scholarly journals Comparing Cross-Section and Time-Series Factor Models

2019 ◽  
Vol 33 (5) ◽  
pp. 1891-1926 ◽  
Author(s):  
Eugene F Fama ◽  
Kenneth R French
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Factor Models ◽  
Constant Factor ◽  
Time Series Models ◽  
Time Varying ◽  
Oxford University ◽  
Regression Approach ◽  
Fama And French ◽  
Published Paper

Abstract We use the cross-section regression approach of Fama and MacBeth (1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (2015). Time-series models that use only cross-section factors provide better descriptions of average returns than time-series models that use time-series factors. This is true when we impose constant factor loadings and when we use time-varying loadings that are natural for time-series factors and time-varying loadings that are natural for cross-section factors. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Contingent Capital Trigger Effects: Evidence from Liability Management Exercises

2019 ◽  
Vol 8 (2) ◽  
pp. 235-259 ◽  
Author(s):  
Boris Vallée
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Web Site ◽  
Regulatory Capital ◽  
Oxford University ◽  
Contingent Capital ◽  
Liability Management ◽  
Editorial Decision ◽  
Debt Holders ◽  
Published Paper

AbstractThis paper studies liability management exercises (LME) by banks, which have comparable regulatory capital effects than contingent capital triggers. LMEs are concentrated on low capitalization situations, both in the cross-section and in the time series and are frequently associated with equity issuances. These exercises prove effective at improving bank capitalization levels. The market reaction to LMEs is positive and mostly accrues to debt holders. These findings strengthen the case for innovative liabilities securities as a tool to improve bank resilience.Received February 8, 2019; editorial decision May 16, 2019 by Editor Andrew Ellul. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Taking Advantage of an Untapped Pool: Assessing the Success of African American Head Coaches in the National Football League

2008 ◽  
Vol 35 (4) ◽  
pp. 129-146 ◽  
Author(s):  
David Branham
Keyword(s):  
Time Series ◽  
African American ◽  
African Americans ◽  
Cross Section ◽  
Fixed Effects ◽  
National Football League ◽  
Head Coaches ◽  
Time Series Models

Noting that only five African American coaches had been hired to lead National Football League (NFL) teams from 1989–2002, Madden (J of Sports Econ, 5(1):6–19 2004) found that teams coached by African Americans in the NFL outperformed their counterparts in the regular season but were significantly below average in the playoffs. This analysis, with data that includes nine African American coaches and extends through 2007, reconfirms Madden's finding that African American head coaches outperform their rivals in the regular season, but also finds that African American coaches no longer suffer from poor playoff performance. Using fixed effects pooled cross section time series models, this analysis confirms that teams with African American head coaches can expect more wins in the regular season than their peers, other things equal. However, there is some evidence that as the pool of African American coaching talent diminishes from additional hires their extraordinary performance may be slightly regressing. The playoff analysis shows that that when controlling for seeding, organizational strength and regular season wins, African American coaches perform at the same level as their counterparts.

scholarly journals Publication Bias and the Cross-Section of Stock Returns

2019 ◽  
Vol 10 (2) ◽  
pp. 249-289 ◽  
Author(s):  
Andrew Y Chen ◽  
Tom Zimmermann
Keyword(s):  
Stock Returns ◽  
Publication Bias ◽  
Cross Section ◽  
Standard Error ◽  
P Value ◽  
Oxford University ◽  
Percentage Points ◽  
Sample Data ◽  
Out Of Sample ◽  
Published Paper

Abstract We develop an estimator for publication bias-adjusted returns and apply it to 156 published long-short portfolios. Our adjustment uses only in-sample data and provides sharper inferences than out-of-sample tests. Bias-adjusted returns are only 12.3% smaller than in-sample returns with a standard error of 1.7 percentage points. The small bias comes from the dispersion of returns across predictors, which is too large to be explained by data-mined noise. The bias is much smaller than post-publication decay (p-value ¡.0001), suggesting mispricing is important. Our results offer a different perspective about recent papers that find most published predictors are likely false. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Understanding Mortgage Spreads

2019 ◽  
Vol 32 (10) ◽  
pp. 3799-3850 ◽  
Author(s):  
Nina Boyarchenko ◽  
Andreas Fuster ◽  
David O Lucca
Keyword(s):  
Risk Factors ◽  
Time Series ◽  
The United States ◽  
Pricing Model ◽  
Cross Sectional ◽  
Yield Spreads ◽  
Oxford University ◽  
Mortgage Backed Securities ◽  
Editorial Decision ◽  
Published Paper

Abstract Because most mortgages in the United States are securitized in agency mortgage-backed securities (MBS), yield spreads on MBS are a key determinant of homeowners’ funding costs. We study variation in MBS spreads in the time series and across securities and document that MBS spreads show a pronounced cross-sectional smile with respect to the securities’ coupon rates. We present a new pricing model that uses “stripped” MBS prices to identify the contribution of non-interest-rate prepayment risk to spreads and find that this risk explains the smile, whereas the time-series spread variation is mostly accounted for by nonprepayment risk factors. Received March 30, 2015; editorial decision November 21, 2018 by Editor Leonid Kogan. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Wavelet-M-Estimation for Time-Varying Coefficient Time Series Models

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xingcai Zhou ◽  
Fangxia Zhu
Keyword(s):  
Time Series ◽  
Asymptotic Properties ◽  
Time Series Models ◽  
Time Varying ◽  
Bahadur Representation ◽  
Conditional Quantile ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
M Estimation ◽  
Time Varying Coefficients

This paper proposes wavelet-M-estimation for time-varying coefficient time series models by using a robust-type wavelet technique, which can adapt to local features of the time-varying coefficients and does not require the smoothness of the unknown time-varying coefficient. The wavelet-M-estimation has the desired asymptotic properties and can be used to estimate conditional quantile and to robustify the usual mean regression. Under mild assumptions, the Bahadur representation and the asymptotic normality of wavelet-M-estimation are established.

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