Spring 2018

Alec Kercheval, Florida State University: A Credit Risk Framework With Jumps and Stochastic Volatility

The jump threshold perspective is a view of credit risk in which the event of default corresponds to the first time a stock's log price experiences a downward jump exceeding a certain threshold size. We will describe and motivate this perspective and show that we may obtain explicit formulas for default probabilities and credit default swaps, even when the stock has stochastic volatility, the interest rate is stochastic, and the default threshold is a non-constant stochastic process. This talk is based on joint work with Pierre Garreau and Chun-Yuan Chiu.

Start date: 2018-03-15 12:30:...

Rupal Kamdar, UC Berkeley: The Securitization and Solicited Refinancing Channel of Monetary Policy

I document the “securitization and solicited refinancing channel,” a novel transmission mechanism of monetary policy and its heterogenous regional effects. The mechanism predicts that mortgage lenders who sell their originations to Government Sponsored Enterprises or into securitizations no longer hold the loan’s prepayment risk, and when rates drop, these lenders are more likely to signal to their borrowers to refinance, resulting in more borrower refinancing. A regression analysis finds that in response to a decline in mortgage-backed security yields, regions where originate-to-sell-or-...

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....

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....

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...

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...

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...

Lisa Goldberg, John Arabadjis, and Jeff Bohn to speak at Stanford's AI in Fintech Forum

Read the agenda here.

Start date: 2018-02-08 08:30:00 End date: 2018-02-08 17:30:00 Venue: Arrillaga Alumni Center, Mccaw Hall Address: 326 Galvez Street, Stanford, CA, 94305