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Session Overview
FE-1: Econometric Modeling of Risk and Risk Premia
Friday, 25/Aug/2017:
10:30am - 12:00pm

Session Chair: Alberto G. Rossi, University of Maryland
Location: O145

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Inference on Risk Premia in the Presence of Omitted Factors

Stefano Giglio1, Dacheng Xiu2

1Yale School of Management; 2University of Chicago

Discussant: Patrick Gagliardini (USI Università della Svizzera italiana)

We propose a three-pass method to estimate the risk premia of observable factors in a linear asset pricing model, which is valid even when the observed factors are just a subset of the true factors that drive asset prices. Standard methods to estimate risk premia are biased in the presence of omitted priced factors correlated with the observed factors. We show that the risk premium of a factor can be identified in a linear factor model regardless of the rotation of the other control factors as long as they together span the space of true factors. Motivated by this rotation invariance result, our approach uses principal components to recover the factor space and combines the estimated principal components with each observed factor to obtain a consistent estimate of its risk premium. This methodology also accounts for potential measurement error in the observed factors and detects when such factors are spurious or even useless. The methodology exploits the blessings of dimensionality, and we therefore apply it to a large panel of equity portfolios to estimate risk premia for several workhorse linear models.

EFA2017-374-FE-1-Giglio-Inference on Risk Premia in the Presence of Omitted Factors.pdf

Macro Risks and the Term Structure of Interest Rates

Geert Bekaert1, Eric Engstrom2, Andrey Ermolov3

1Columbia University, NBER; 2Federal Reserve Board; 3Fordham University

Discussant: Andrea Vedolin (Boston University)

We extract aggregate supply and aggregate demand shocks for the US economy from macroeconomic data on inflation, real GDP growth, core inflation and the unemployment gap. We first use unconditional non-Gaussian features in the data to achieve identification of these structural shocks while imposing minimal economic assumptions. We find that recessions in the 1970s and 1980s are better characterized as driven by supply shocks while later recessions were driven primarily by demand shocks. The Great Recession exhibited large negative shocks to both demand and supply. We then use conditional (time-varying) non-Gaussian features of the structural shocks to estimate "macro risk factors" for supply and demand shocks that drive "bad" (negatively skewed) and "good" (positively skewed) variation for supply and demand shocks. The Great Moderation, a general decline in the volatility of many macroeconomic time series since the 1980s, is mostly accounted for by a reduction in the good demand variance risk factor. In contrast, the risk factors driving bad variance for both supply and demand shocks, which account for most recessions, show no secular decline. Finally, we find that macro risks significantly contribute to the variation in yields, bond risk premiums and the term premium. While overall bond risk premiums are counter-cyclical, an increase in bad demand variance is associated with lower risk premiums on bonds.

EFA2017-1001-FE-1-Bekaert-Macro Risks and the Term Structure of Interest Rates.pdf

A Least Squares Regression Realized Covariation Estimation Under MMS Noise and Non-Synchronous Trading

Ingmar Nolte1, Michalis Vasios2, Valeri Voev3, Qi Xu4

1Lancaster University; 2Bank of England; 3LEGO System A/S; 4Zhejiang University

Discussant: Rasmus Varneskov (Northwestern University)

In this paper we propose a least squares regression framework for the estimation of the realized covariation matrix in the presence of market microstructure (MMS) noise and non-synchronous trading. Different from alternative approaches, our framework also allows for the estimation of MMS noise moments. We show that these noise moments are related to measures of liquidity and most importantly contain information that helps to significantly improve out-of-sample asset allocation. In terms of the estimator itself, we show within a comprehensive simulation study and an empirical analysis that our approach precisely estimates the covariation matrix and outperforms a set of widely applied alternative estimators.

EFA2017-504-FE-1-Nolte-A Least Squares Regression Realized Covariation Estimation Under MMS Noise and.pdf

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