Events

This week, the seminar defers to the BSTARS Conference March 23, 2017

Please see the following link for information on the BSTARS Conference 2017. The Seminar will reconvene as usual on April 4, 2017.

Start date: 2017-03-21 11:00:00 End date: 2017-03-21 12:30:00 Venue: 639 Evans Hall at UC Berkeley Address: 639 Evans Hall, Berkeley, CA, 94720

John Arabadjis, State Street (CANCELLED)

Start date: 2017-04-25 11:00:00 End date: 2017-04-25 13:00:00 Venue: 639 Evans Hall at UC Berkeley Address: 639 Evans Hall, Berkeley, CA, 94720

Farzad Pourbabaee, UC Berkeley: Large Deviations of Factor Models with Regularly-Varying Tails: Asymptotics and Efficient Estimation

Abstract: I analyze the large deviation probability of factor models generated from components with regularly-varying tails, a large subclass of heavy tailed distributions. An efficient sampling method for tail probability estimation of this class is introduced and shown to exponentially outperform the classical Monte-Carlo estimator, in terms of the coverage probability and/or the confidence interval’s length. The obtained theoretical results are applied to financial portfolios, verifying that deviation probability of the return to portfolios of many securities is asymptotically robust...

James Lewis, State Street Global Advisors: Systematic Long/Short Factor Portfolios

We consider a panel of 88 "systematic factors": simple, quantitative procedures that assign scores to a universe of assets using publicly available data. For each factor, we construct idealized daily factor portfolios (long/short, market-neutral) and daily return series for the 16-year period between January 2001 and December 2016. Each of the factor return series has positive sample mean, and for all but twelve, the one-sided t-test rejects the zero-mean hypothesis at the 95% confidence level. Moreover, for the full sample, the factors are nearly uncorrelated, and when we partition the...

Alex Papanicolaou, UC Berkeley: Minimum Conditional Expected Drawdown Portfolios

Drawdown, and in particular maximum drawdown, is a widely used indicator of risk in the fund management industry. It is a vital metric for a levered investor who can get caught in a liquidity trap and forced to sell valuable positions if unable to secure funding after an abrupt market decline. Moreover, it is a pathwise risk measure in contrast to end-horizon risk diagnostics like volatility, Value-at-Risk, and Expected Shortfall, which are less significant conditioned on a large drawdown. In this talk, I will present ongoing work aimed at computations for Conditional Expected Drawdown, a...

Carl-Fredrik Arndt, Two Sigma: Dynamics for the Top Eigenvalue and Eigenvector of Empirical Correlation Matrices of Financial Data

In this talk we will discuss how the top eigenvalue/eigenvector pair evolves through time for estimators of covariance and correlation matrices of equity return type data. By this we mean that the matrices have a top eigenvalue which is well separated from the others. Our main results are that both the eigenvalue and eigenvector of a correlation matrix has an extra stability effect, which has previously been observed empirically but to our knowledge never previously studied theoretically. Because of this, one has to use different methods for determining and studying the stationarity of...

Peter Shepard, MSCI: Second Order Risk

Managing a portfolio to a risk model can tilt the portfolio toward weaknesses of the model. As a result, the optimized portfolio acquires downside exposure to uncertainty in the model itself, what we call “second order risk.” We propose a risk measure that accounts for this bias. Studies of real portfolios, in asset-by-asset and factor model contexts, demonstrate that second order risk contributes significantly to realized volatility, and that the proposed measure accurately forecasts the out-of-sample behavior of optimized portfolios....

Terrence Hendershott, Haas School of Business: Relationship Trading in OTC Markets

We examine the network of bilateral trading relations between insurers and dealers in the over-the-counter corporate bond market. Using comprehensive regulatory data we find that many insurers use only one dealer while the largest insurers have a network of up to eighty dealers. To understand the heterogeneity in network size we build a model of decentralized trade in which insurers trade off the benefits of repeat business against more intense dealer competition. Empirically, large insurers form more relations and receive better prices than small insurers. The model matches both the...

Paul Kaplan, Morningstar: A Popularity Asset Pricing Model

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...

Adair Morse, Haas School of Business: A Popularity Asset Pricing Model

We study investments in impact funds, defined as venture capital or growth equity funds with dual objectives of generating financial returns and positive externalities. Being an impact fund elevates a fund’s marginal investment rate by 14.1% relative to a traditional VC fund, even more for funds focused on environmental, poverty, and minority/women issues. Europeans and UNPRI signatories have sharply higher demand for impact. Three investor attributes – household-backed capital, mission-oriented investors, and investors facing political/regulatory pressure to invest in impact – account for...