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    A hypothesis test for the goodness-of-fit of the marginal distribution of a time series with application to Stablecoin data

    Levene, Mark (2021) A hypothesis test for the goodness-of-fit of the marginal distribution of a time series with application to Stablecoin data. Engineering Proceedings , ISSN 2673-4591. (In Press)

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    Abstract

    Abootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen-Shannon divergence (ESJS) and the Kolmogorov-Smirnov two-sample test statistic (KS2), are compared on four data sets, three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit a heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

    Metadata

    Item Type: Article
    Additional Information: ITISE 2021. 19th-21th July, Gran Canaria, Spain. http://itise.ugr.es/index.php
    Keyword(s) / Subject(s): cryptocurrency, Bitcoin, stablecoin, marginal distribution, heavy-tails, stationary process, stable distribution, goodness-of-fit, survival Jensen-Shannon divergence
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 25 Jun 2021 12:56
    Last Modified: 27 Jun 2021 05:56
    URI: https://eprints.bbk.ac.uk/id/eprint/44863

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