Bryan T. Kelly
| Affiliation | Ph.D candidate Stern School of Business New York University |
| Address | 44 W 4th Street, KMC
9-197 New York, NY 10012 |
| Phone | 212-998-0368 |
| bkelly@stern.nyu.edu |
|
| Research interests | Asset pricing (theory and empirics) Information and markets Financial econometrics Extreme events in finance |
| Curriculum vitae | [Download PDF] |
| References | Robert Engle (Thesis chair) Alexander Ljungqvist Xavier Gabaix Stijn Van Nieuwerburgh |
Working Papers
Risk Premia and the Conditional Tails of Stock Returns (Job
Market Paper)
(This version posted January 2010)
Theory suggests that the risk of infrequent yet extreme events has a large impact on asset prices. Testing models of this hypothesis remains a challenge due to the difficulty of measuring tail risk fluctuations over time. I propose a new measure of time-varying tail risk that is motivated by asset pricing theory and is directly estimable from the cross section of returns. My procedure applies Hill's (1975) tail risk estimator to the cross section of extreme events each day. It then optimally averages recent cross-sectional Hill estimates to provide conditional tail risk forecasts. Empirically, my measure has strong predictive power for aggregate market returns, outperforming all commonly studied predictor variables. I find that a one standard deviation increase in tail risk forecasts an increase in excess market returns of 4.4% over the following year. Cross-sectionally, stocks that highly positively covary with my tail risk measure earn average annual returns 6.0% lower than stocks with low tail risk covariation. I show that these results are consistent with predictions from two structural models: i) a long run risks economy with heavy-tailed consumption and dividend growth shocks, and ii) a time-varying rare disaster framework.
Testing Asymmetric Information Asset Pricing Models (with A. Ljungqvist) (Under review)
Modern asset pricing theory is based on the assumption that investors have heterogeneous information. We provide direct evidence of the importance of information asymmetry for asset prices and investor demands using a natural experiment. The experiment captures plausibly exogenous variation in information asymmetry on a stock-by-stock basis for a large set of U.S. companies. Consistent with predictions derived from a Grossman and Stiglitz-type model, we find that prices and uninformed investors’ demands fall as information asymmetry increases. In the cross-section, these falls are larger, the more investors are uninformed, the larger and more variable is stock turnover, the more uncertain is the asset’s payoff, and the noisier is the better-informed investors’ signal. We show that at least part of the fall in prices is due to expected returns becoming more sensitive to liquidity risk. Our results confirm that information asymmetry has a substantial effect on asset prices and imply that a primary channel linking asymmetry to prices is liquidity.
Dynamic Equicorrelation (with R. Engle) (Under review)
A new covariance matrix estimator is proposed under the assumption that at every time period all pairwise correlations are equal. This assumption, which is pragmatically applied in various areas of finance, makes it possible to estimate arbitrarily large covariance matrices with ease. The model, called DECO, is a special case of the CCC and DCC models which involve first adjusting for individual volatilities and then estimating the correlations. A QMLE result shows that DECO can continue to give consistent parameter estimates when the equicorrelation assumption is violated. Generalizations to block equicorrelation structures, models with exogenous variables, and alternative specifications are explored and diagnostic tests are proposed. Estimation is evaluated by Monte Carlo and using US stock return data.
A Practical Guide to Volatility Forecasting (with C. Brownlees and R. Engle) (Under review)
We present a volatility forecasting comparative study based on the methodology and financial data from Vlab, an econometric software application for automated real time volatility analysis. Our goal is to identify successful predictive models over multiple horizons and to investigate how predictive ability is influenced by choices for estimation window length, innovation distribution, and frequency of parameter re-estimation. Test assets include a range of domestic and international equity indices and exchange rates. We find that model rankings are insensitive to forecast horizon and suggestions for estimation best practices emerge. While our main sample spans 1990-2008, we take advantage of the near-record surge in volatility during the last half of 2008 to ask if forecasting models or best practices break down during periods of turmoil. We find that volatility during the 2008 crisis was well approximated by predictions one day ahead, and should have been within risk managers' 1\% confidence intervals up to one month ahead.
The Value of Research (with A. Ljungqvist) (Currently being revised)
We estimate the value added by sell-side equity research analysts and explore the links between analyst research, informational efficiency, and asset prices. We identify the value of research from exogenous changes in analyst coverage. On announcement that a stock has lost all coverage, share prices fall by around 110 basis points or $8.4 million on average. The share price reaction is attenuated the more analysts continue to cover the stock, suggesting that there are diminishing returns to coverage at the margin. The adverse effect of coverage terminations is proportional to the analyst's reputation and experience and to the size of the broker's retail sales force. Exogenous reductions in coverage are followed by: less efficient pricing and lower liquidity; greater earnings surprises and more volatile trading around subsequent earnings announcements; increases in required returns; and reduced return volatility. Simulations suggest investors can trade profitably on the volatility changes. Finally, retail investors sell and large institutional investors buy around coverage terminations, suggesting that different investor clienteles have different demands for analyst research.
Technical Note: Reconstructing a Positive Semidefinite Matrix (with C.K. Li)
2009. The Bulletin of the International Linear Algebra Society, 43, p.44
We show that generating block equicorrelated matrices as in DECO (Engle and Kelly 2009) results in a PSD matrix.
Honors and Awards
2010 Herman E. Krooss Award,
Best Dissertation across disciplines, NYU-Stern
SAC Capital Ph.D. Candidate Award for Outstanding Research, WFA Annual Meeting
2009 Shmuel
Kandel Award, Outstanding Ph.D. Student in Financial Economics, UWFC
Best Dissertation Proposal Award in Risk
Management, FMA
Joseph
H. Taggart Fellowship, NYU Stern School of Business
Student Travel Grant, AFA
2008 Best Paper Award in Financial Markets,
FMA (for "The Value of Research")
2005 NYU Stern School of Business Fellowship
Teaching Experience
Instructor,
Foundations of Financial Markets
NYU, Stern Undergraduate College (Summer 2008)
Topics:
Fixed income, risk and return, CAPM, equity valuation
and options
Teaching Assistant,
Volatility
NYU, Stern MBA, Prof. Robert Engle (2007-2009)
Teaching Assistant,
Corporate Finance
NYU, Stern MBA, Prof. Daniel Wolfenzon (2006-2008)
Bryan Kelly
http://pages.stern.nyu.edu/~bkelly/
bkelly@stern.nyu.edu
