians and researchers in related fields. There is more information in the about MCQMC tab. The conference focusses on the topics below.

Topics Monte Carlo, quasi-Monte Carlo, Markov chain Monte Carlo Digital nets and lattice rules Discrepancy theory Complexity and tractability of multivariate problems Multi-level Monte Carlo Sequential Monte Carlo and...

Center for Financial and Risk Analytics Seminar

Lisa Goldberg and Alex Shkolnik to speak at Stanford’s Center for Financial Risk Analytics The Center for Financial and Risk Analytics at Stanford University pioneers financial models, statistical tools, computational algorithms, and software to address the challenges that arise in this context.

Start date: 2016-10-13 15:00:00 End date: 2016-10-13 17:00:00 Website: http://www.stanford.edu/group/cfra/cgi-...

FMA International 2016 Annual Meeting

Baeho Kim, CDAR Visiting Researcher, to present at FMA International’s 2016 Annual Meeting A Smiling Bear in the Equity Options Market and the Cross-section of Stock Returns Abstract: We propose a measure for the convexity of an option-implied volatility curve, IV convexity, as a forward-looking measure of excess tail-risk contribution to the perceived variance of underlying equity returns. Using equity options data for individual U.S.-listed stocks during 2000-2013, we find that the average return differential between the lowest and highest IV convexity quintile...

Alexandre Pauli, UC Berkeley: Forecasting a Drawdown-based Risk Measure

Goldberg and Mahmoud (2014) have defined Conditional Expected Drawdown (CED) as the tail-mean of the maximum drawdown distribution and abstractly showed the attractive properties of the risk measure for risk management and portfolio construction. The purpose of this project is to empirically investigate how CED can be employed in practice. The major challenge is to find accurate estimators and to retrieve enough information about maximum drawdowns, which vary on both magnitude and duration, to produce a forecast of CED with minimum uncertainty. We compare CED to the well-known Value-at-Risk (...

Alex Shkolnik, UC Berkeley: Identifying broad and narrow financial risk factors with convex optimization

Factor analysis of security returns aims to decompose a return covariance matrix into systematic and specific risk components. While successful in many respects,traditional approaches like PCA and maximum likelihood suffer from several drawbacks. These include a lack of robustness and strict assumptions on the underlying model of returns.

We apply convex optimization methods to decompose a security return covariance matrix into a low rank, sparse and diagonal components. The low rank component includes the market and other broad factors that affect most securities. The sparse component...

Yang Xu: Intervention to Mitigate Contagion in a Financial Network

Systemic risk in financial networks has received attention from academics since the 2007-2009 financial crisis. We analyze a financial network from the perspective of a regulator who aims to minimize the fraction of defaults under a budget constraint. Unlike the majority of literature in this field, the connections between financial institutions (hereafter, banks) are assumed unknown in the beginning, but are revealed as the contagion process unfolds. We focus on the case in which the number of initial defaults is small relative to the total number of banks. We analyze the optimal...

Stanford Center for Financial Risk Analytics Seminar

CDAR Co-Director Bob Anderson presented: “PCA with Model Misspecification” The theoretical justifications for Principal Component Analysis (PCA) typically assume that the data is IID over the estimation window. In practice, this assumption is routinely violated in financial data. We examine the extent to which PCA-like procedures can be justified in the presence of two specific kinds of misspecification present in financial data: time-varying volatility, and the presence of regimes in factor loadings. Joint work with Stephen W. Bianchi (Berkeley).

Start date:...

BIDS Spring 2016 Data Science Faire

Join BIDS May 3 from 1:30-4:30PM in 190 Doe Library for our Spring 2016 Data Science Faire to close out BIDS' second academic year and celebrate data science at Berkeley.

We hear the word "data" almost every day on campus and in the news. But, did you ever wonder what UC Berkeley students, faculty, and researchers are doing with data and what interesting findings they have uncovered? At this year's Data Science Faire, we will showcase exciting data-intensive initiatives at BIDS and UC Berkeley, highlighting work from the diverse community of data scientists around campus. Learn more...