Performance of ARCH and GARCH Models in Forecasting Cryptocurrency Market Volatility

Almansour, Bashar Yaser; Alshater, Muneer M.; Almansour, Ammar Yaser

doi:10.7232/iems.2021.20.2.130

Cited by 17 publications

(6 citation statements)

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“…Financial time series data were used in this study. Therefore, it is important to investigate whether the unit root exists in the data series or not because non-stationary variables can cause false regression problems among unconnected variables [16]. The ADF results are shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Analysis of the Effect of Exchange Rate Fluctuations on Bitcoin Returns: A Time Series Approach in a Digital Finance Perspective

Metaxas

2024

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Cryptocurrencies, including Bitcoin, have been the subject of great attention in the media and academia due to their large price fluctuations. This study aims to analyze the effect of exchange rate fluctuations (USD/AUD, USD/EUR, USD/GBP, and USD/JPY) on Bitcoin returns in the period 2014 to 2019 using a time series approach. We applied the ARMA model and the Maximum Likelihood Estimation method to analyze the data. The results showed that exchange rate fluctuations had no significant effect on Bitcoin's returns when confidence was measured at a rate of 95 percent. However, when significance was measured at a rate of 90 percent, GBP was found to have a significant effect on Bitcoin returns. This may be due to common factors affecting the simultaneous returns of USD/GBP and Bitcoin in recent times. Future studies may consider investigating the effect of exchange rate fluctuations on other cryptocurrencies, other than Bitcoin, to gain a more comprehensive understanding of the effect of exchange rates on digital assets in a digital finance perspective.

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Section: Resultsmentioning

confidence: 99%

Analysis of the Effect of Exchange Rate Fluctuations on Bitcoin Returns: A Time Series Approach in a Digital Finance Perspective

Metaxas

2024

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“…Almansour, Alshater ve Almansour (2021), kripto varlık piyasasının %80'inden fazlasını temsil eden 9 kripto paranın volatilitesini ARCH ve GARCH modelleri ile incelemişlerdir. ARCH ve GARCH modellerinin kripto paraların piyasa volatilitesini tahmin etmede anlamlı bir etkisinin olduğunu ve tüm kripto paraların volatilitesinin iyi ve kötü haberlerden etkilendiğini tespit etmişlerdir.…”

Section: Literatür öZetiunclassified

Twitter Bazlı Belirsizlik Endeksi Kripto Paraların Volatilitesini Etkiler mi?

Gazel¹

2021

Ekonomi, Politika &Amp; Finans Araştırmaları Dergisi

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Bu çalışma 2011 yılından itibaren temel olarak "belirsizlik" ve "ekonomi" anahtar kelimelerini içeren tweetlerin baz alınarak oluşturulduğu Twitter Bazlı Belirsizlik Endeksinin, son yılların gözde yatırım araçlarından olan kripto paraların volatilitesine etkisini incelemeyi amaçlamaktadır. Piyasa değeri en yüksek, Binance, Bitcoin, Cardano, Ethereum, Ripple ve Tether kripto paralar 18/01/2018-11/07/2021 dönemi için günlük verilerle ARCH-GARCH ailesi modelleri ile incelenmiştir. Çalışmada öncelikle ortalama denklemi oluşturulan modellerin ARCH-GARCH modellerine uygunluğu sınanmış ve incelenen dönemde Bitcoin ve Ethereum için ARCH etkisinin olmadığı ancak Binance, Cardano, Ripple ve Tether için volatilite modellerinin kullanımının uygun olduğu bulgusu elde edilmiştir. Binance için GARCH (1,1), Cardano için GARCH-M (1,1), Ripple için ARCH (2) modeli volatiliteyi en iyi yakalayan model olarak seçilmiştir. Twitter Bazlı Belirsizlik Endeksinin bu modellerin hepsinde istatistiki olarak anlamlı ve pozitif bir etkiye sahip olduğu tespit edilmiştir. Bu sonuçlara göre bir sosyal medya platformu olan Twitter'da yer alan belirsizlik ve ekonomi içerikli tweetlerin kripto varlıkların volatilitesini etkilediğini söylemek mümkündür.

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“…Time series data with conditional heteroscedasticity, namely time series data with non-uniform variance, are known as time series data with volatility as a measure of uncertainty [2]. The ARCH and GARCH models use heteroscedasticity as the variance to be modeled, so that we know the expected output of the error variance and forecasting itself is an interesting thing, especially in financial applications [3].…”

Section: Introductionmentioning

confidence: 99%

Forecasting The Share Price of PT Merdeka Copper Gold Tbk By Using Arch-Garch Model

Nabilah Faisal,

Nababan,

Sutarman

et al. 2023

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This study aims to obtain a forecasting model and compare PT Merdeka Copper Gold Tbk share price using the time series method, ARCH-GARCH model. The data used is historical data for the period December 2021 – December 2022. The initial steps are the stationarity test, identifying the ARIMA model, and checking the heteroscedasticity effect of the best ARIMA model. Then from this model, identify the ARCH-GARCH model. After the model has been formed, compare those models that have been assumed by using the smallest AIC and SBC values and checking the model's heteroscedasticity effect. The last step is forecasting for January 2023 using GARCH (1,0). The equation is .

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Performance of ARCH and GARCH Models in Forecasting Cryptocurrency Market Volatility

Cited by 17 publications

References 0 publications

Analysis of the Effect of Exchange Rate Fluctuations on Bitcoin Returns: A Time Series Approach in a Digital Finance Perspective

Analysis of the Effect of Exchange Rate Fluctuations on Bitcoin Returns: A Time Series Approach in a Digital Finance Perspective

Twitter Bazlı Belirsizlik Endeksi Kripto Paraların Volatilitesini Etkiler mi?

Forecasting The Share Price of PT Merdeka Copper Gold Tbk By Using Arch-Garch Model

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