Is it Mathematics or is it Software? (link to pdf) Forthcoming in Notices of the American Mathematical Society, CDAR Co-Director Lisa Goldberg explores the concepts and issues surrounding mathematical software development. From the abstract: “Since we are in an age where significant progress has been achieved on both practical and intellectual levels by such thinking, the development of mathematical software is genuinely part of mathematics. That is, the conceptual breakthroughs in software development should find a home in the academic mathematical community.”
CDAR encourages innovative thinking in data science research and its applications to investment risk and portfolio management. With support from State Street, as well as the Economics and Statistics Departments at UC Berkeley, CDAR organizes workshops, conferences, and research opportunities to create a culture of learning. These events bring together academic researchers in physical and social sciences and industry researchers from finance firms and technology companies both large and small.
Lisa Goldberg and Alex Shkolnik to speak at Stanford’s Center for Financial Risk Analytics
October 13, 2016 at Stanford (link to website)
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.
CDAR Co-Director Lisa Goldberg to Present Moskowitz Prize at 2016 SRI Conference
November 9-11, 2016 in Denver, Colorado (link to website)
The SRI Conference – on Sustainable, Responsible, Impact Investing serves thought leaders, investors, and investment professionals in the ESG, Shareowner Advocacy, and Impact Investing space. Together, we are catalyzing the shift to a more socially equitable and environmentally sustainable economy. The Moskowitz Prize is a global award that recognizes outstanding academic research on a topic germane to the field of sustainable, responsible, impact (SRI) investment industry.
Baeho Kim, CDAR Visiting Researcher, to present at the SIAM Conference on Financial Mathematics & Engineering
November 17-19, 2016 in Austin, Texas (link to website)
Stochastic Intensity Margin Modeling of Credit Default Swap Portfolios
Abstract: We consider the problem of initial margin (IM) modeling for portfolios of credit default swaps (CDS) from the perspective of a derivatives Central Counterparty (CCP). The CCPs’ IM models in practice are based on theoretically-unfounded direct statistical modeling of CDS spreads. Using a reduced-form approach, our IM model based on stochastic default intensity prices the portfolio constituents in a theoretically meaningful way and shows that statistical IM models can underestimate CCPs’ collateral requirements. In addition, our proposed Affine jump-diffusion intensity modeling approach illustrates that a counter-cyclical IM scheme can be implemented from a macro-prudential perspective.
Baeho Kim, CDAR Visiting Researcher, to present at FMA International’s 2016 Annual Meeting
October 19-22 2016 in Las Vegas, Nevada (link to website)
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 portfolios exceeds 1% per month, which is both economically and statistically significant on a risk-adjusted basis. Our empirical findings indicate that informed options traders anticipating heavier tail risk proactively induce leptokurtic implied distributions of underlying stock returns before equity investors express their tail-risk aversion.
CDAR Affiliate Alex Shkolnik presented at the 2016 International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC)
August 16-19, 2016 at Stanford (link to website)
Important Sampling for Default Timing Models
Abstract: In credit risk management and other applications, one is often interested in estimating the likelihood of large losses in a portfolio of positions exposed to default risk. A serious challenge to the application of importance sampling (IS) methods in this setting is the computation of the transform of the loss on the portfolio. This transform is intractable for many models of interest, limiting the scope of standard IS algorithms. We develop, analyze and numerically test an IS estimator of large-loss probabilities that does not require a transform to be computed. The resulting IS algorithm is implementable for any reduced form model of namy-by-name default timing that is amenable to simulation.