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Activity Number: 580 - Time Series and Factor Models
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #323231 View Presentation
Title: Knowing Factors or Factor Loadings, or Neither? Evaluating Estimators of Large Covariance Matrices with Noisy and Asynchronous Data
Author(s): Kun Lu* and Chaoxing Dai and Dacheng Xiu
Companies: Princeton University and Booth School of Business University of Chicago and University of Chicago
Keywords: high-dimensional data ; high-frequency data ; factor model ; pre-averaging estimator ; portfolio allocation ; Barra covariance estimator

In this paper, we investigate factor-model-based large covariance (and precision) matrices estimators using high frequency data, which are asynchronous, and potentially contaminated by the market microstructure noise. Our estimation strategies rely on the pre-averaging method with refresh time to solve the microstructure problems, while using three different specifications of factor models, and their corresponding estimators, respectively, to battle against the curse of dimensionality.

To estimate a factor model, we either adopt the time-series regression (TSR) to recover loadings if factors are known, or use the cross-sectional regression (CSR) to recover factors from known loadings, or use the principal component analysis (PCA) if neither factors nor their loadings are assumed known. We compare the convergence rates in these scenarios using the joint in-fill and increasing dimensionality asymptotic.

To evaluate the empirical trade-off between robustness to model misspecification and statistical efficiency among all estimators, we run a horse race on the out-of-sample portfolio allocation with Dow Jones 30, S&P 100, and S&P 500 index constituents, respectively.

Authors who are presenting talks have a * after their name.

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