On Jackknife-After-Bootstrap Method for Dependent Data

On Jackknife-After-Bootstrap Method for Dependent Data Comput Econ https://doi.org/10.1007/s10614-018-9827-4 On Jackknife-After-Bootstrap Method for Dependent Data 1 1 Ufuk Beyaztas · Beste H. Beyaztas Accepted: 28 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we adapt sufficient and ordered non-overlapping block boot- srap methods into jackknife-after-bootstrap (JaB) algorithm to estimate the standard error of a statistic where observations form a stationary sequence. We also extend the JaB algorithm to obtain prediction intervals for future returns and volatilities of GARCH processes. The finite sample properties of the proposed methods are illus- trated by an extensive simulation study and they are applied to S&P 500 stock index data. Our findings reveal that the proposed algorithm often exhibits improved perfor- mance and, is computationally more efficient compared to conventional JaB method. Keywords Financial time series · Prediction · Resampling methods Mathematics Subject Classification 62F40 · 92B84 · 62M20 1 Introduction Measuring volatility and construction of valid predictions for future returns and volatil- ities have an important role in assessing risk and uncertainty in the financial market. To this end, the generalized autoregressive conditionally heteroscedastic (GARCH) model proposed by Bollerslev (1986) is one of the most commonly used technique for modelling volatility http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Economics Springer Journals

On Jackknife-After-Bootstrap Method for Dependent Data

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Economics; Economic Theory/Quantitative Economics/Mathematical Methods; Computer Appl. in Social and Behavioral Sciences; Operations Research/Decision Theory; Behavioral/Experimental Economics; Math Applications in Computer Science
ISSN
0927-7099
eISSN
1572-9974
D.O.I.
10.1007/s10614-018-9827-4
Publisher site
See Article on Publisher Site

Abstract

Comput Econ https://doi.org/10.1007/s10614-018-9827-4 On Jackknife-After-Bootstrap Method for Dependent Data 1 1 Ufuk Beyaztas · Beste H. Beyaztas Accepted: 28 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we adapt sufficient and ordered non-overlapping block boot- srap methods into jackknife-after-bootstrap (JaB) algorithm to estimate the standard error of a statistic where observations form a stationary sequence. We also extend the JaB algorithm to obtain prediction intervals for future returns and volatilities of GARCH processes. The finite sample properties of the proposed methods are illus- trated by an extensive simulation study and they are applied to S&P 500 stock index data. Our findings reveal that the proposed algorithm often exhibits improved perfor- mance and, is computationally more efficient compared to conventional JaB method. Keywords Financial time series · Prediction · Resampling methods Mathematics Subject Classification 62F40 · 92B84 · 62M20 1 Introduction Measuring volatility and construction of valid predictions for future returns and volatil- ities have an important role in assessing risk and uncertainty in the financial market. To this end, the generalized autoregressive conditionally heteroscedastic (GARCH) model proposed by Bollerslev (1986) is one of the most commonly used technique for modelling volatility

Journal

Computational EconomicsSpringer Journals

Published: Jun 6, 2018

References

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