Svetlana Bryzgalova

photograph of Svetlana Bryzgalova

PhD Candidate, Department of Economics, LSE



Phone number:

Room number: R4z22b

Address: Department of Economics and
                 Financial Markets Group
                 London School of Economics and Political Science
                 Houghton Street
                 London WC2A 2AE

Job Market Candidate Information
Placement Officer Professor Leonardo Felli 
Supervisor: Professor Peter Robinson
Advisor: Dr Christian Julliard
Primary Fields: Asset Pricing
Financial Economics
Financial Econometrics
Secondary Fields: Applied Econometrics
References: Professor Peter Robinson (LSE)
Dr Christian Julliard (LSE)
Dr Andrea Tamoni (LSE)
Job Market Paper:

Spurious Factors in Linear Asset Pricing Model


When a risk factor has (close to) zero covariance with asset returns, risk premia in linear asset pricing models are no longer strongly identified. In this case, usual inference methods become unreliable due to lack of consistency, asymptotic normality, irregular confidence bounds, and tend to generate spuriously high significance levels and measures of fit for specifications that include such a factor. I develop a new shrinkage-based framework for the estimation of the price of risk that: a) provides simultaneous model diagnostics and parameter estimates; b) automatically removes the effect of spurious factors; c) restores consistency and asymptotic normality for the estimates of strongly identified parameters, as well as the accuracy of standard measures of fit; d) performs well in both small and large samples. Using this novel framework to revisit a large set of established empirical results, I provide new insights on the pricing ability of various factors proposed in the literature. In particular, I identify a set of robust factors (e.g. Fama-French ones, but not only), and those that suffer from severe identification problems that render the standard assessment of their pricing performance unreliable (e.g. durable consumption growth, some currency factors, and human capital proxies).

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