APPLICATION OF HYBRID LSTAR-GARCH MODEL WITH EXPECTED TAILL LOSS IN PREDICTING THE PRICE MOVEMENT OF BITCOIN CRYPTOCURRENCY AGAINST RUPIAH CURRENCY

Authors

  • Yanna Rezki Fadillah Universitas Islam Negeri Sumatera Utara
  • Rina Widyasari Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.30829/zero.v7i1.17149

Keywords:

Forecasting, Time series, Bitcoin, LSTAR, GARCH, Expected Taill Loss

Abstract

Time series data from bitcoin has nonlinear data fluctuations so that a model is needed that can accommodate data with these conditions. The method that can be used for nonlinear time series data cases such as bitcoin is the LSTAR-GARCH model. LSTAR-GARCH is a combination of the LSTAR model and the GARCH model. Bitcoin investment also contains an element of risk. To find out the value of risk, the Expected Tail Loss risk measurement tool can be used. Expected Tail Loss (ETL). The data used in this study are historical daily bitcoin price data for the period April 1, 2022 to April 1, 2023. The modeling results obtained based on the MAPE value show that the LSTAR-GARCH model is the best model with the smallest MAPE value of 30% compared to the AR, LSTAR, or AR-GARCH models. The expected Taill loss value of bitcoin is -0.06784.

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Published

2023-08-24

Issue

Vol. 7 No. 1 (2023): Zero: Jurnal Sains Matematika dan Terapan

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Articles

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