Pemodelan Harga Cryptocurrency Menggunakan Markov Switching Autoregressive

Ashariansyah, Akhmad Ridho; Iriawan, Nur; Mukarromah, Adatul

doi:10.12962/j27213862.v3i2.7726

Cited by 3 publications

(6 citation statements)

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“…concluded that the high volatility in cryptocurrencies might be the reason for the high returns in cryptocurrencies. This instrument attracts investors who like high risk and high returns (Ashariansyah et al, 2020). more significant the gap, it shows that the liquidity in an asset is low.…”

Section: Discussionmentioning

confidence: 99%

“…Bitcoin is a newly recognized asset, making this instrument volatile, unstable, and fluctuating very quickly. The cycle of rising bitcoin prices can be mapped through historical data from the halving day cycle; fluctuations in cryptocurrency prices are caused by periodic Hash and Halving levels (Ashariansyah, Iriawan & Mukarromah, 2020). The halving day is a scheme every four years programmed on the bitcoin blockchain, reducing the amount of supply to be circulated by 50% (Meynkhard, 2019).…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Bitcoin Halving Day on Stock Market in Indonesia

Ramadhani

2022

JICP

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This study aims to determine the impact of the bitcoin halving day phenomenon that occurred on May 11, 2020, on abnormal return and trading volume activity on listed firms of index LQ45 on the Indonesia Stock Exchange. At the bitcoin halving day, there was a jump in the price of bitcoin and other cryptocurrencies. Based on the event theory, the researcher wanted to find out whether capital market investors in Indonesia tend to switch investment instruments to the cryptocurrency market with this phenomenon. These data were taken weekly for four weeks before the halving and four weeks after the 5-month halving. Using the Paired sample T-test to determine whether there is a difference in abnormal return and trading volume. The results showed that there was a significant difference between stock price’s abnormal return and trading volume before the halving day and after the bitcoin halving day. The abnormal return did not increase, but the trading volume decreased, which is thought to be due to the halving day

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Impact of Bitcoin Halving Day on Stock Market in Indonesia

Ramadhani

2022

JICP

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“…Metode ini diharapkan dapat memberikan gambaran terkait kasus Covid-19 di Indonesia berdasarkan nilai probabilitas transisi pada setiap state menggunakan model MSAR. [12] dengan menggunakan rumus yaitu :…”

Section: Pendahuluanunclassified

“…Korelasi antara variabel dilihat dari model autoregressive (AR) yang dapat dituliskan dalam persamaan berikut [12] :…”

Section: Metode Penelitianunclassified

Model Markov Switching Autoregressive pada Data Covid-19 di Indonesia

Rizki,

Martha,

Bartolomius

et al. 2024

Jambura J. Probab. Stat.

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The Covid-19 pandemic has had a very influential impact on socio-economic conditions in Indonesia. Forecasting the number of Covid-19 cases is needed to support taking preventive action. The method that can be used to determine the number of Covid-19 cases is a forecasting method using the Markov Switching Autoregressive (MSAR) time series data model as an alternative for analyzing structural change data. This research uses Covid-19 confirmation data in Indonesia for the period March 2020-June 2021, with the aim of designing an MSAR model and calculating the magnitude of the transition opportunity in each state in the Covid-19 confirmation data in Indonesia. The MSAR model begins by describing the data and checking the stationarity of the data. After that, Box-Jenkins modeling was carried out to test heteroskedasticity and structural changes. Next, the MSAR model parameters were estimated and the transition matrix was formed. This research shows that the best MSAR model formed is the MS (2)-AR (5) model, with a static transition probability value in state 1 of 0.981330. However, it appears that there is a chance of 0.018670 for the Covid-19 confirmation condition to move to state 2. Testing in the case of state 2 produces a transition chance of 0.980991 in state 2, with a transition chance of Covid-19 confirmation changing to state 1 of 0.019009.

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“…Mata uang ini dibuat untuk menawarkan kemudahan dan keamanan pembayaran. Mata uang digital bernama cryptocurrency menggunakan sistem kriptografi untuk setiap transaksi data tanpa memerlukan pihak ketiga [3]. Meski banyak peminatnya, berinvestasi pada aset kripto juga memiliki resiko yang cukup besar, karena pergerakan harga yang tidak menentu dan sulitnya untuk memprediksi.…”

Section: Pendahuluanunclassified

Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine

Kasliono,

Candraningrum,

Sari

2023

bits

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The price of cryptocurrencies such as Ethereum often experiences high fluctuations and is difficult to predict. This study aims to predict Ethereum prices using the Extreme Learning Machine (ELM) algorithm which is a fast and efficient machine learning method. Ethereum price data is collected from CoinMarketCap by scraping the data using CoinmarketCap Scraper from the cryptocmd library using Python. An ELM model is built by changing the number of hidden nodes to determine the optimal prediction model of Ethereum prices based on the smallest average MAPE. Model performance was evaluated using the mean absolute percentage error (MAPE) on the test data set. The results show that the ELM model built can predict Ethereum prices with an accuracy of 96.96%. The MAPE obtained is 3.035334%, with 9 hidden nodes in the ELM network architecture model that was built. This shows that the model can explain about 96.96% of the variation in Ethereum price data. Therefore, the ELM model can be used as an aid in making investment decisions

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Pemodelan Harga Cryptocurrency Menggunakan Markov Switching Autoregressive

Cited by 3 publications

References 0 publications

The Impact of Bitcoin Halving Day on Stock Market in Indonesia

The Impact of Bitcoin Halving Day on Stock Market in Indonesia

Model Markov Switching Autoregressive pada Data Covid-19 di Indonesia

Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine

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