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Applying Block Bootstrap Methods in Silver Prices Forecasting

Author

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  • Sroka Łukasz

    (University of Economics in Katowice, Katowice, Poland)

Abstract

This article focuses on the presentation of the forecasting possibilities of bootstrap methods used to predict prices based on time series. The aim of the paper was to examine the quality of the forecasts made with the methods for silver futures contracts. In order to achieve the intended goal, ex-post and ex-ante errors for the forecasts prepared by applying bootstrap methods were analysed. The forecasts were calculated using the daily closing prices of the silver futures contracts for the period from 01/07/2020 to 27/03/2022 The analysis showed that the quality of forecasts for each of the presented methods is at a satisfactory level. Moreover, the forecasts calculated using the bootstrap methods were closer to the real performance of the silver futures contracts than the forecasts obtained using the ARMA model (1,1). In addition, it was shown that the forecasts made with the tapered block bootstrap method are less affected by forecast errors than the other analysed methods.

Suggested Citation

  • Sroka Łukasz, 2022. "Applying Block Bootstrap Methods in Silver Prices Forecasting," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 26(2), pages 15-29, June.
    • Handle: RePEc:vrs:eaiada:v:26:y:2022:i:2:p:15-29:n:1
    DOI: 10.15611/eada.2022.2.02
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    block bootstrap; price forecasting; silver futures contracts;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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