The Econometrics Journal News

Large Dimensional Models Videos Now Available

  • Published Date: 11 June 2014

The “Large Panel Test of Factor Pricing Models” and the “General Dynamic Factors and Volatilities” presentation by Jianqing Fan (Princeton) and Marc Hallin (ECARES ULB and ORFE Princeton) respectively delivered at the RES Conference in Manchester 2014 are now available for viewing.

In “Large Panel Test of Factor Pricing Models” authors consider testing the high-dimensional multi-factor pricing model, with the number of assets much larger than the length of time series. Most of the existing tests are based on a quadratic form of estimated alphas. They suffer from low powers, however, due to the accumulation of errors in estimating high-dimensional parameters that overrides the signals of non-vanishing alphas. To resolve this issue, we develop a new class of tests, called ``power enhancement" tests. It strengthens the power of existing tests in important sparse alternative hypotheses where market inefficiency is caused by a small portion of stocks with significant alphas.

The power enhancement component is asymptotically negligible under the null hypothesis and hence does not distort much the size of the original test.

Yet, it becomes large in a specific region of the alternative hypothesis and therefore significantly enhances the power. In particular, we design a screened Wald-test that enables us to detect and identify individual stocks with significant alphas. We also develop a feasible Wald statistic using a regularized high-dimensional covariance matrix. By combining those two, our proposed method achieves power enhancement while controlling the size, which is illustrated by extensive simulation studies and empirically applied to the components in the S&P 500 index. Our empirical study shows that market inefficiency is primarily caused by merely a few stocks with significant alphas, most of which are positive, instead of a large portion of slightly mis-priced assets.

Click to view:

Start time: 0:00:37 End time: 0:52:58

The “General Dynamic Factors and Volatilities” presentation considers large panels of time series admitting a dynamic factor structure on the levels or returns, common volatility does not necessarily originate in the common shocks, but is likely to be present also in idiosyncratic components. The volatilities of the common components and those of the idiosyncratic both admit dynamic factor structures and therefore each of them decomposes into common and idiosyncratic volatilities of the common, and common and idiosyncratic volatilities of the idiosyncratic returns, respectively. The volatility-common shocks in the returns-common components may or may not be related to the volatility-common shocks in the returns-idiosyncratic components. Based on this observation, we propose a two-stage generalized dynamic factor model accounting both for the factor structure of returns and for the factor structure of the volatilities in the common and idiosyncratic components of the returns. When applied to S&P100 asset returns data, we find that a considerable proportion of the common volatility of returns originates in their idiosyncratic components.

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Start time: 0:53:37 End time: 1:35:53

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