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
Computational Economics – Springer Journals
Published: Jun 6, 2018
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