IDEAS home Printed from https://ideas.repec.org/a/vrs/eaiada/v26y2022i2p15-29n1.html
   My bibliography  Save this article

Applying Block Bootstrap Methods in Silver Prices Forecasting

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.15611/eada.2022.2.02
    Download Restriction: no
    File URL: https://libkey.io/10.15611/eada.2022.2.02?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dooley, Gillian & Lenihan, Helena, 2005. "An assessment of time series methods in metal price forecasting," Resources Policy, Elsevier, vol. 30(3), pages 208-217, September.
    2. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
    3. Daniel J. Nordman & Soumendra N. Lahiri, 2012. "Block Bootstraps for Time Series With Fixed Regressors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 233-246, March.
    4. Bergmeir, Christoph & Hyndman, Rob J. & Benítez, José M., 2016. "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 303-312.
    5. Parisi, Antonino & Parisi, Franco & Díaz, David, 2008. "Forecasting gold price changes: Rolling and recursive neural network models," Journal of Multinational Financial Management, Elsevier, vol. 18(5), pages 477-487, December.
    6. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    7. He, Kaijian & Lu, Xingjing & Zou, Yingchao & Keung Lai, Kin, 2015. "Forecasting metal prices with a curvelet based multiscale methodology," Resources Policy, Elsevier, vol. 45(C), pages 144-150.
    8. Andrés Alonso & Daniel Peña & Juan Romo, 2006. "Introducing model uncertainty by moving blocks bootstrap," Statistical Papers, Springer, vol. 47(2), pages 167-179, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ozdemir, Ali Can & Buluş, Kurtuluş & Zor, Kasım, 2022. "Medium- to long-term nickel price forecasting using LSTM and GRU networks," Resources Policy, Elsevier, vol. 78(C).
    2. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine," Resources Policy, Elsevier, vol. 69(C).
    3. Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022. "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, vol. 78(C).
    4. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    5. Sheng‐Tun Li & Kuei‐Chen Chiu & Chien‐Chang Wu, 2023. "Apply big data analytics for forecasting the prices of precious metals futures to construct a hedging strategy for industrial material procurement," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 942-959, March.
    6. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    7. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
    8. Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    9. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
    10. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018. "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 15-25.
    11. Devendra Joshi & Premkumar Chithaluru & Divya Anand & Fahima Hajjej & Kapil Aggarwal & Vanessa Yelamos Torres & Ernesto Bautista Thompson, 2023. "RETRACTED: An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    12. Li, Wenlan & Cheng, Yuxiang & Fang, Qiang, 2020. "Forecast on silver futures linked with structural breaks and day-of-the-week effect," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    13. Zhao, Jue & Hosseini, Shahab & Chen, Qinyang & Jahed Armaghani, Danial, 2023. "Super learner ensemble model: A novel approach for predicting monthly copper price in future," Resources Policy, Elsevier, vol. 85(PB).
    14. Rubaszek, Michał & Karolak, Zuzanna & Kwas, Marek, 2020. "Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 65(C).
    15. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    16. Chen, Yanhui & He, Kaijian & Zhang, Chuan, 2016. "A novel grey wave forecasting method for predicting metal prices," Resources Policy, Elsevier, vol. 49(C), pages 323-331.
    17. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
    18. Yifei Zhao & Jianhong Chen & Hideki Shimada & Takashi Sasaoka, 2023. "Non-Ferrous Metal Price Point and Interval Prediction Based on Variational Mode Decomposition and Optimized LSTM Network," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
    19. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.
    20. Zhou, Jianguo & Xu, Zhongtian, 2023. "A novel three-stage hybrid learning paradigm based on a multi-decomposition strategy, optimized relevance vector machine, and error correction for multi-step forecasting of precious metal prices," Resources Policy, Elsevier, vol. 80(C).

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:eaiada:v:26:y:2022:i:2:p:15-29:n:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.