Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/217147 
Year of Publication: 
2019
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 10 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2019 [Pages:] 419-456
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
We develop tests for deciding whether a large cross-section of asset prices obey an exact factor structure at the times of factor jumps. Such jump dependence is implied by standard linear factor models. Our inference is based on a panel of asset returns with asymptotically increasing cross-sectional dimension and sampling frequency, and essentially no restriction on the relative magnitude of these two dimensions of the panel. The test is formed from the high-frequency returns at the times when the risk factors are detected to have a jump. The test statistic is a cross-sectional average of a measure of discrepancy in the estimated jump factor loadings of the assets at consecutive jump times. Under the null hypothesis, the discrepancy in the factor loadings is due to a measurement error, which shrinks with the increase of the sampling frequency, while under an alternative of a noisy jump factor model this discrepancy contains also nonvanishing firm-specific shocks. The limit behavior of the test under the null hypothesis is nonstandard and reflects the strong-dependence in the cross-section of returns as well as their heteroskedasticity which is left unspecified. We further develop estimators for assessing the magnitude of firm-specific risk in asset prices at the factor jump events. Empirical application to S&P 100 stocks provides evidence for exact one-factor structure at times of big market-wide jump events.
Subjects: 
Factor model
panel
high-frequency data
jumps
semimartingale,specification test
stochastic volatility
JEL: 
C51
C52
G12
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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