All seminars are held in 1011 Evans Hall at UC Berkeley, unless otherwise notified.

Upcoming seminar

Thursday, February 22, 2018 12:30 PM to 2:00 PM

Jose Menchero, Bloomberg: Solving the "Curse of Dimensionality" Problem in Multi-Asset-Class Risk Models

Estimating a robust risk model risk for a portfolio that spans multiple asset classes is a challenging task due to the “curse of dimensionality” (i.e., the problem of estimating too many relationships from too few observations). While the sample covariance matrix is easily computed, it is susceptible to capturing spurious relationships that make it unsuitable for portfolio construction purposes. In this talk, we present a new approach for constructing risk models that span multiple asset classes. We also discuss the implications for portfolio risk management and portfolio construction.

All seminars


Thursday, January 18, 2018 12:30 PM to 2:00 PM
Matthias Weber, Swiss Re: Concrete examples of trend analyses and forward-looking modelling in Swiss Re's underwriting

Abstract:

  • In insurance, underwriting performance is a function of exposures, losses relative to exposures and premiums relative to exposures. Getting losses and loss trends right (--> cost of goods sold) is critically important. A small estimation mistake typically has a large impact on the bottom line.
  • Swiss Re is determining loss relevant trends using advanced analytics, often in collaboration with universities, government organizations, NGOs, rating agencies, consultants, investment management firms, lawyers, and others. Findings are used for both capital allocation and experience-based costing analyses.
  • In situations where the past is a poor predictor of the future, exposure-based rating analyses using forward-looking models are superior to the traditional experience-based approach. Swiss Re's proprietary forward-looking models are routinely used in costing.
  • The Swiss Re Institute professionalizes Swiss Re's R&D to improve its competitive advantage in risk selection and capital allocation in line with Swiss Re's strategic priorities.
Download the slides from this presentation: Trend Analysis Forward Looking Modeling


Thursday, January 25, 2018 12:30 PM to 2:00 PM
Mariana Olvera-Cravioto, UC Berkeley: PageRank on directed complex networks

Abstract: The talk will center around a set of recent results on the analysis of Google’s PageRank algorithm on directed complex networks. In particular, it  will focus on the so-called power-law hypothesis, which states that the distribution of the ranks produced by PageRank on a scale-free graph (whose in-degree distribution follows a power-law) also follows a power-law with the same tail-index as the in-degree. We show that the distribution of PageRank on both the directed configuration model and the inhomogeneous random digraph does indeed follow a power-law whenever the in-degree does, and we provide explicit asymptotic limits for it. Moreover, our asymptotic expressions exhibit qualitatively different behaviors depending on the level of dependence between the in-degree and out-degree of each vertex. On graphs where the in-degree and out-degree are close to independent, our main theorem predicts that PageRank will tend to grant high ranks to vertices with large in-degrees, but also  to vertices who have highly-ranked inbound neighbors. However, when the in-degree and out-degree are positively correlated, the latter can potentially disappear, strengthening the impact of high-degree vertices on the ranks produced by the algorithm.

Download the slides from this presentation: Olvera_PageRank


Thursday, February 1, 2018 12:30 PM to 2:00 PM
Markus Pelger, Stanford: Interpretable proximate factors for large dimensions

This papers deals with the approximation of latent statistical factors with sparse and easy-to-interpret proximate factors. Latent factors in a large-dimensional factor model can be estimated by principal component analysis, but are usually hard to interpret. By shrinking the factor weights, we obtain proximate factors that are easier to interpret. We show that proximate factors consisting of 5-10% of the cross-sectional observations with the largest exposure are usually sufficient to almost perfectly replicate the population factors, even if these do not have a sparse structure. We derive an asymptotic lower bound for the correlation and generalized correlations of proximate factors with the population factors providing guidance on how to construct the proximate factors. Simulations and empirical applications to financial single- and double-sorted portfolios illustrate that proximate factors provide an excellent approximation to latent factors while being interpretable.

Download the slides from this presentation: Interpretable Factor Models


Thursday, February 8, 2018 8:30 AM to 5:30 PM


Thursday, February 15, 2018 12:30 PM to 2:00 PM
Jeffrey Bohn, Swiss Re: Digitally-driven change in the insurance industry—disruption or transformation?

Abstract: As technology continues to insinuate itself into all facets of financial services, the insurance industry faces a slow-motion parade of promise, possibilities, prematurity, and pared-down expectations. Digitization, the birth of InsurTech, machine intelligence, and the collection & curation of (orders of magnitude) more structured & unstructured data are changing (and will continue to change) the industry in material ways—not always in line with predictions. This presentation describes (from a large insurer’s perspective) trends and challenges related to how technology and society’s digitization are irrevocably changing risk markets and insurance. Based on the described trends, one nuanced answer will be suggested to the question of whether insurance is being disrupted or transformed.

Download the slides from this presentation: Bohn_DigitallyDrivenChangesInsurance_2018_v3.1


Thursday, February 22, 2018 12:30 PM to 2:00 PM
Jose Menchero, Bloomberg: Solving the "Curse of Dimensionality" Problem in Multi-Asset-Class Risk Models

Estimating a robust risk model risk for a portfolio that spans multiple asset classes is a challenging task due to the “curse of dimensionality” (i.e., the problem of estimating too many relationships from too few observations). While the sample covariance matrix is easily computed, it is susceptible to capturing spurious relationships that make it unsuitable for portfolio construction purposes. In this talk, we present a new approach for constructing risk models that span multiple asset classes. We also discuss the implications for portfolio risk management and portfolio construction.



Thursday, March 1, 2018 12:30 PM to 2:00 PM
Kyong Shik Eom, UC Berkeley: The role of dynamic and static volatility interruptions: Evidence from the Korean stock markets

We conduct a comprehensive analysis on the sequential introductions of dynamic and static volatility interruption (VI) in the Korean stock markets. The Korea Exchange introduced VIs to improve price formation, and to limit damage to investors from brief periods of abnormal volatility, for individual stocks. We find that dynamic VI is effective in stabilizing markets and price discovery, while the effect of static VI is limited. The static VI functions similarly to the pre-existing price-limit system; this accounts for its limited incremental benefit.



Thursday, March 15, 2018 12:30 PM to 2:00 PM


Thursday, March 22, 2018 12:30 PM to 2:00 PM


Thursday, April 5, 2018 12:30 PM to 2:00 PM


Thursday, April 12, 2018 12:30 PM to 2:00 PM


Thursday, April 19, 2018 12:30 PM to 2:00 PM


Thursday, April 26, 2018 12:30 PM to 2:00 PM