Time-Varying Risk Premium in Large Cross-Sectional Equity Datasets

We develop an econometric methodology to infer the path of risk premia from 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 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, and testing for asset pricing restrictions induced by the no-arbitrage assumption in large economies. The empirical illustration on returns for about ten thousands US stocks from July 1964 to December 2009 shows 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 usual unconditional four-factor model capturing market, size, value and momentum effects.

Patrick Gagliardini's scientific interests are in the area of econometrics, with special emphasis on financial econometrics. His current research focuses on: recent developments in Generalized Method of Moments (GMM) estimation and derivative pricing, Tikhonov regularization for nonparametric estimation with instrumental variables, and panel factor models for default correlation analysis.