A Smooth Block Bootstrap for Statistical Functionals and Time Series

2015 ◽  
Vol 36 (3) ◽  
pp. 442-461 ◽  
Author(s):  
Karl B. Gregory ◽  
Soumendra N. Lahiri ◽  
Daniel J. Nordman
Keyword(s):  
Time Series ◽  
Block Bootstrap ◽  
Statistical Functionals

A New Non-Parametric Subspace-Based Approach for Unit Root Test

2019 ◽  
Vol 18 (01) ◽  
pp. 1950009 ◽  
Author(s):  
Mehran Farahikia ◽  
Masoud Yarmohammadi ◽  
Hossein Hassani
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Unit Root Test ◽  
Statistical Testing ◽  
Block Bootstrap ◽  
Testing Methodology ◽  
Non Parametric

A new non-parametric subspace-based approach is introduced for unit root test in AR(1) process. The proposed approach contains block-bootstrap and spectrum properties of a time series as well as statistical testing methodology. The simulation result validates the proposed test and indicates its superiority compared with other existent procedures.

scholarly journals Models for Expected Returns with Statistical Factors

2020 ◽  
Vol 13 (12) ◽  
pp. 314
Author(s):  
José Manuel Cueto ◽  
Aurea Grané ◽  
Ignacio Cascos
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Stock Prices ◽  
Bootstrap Method ◽  
Block Bootstrap ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Multifactor Models ◽  
Two Factors ◽  
Resampling Procedure

In this paper, we propose multifactor models for the pan-European Equity Market using a block-bootstrap method and compare the results with those of traditional inferential techniques. The new factors are built from statistical measurements on stock prices—in particular, coefficient of variation, skewness, and kurtosis. Data come from Reuters, correspond to nearly 2000 EU companies, and span from January 2008 to February 2018. Regarding methodology, we propose a non-parametric resampling procedure that accounts for time dependency in order to test the validity of the model and the significance of the parameters involved. We compare our bootstrap-based inferential results with classical proposals (based on F-statistics). Methods under assessment are time-series regression, cross-sectional regression, and the Fama–MacBeth procedure. The main findings indicate that the two factors that better improve the Capital Asset Pricing Model with regard to the adjusted R2 in the time-series regressions are the skewness and the coefficient of variation. For this reason, a model including those two factors together with the market is thoroughly studied. We also observe that our block-bootstrap methodology seems to be more conservative with the null of the GRS test than classical procedures.

Matched block bootstrap for resampling multiseason hydrologic time series

2005 ◽  
Vol 19 (18) ◽  
pp. 3659-3682 ◽  
Author(s):  
V. V. Srinivas ◽  
K. Srinivasan
Keyword(s):  
Time Series ◽  
Block Bootstrap ◽  
Hydrologic Time Series

A GENERALIZED BLOCK BOOTSTRAP FOR SEASONAL TIME SERIES

2013 ◽  
Vol 35 (2) ◽  
pp. 89-114 ◽  
Author(s):  
Anna E. Dudek ◽  
Jacek Leśkow ◽  
Efstathios Paparoditis ◽  
Dimitris N. Politis
Keyword(s):  
Time Series ◽  
Block Bootstrap ◽  
Seasonal Time Series

scholarly journals A smooth block bootstrap for quantile regression with time series

2018 ◽  
Vol 46 (3) ◽  
pp. 1138-1166 ◽  
Author(s):  
Karl B. Gregory ◽  
Soumendra N. Lahiri ◽  
Daniel J. Nordman
Keyword(s):  
Time Series ◽  
Quantile Regression ◽  
Block Bootstrap

scholarly journals How to Explain the Cross-Section of Equity Returns through Common Principal Components

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1011
Author(s):  
José Manuel Cueto ◽  
Aurea Grané ◽  
Ignacio Cascos
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Principal Components ◽  
Equity Returns ◽  
Block Bootstrap ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Multifactor Models ◽  
Multifactor Model ◽  
Common Principal Components

In this paper, we propose a procedure to obtain and test multifactor models based on statistical and financial factors. A major issue in the factor literature is to select the factors included in the model, as well as the construction of the portfolios. We deal with this matter using a dimensionality reduction technique designed to work with several groups of data called Common Principal Components. A block-bootstrap methodology is developed to assess the validity of the model and the significance of the parameters involved. Data come from Reuters, correspond to nearly 1250 EU companies, and span from October 2009 to October 2019. We also compare our bootstrap-based inferential results with those obtained via classical testing proposals. Methods under assessment are time-series regression and cross-sectional regression. The main findings indicate that the multifactor model proposed improves the Capital Asset Pricing Model with regard to the adjusted-R2 in the time-series regressions. Cross-section regression results reveal that Market and a factor related to Momentum and mean of stocks’ returns have positive risk premia for the analyzed period. Finally, we also observe that tests based on block-bootstrap statistics are more conservative with the null than classical procedures.

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