Events

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

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. Applying the methodology to a dataset of U.S.-domiciled mutual funds we distinguish the components of active returns attributable to (1) constant factor exposures, for example, a tilt to value stocks; (2) time-varying factor exposures; and (3) security selection. We find large-cap growth funds tend...

Alex Shkolnik presents "The Dispersion Bias" at the Financial Risks International Forum in Paris

The explosive growth in the area of data-driven risk factor identification elevates the importance of analyzing and, to the extent possible, mitigating estimation error. Using random matrix theory, we show how a large, estimation-error-induced bias in the sample eigenvectors of factor-based covariance matrices affects portfolio construction and risk estimation. We develop a bias correction approach for the first sample eigenvector which corrects the problems of portfolio construction and risk estimation. Our approach is distinct from the regularization and eigenvalue shrinkage methods...

Lisa Goldberg presents “The Dispersion Bias” at NYU

Abstract: Estimation error has plagued quantitative finance since Harry Markowitz launched modern portfolio theory in 1952. Using random matrix theory, we characterize a source of bias in the sample eigenvectors of financial covariance matrices. Unchecked, the bias distorts weights of minimum variance portfolios and leads to risk forecasts that are severely biased downward. To address these issues, we develop an eigenvector bias correction. Our approach is distinct from the regularization and eigenvalue shrinkage methods found in the literature. We provide theoretical guarantees on the...

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...

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

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. In this work, we explore one such indicator named Probability of Informed Trading (typically shorten as PIN) and its variants. In an earlier test, a variant known as VPIN was demonstrated to show a strong signal more than an hour before the Flash Crash of 2010....

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

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. Utilizing a large comprehensive data set of 300,000 clients of a German discount broker, we find that even today East Germans invest less in the stock market, both at the extensive and the intensive margin, are more likely to hold stocks of communist countries, and are less likely to hold stocks of capitalist institutions and countries. Effects are...

George Papanicolaou, Stanford: Statistical Arbitrage

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. The trading algorithm itself will be presented and then a well calibrated version of it will be used on daily SP500 data from 2003-2014. Well calibrated means that the risk associated with this trading algorithm can be identified and controlled...

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....

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-...