Time‐Varying Risk Premium in Large Cross‐Sectional Equity Data Sets

Patrick Gagliardini (), Elisa Ossola () and Olivier Scaillet

Econometrica, 2016, vol. 84, 985-1046

Abstract: We develop an econometric methodology to infer the path of risk premia from a large unbalanced panel of individual stock returns. We estimate the time‐varying risk premia implied by conditional linear asset pricing models where the conditioning includes both instruments common to all assets and asset‐specific instruments. The estimator uses simple weighted two‐pass cross‐sectional regressions, and we show its consistency and asymptotic normality under increasing cross‐sectional and time series dimensions. We address consistent estimation of the asymptotic variance by hard thresholding, and testing for asset pricing restrictions induced by the no‐arbitrage assumption. We derive the restrictions given by a continuum of assets in a multi‐period economy under an approximate factor structure robust to asset repackaging. The empirical analysis on returns for about ten thousand U.S. stocks from July 1964 to December 2009 shows that risk premia are large and volatile in crisis periods. They exhibit large positive and negative strays from time‐invariant estimates, follow the macroeconomic cycles, and do not match risk premia estimates on standard sets of portfolios. The asset pricing restrictions are rejected for a conditional four‐factor model capturing market, size, value, and momentum effects.

Date: 2016
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Citations: View citations in EconPapers (76)

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Related works:
Working Paper: Time-varying risk premium in large cross-sectional equity datasets (2015)
Working Paper: Time-Varying Risk Premium In Large Cross-Sectional Equidity Datasets (2011)
Working Paper: Time-Varying Risk Premium In Large Cross-Sectional Equidity Datasets (2011)
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