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Volatility forecasting for interbank offered rate using grey extreme learning machine: The case of China

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  • Liu, Xiaoyong
  • Fu, Hui

Abstract

Interbank Offered rate is the only direct market rate in China’s currency market. Volatility forecasting of China Interbank Offered Rate (IBOR) has a very important theoretical and practical significance for financial asset pricing and financial risk measure or management. However, IBOR is a dynamics and non-steady time series whose developmental changes have stronger random fluctuation, so it is difficult to forecast the volatility of IBOR. This paper offers a hybrid algorithm using grey model and extreme learning machine (ELM) to forecast volatility of IBOR. The proposed algorithm is composed of three phases. In the first, grey model is used to deal with the original IBOR time series by accumulated generating operation (AGO) and weaken the stochastic volatility in original series. And then, a forecasting model is founded by using ELM to analyze the new IBOR series. Lastly, the predictive value of the original IBOR series can be obtained by inverse accumulated generating operation (IAGO). The new model is applied to forecasting Interbank Offered Rate of China. Compared with the forecasting results of BP and classical ELM, the new model is more efficient to forecasting short- and middle-term volatility of IBOR.

Suggested Citation

  • Liu, Xiaoyong & Fu, Hui, 2016. "Volatility forecasting for interbank offered rate using grey extreme learning machine: The case of China," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 249-254.
    • Handle: RePEc:eee:chsofr:v:89:y:2016:i:c:p:249-254
    DOI: 10.1016/j.chaos.2015.11.033
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    References listed on IDEAS

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    1. Naimzada, Ahmad & Pireddu, Marina, 2015. "Real and financial interacting markets: A behavioral macro-model," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 111-131.
    2. Rajagopal, 2015. "Chaos in Markets," Palgrave Macmillan Books, in: The Butterfly Effect in Competitive Markets, chapter 1, pages 3-29, Palgrave Macmillan.
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    Cited by:

    1. Lahmiri, Salim & Bekiros, Stelios, 2017. "Disturbances and complexity in volatility time series," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 38-42.
    2. Lahmiri, Salim & Bekiros, Stelios & Salvi, Antonio, 2018. "Long-range memory, distributional variation and randomness of bitcoin volatility," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 43-48.
    3. Mojtaba Qolipour & Ali Mostafaeipour & Mohammad Saidi-Mehrabad & Hamid R Arabnia, 2019. "Prediction of wind speed using a new Grey-extreme learning machine hybrid algorithm: A case study," Energy & Environment, , vol. 30(1), pages 44-62, February.
    4. Liu, Weiping & Wang, Chengzhu & Li, Yonggang & Liu, Yishun & Huang, Keke, 2021. "Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    5. Luo, Xilin & Duan, Huiming & He, Leiyuhang, 2020. "A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy," Energy, Elsevier, vol. 205(C).

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