Research seminars

Real-Time Recovery of Tail Risks

Finance

Speaker: Brian Weller
Kellogg

9 April 2015


I develop a new methodology for estimating tail risks in real time using the cross-section of bid-ask spreads. Competitive market makers embed tail risk information into the spread because (1) stale quotes can only be picked o↵ for large price movements and (2) the magnitude of picking o↵ costs is linear in the size of jumps. Using this insight, simple cross-sectional regressions relating trading volume to spreads and factor exposures can recover instantaneous tail risks. This approach allows for estimation of risks for priced or non-priced return factors at arbitrarily high frequency without requiring directly traded factors. The recovered time series of implied market risks aligns closely with both realized market jumps and the VIX. In addition, the methodology correctly disentangles financial and aggregate market risks during the 2007-2008 Financial Crisis; anticipates jump risks associated with FOMC announcements; and quantifies a sharp, temporary increase in market tail risk before and throughout the 2010 Flash Crash.

Finance

Speaker: Matthieu Bouvard
Desautels Faculty of Management

14 June 2018 - From 2:00 pm to 3:15 pm


Finance

Speaker: Mikhail Simutin
Rotman School of Management

7 June 2018 - From 10:00 am to 12:30 pm


Disclosure, Competition, and Learning from Asset Prices

Finance

Speaker: Liyan Yang
Rotman School of Management

31 May 2018 - T208 - From 2:00 pm to 3:15 pm

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This paper studies the classic information-sharing problem in a duopoly setting in which firms learn information from a financial market. By disclosing information, a firm incurs a proprietary cost of losing competitive advantage to its rival firm but benefits from learning from a more informative asset market. Firms' disclosure decisions can exhibit strategic complementarity, which is strong enough to support both a disclosure equilibrium and a nondisclosure equilibrium. Allowing minimal learning from asset prices dramatically changes firms' disclosure behaviors: without learning from prices, firms do not disclose at all; but with minimal learning from prices, firms can almost fully disclose their information. Learning from asset prices benefits firms, consumers, and liquidity traders, but harms financial speculators.

Alpha Decay*

Finance

Speaker: Anton Lines
Columbia Business School

24 May 2018 - T020 - From 2:00 pm to 3:15 pm

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Using a novel sample of professional asset managers, we document positive incremental alpha on newly purchased stocks that decays over twelve months. While managers are successful forecasters at these short-to-medium horizons, their average holding period is substantially longer (2.2 years). Both slow alpha decay and the horizon mismatch can be explained by strategic trading behavior. Managers accumulate positions gradually and unwind gradually once the alpha has run out; they trade more aggressively when the number of competitors and/or correlation among information signals is high, and do not increase trade size after unexpected capital flows. Alphas are lower when competition/correlation increases.

What is the Expected Return on a Stock?

Finance

Speaker: Ian Martin
LSE

17 May 2018 - From 2:00 pm to 3:15 pm


We derive a formula that expresses the expected return on a stock in terms of the risk-neutral variance of the market and the stock’s excess risk-neutral variance relative to the average stock. These quantities can be computed fromindex and stock option prices; the formula has no free parameters. We run panel regressions of realized stock returns onto risk-neutral variances, and find that the theory performs well at 6-month, 1-year, and 2-year forecasting horizons. The formula drives out beta, size, book-to-market and momentum, and outperforms a range of competitors in forecasting stock returns out of sample. Our results suggest that there is considerably more variation in expected returns, both over time and across stocks, than has previously been acknowledged.


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