## SEM217: Rupal Kamdar, UC Berkeley: The Securitization and Solicited Refinancing Channel of Monetary Policy

Tuesday, April 5th @ 12:30-2:00 PM (1011 Evans Hall)

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.

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

Tuesday, February 8th @ 8:30-5:30 PM (Arrillaga Alumni Center, Mccaw Hall)

Read the agenda here.

## SEM217: Matthias Weber, Swiss Re: Concrete examples of trend analyses and forward-looking modeling in Swiss Re's underwriting

Tuesday, January 18th @12:30-2:00 PM (1011 Evans Hall)

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.

## SEM217: Mariana Olvera-Cravioto, UC Berkeley: PageRank on directed complex networks

Tuesday, January 25th @ 12:30-2:00 PM (1011 Evans Hall)

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.

## SEM217: Markus Pelger, Stanford: Interpretable proximate factors for large dimensions

Tuesday, February 1st @ 12:30-2:00 PM (1011 Evans Hall)

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.

## SEM217: Jose Menchero, Bloomberg: Solving the "Curse of Dimensionality" Problem in Multi-Asset-Class Risk Models

Tuesday, February 22nd @ 12:30-2:00 PM (1011 Evans Hall)

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).

## SEM217: Kyong Shik Eom, UC Berkeley: The role of dynamic and static volatility interruptions: Evidence from the Korean stock markets

Tuesday, March 1st @ 12:30-2:00 PM (1011 Evans Hall)

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.

## SEM217: Alec Kercheval, Florida State University: A Credit Risk Framework With Jumps and Stochastic Volatility

Tuesday, March 15th @ 12:30-2:00 PM (1011 Evans Hall)

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.

## SEM217: Ulrike Malmendier, UC Berkeley: The Long-lasting Effects of Propaganda on Financial Risk-Taking

Tuesday, April 12th @ 12:30-2:00 PM (1011 Evans Hall)

We argue that emotional coloring of experiences via political propaganda has long-term effects on risk taking. We show that living in an anti-capitalist system reduces individuals' willingness to invest in the stock market even decades later.

## SEM217: George Papanicolaou, Stanford: Statistical Arbitrage

Tuesday, April 26th @ 12:30-2:00 PM (1011 Evans Hall)

Statistical arbitrage is a collection of trading algorithms that are widely used today but can have very uneven performance, depending on their detailed implementation. I will introduce these methods and explain how the data used as trading signals are prepared so that they depend weakly on market dynamics but have adequate statistical regularity.

## SEM217: Jeffrey Bohn, Swiss Re: Digitally-driven change in the insurance industry—disruption or transformation?

Tuesday, August 2nd @ 12:30-2:00 PM (1011 Evans Hall)

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.

## SEM217: John Wu, LBL: Could Probability of Informed Trading Predict Market Volatility?

Tuesday, April 19th @ 12:30-2:00 PM (1011 Evans Hall)

Significant market events such as Flash Crash of 2010 undermine the trust of the capital market system. An ability to forecast such events would give market participants and regulators time to react to such events and mitigate their impact. For this reason, there have been a number of attempts to develop early warning indicators.

## SEM217: Ananth Madhavan, Blackrock: Factor Strategies: Crowding, Capacity and Sources of Active Returns

Tuesday, March 8th @ 12:30 - 2:00 PM (1011 Evans Hall)

We develop a methodology to estimate dynamic factor loadings using cross-sectional risk characteristics, which is especially useful when factor loadings significantly vary over time. In comparison, standard regression approaches assume the factor loadings are constant over a particular window.

## SEM217: Nick Gunther, UC Berkeley: The Financing Rate Implied by Equity Futures

Tuesday, March 22nd @ 11:00-12:30 PM (1011 Evans Hall)

This talk will explore the cost of implicit leverage associated with an S&P 500 Index futures contract and derive an implied financing rate.