Multiple Testing and Factor Modelling
in Finance
Olivier Scaillet
(Université de Genève et Swiss Finance Institute (SFI))
4 séances de 4 heures
(Février-Mars 2015 mardi matin)
These lectures cover some recent and particularly active research topics in financial econometrics,
with applications in asset management and high-frequency data. We illustrate every concept and tool
with financial data and stress practical implications. We develop two areas of research: multiple testing
and factor modeling in finance
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We often test simultaneously several hypotheses in econometric analysis. A multiple testing
method yields a decision concerning each individual testing problem by either rejecting the
null hypothesis or not. In an ideal world, we would like to reject all those hypotheses that are
false. In a realistic world, given a finite amount of data, this cannot be achieved with certainty.
The goal is to make as many true rejections as possible and to avoid “too many” false
rejections. We review a number of statistical solutions to that problem. We illustrate by
empirical applications to alpha generation by mutual funds and hedge funds, performance
analysis of trading strategies, and jump detection in high frequency data.
The workhorse to empirically study equity risk premia is the linear factor model, whose
theoretical ground lies in the Arbitrate Pricing Theory. We review the main contributions in the
literature to factor modeling and risk premium estimation. We study asymptotic properties of
two-pass cross-sectional estimators of the path over time of the risk premia in unbalanced
panel. We allow the number of stocks and the number of dates growing to infinity
simultaneously. We also study goodness-of-fit test for the conditional factor model based on
the sum of squared residuals of the second-pass cross-sectional regression. Empirical
applications show that conditional risk premia are large and volatile in crisis periods. They
exhibit large positive and negative strays from standard unconditional estimates and follow the
macroeconomic cycles. The asset pricing restrictions are rejected for the conditional fourfactor model capturing market, size, value and momentum effects.
Bibliographie
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Barras, L., Scaillet, O. and R. Wermers(2010), “False Discoveries in Mutual Fund
Performance: Measuring Luck in Estimated Alphas”, Journal of Finance, 65, 179-216.
Bajgrowicz, P. and O. Scaillet (2012), “Technical Trading Revisited: Persistence Tests,
Transaction Costs, and False Discoveries”, forthcoming in Journal of Financial Economics.
Benjamini, Y. and Y. Hochberg (1995), “Controlling the False Discovery Rate: A Practical
and Powerful Approach to Multiple Testing”, Journal of the Royal Statistical Society: Series B,
57, 289-300.
Dudoit, S., Shaffer, J. and J. Boldrik (2003), “Multiple Hypothesis Testing in Microarray
Experiments”, Statistical Science, 18, 71-103.
Gagliardini, P., Ossola, E. and O. Scaillet (2012), “Time-varying Risk Premium in Large Crosssectional Equity Datasets”, Working Paper SFI.
Romano, J., Shaikh, A. and M. Wolf (2008), “Formalized Data Snooping Based on
Generalized Error Rates”, Econometric Theory, 24, 404-447.
Shanken, J. (1992), “On the Estimation of Beta-pricing Models”, The Review of Financial
Studies, 5, 1-33.
Storey, J. (2002),
“A Direct Approach to False Discovery Rates”, Journal of the Royal
Statistical Society: Series B 64, 479-498.