Heteroskedasticity in Excess Bitcoin Return Data: Google Trend vs. GARCH Effects

• Autorzy: Chamil W. Senarathne , Tijana Šoja

Warianty tytułu

Heteroskedastyczność w danych zwrotu bitcoinów: trend Google vs. efekty GARCH

• Języki publikacji: EN

Abstrakty

EN

This paper examines the mixture of distribution properties associated with heteroskedastic excess Bitcoin return data, using the volume of Google search queries as a proxy for the information arrival time, from a monthly data sampling period of June 2010 to May 2019. The results show that the volatility coefficients become highly statistically insignificant when the lagged volume of search queries is included in the conditional variance equation of the GJR-GARCH-M model. This clearly suggests that the volume of search queries is shown to provide significant explanatory power regarding the variance of heteroskedastic excess Bitcoin return, which can be traced from the ARCH process defined in the GJR-GARCH-M specification. A significant negative relationship between the conditional volatility and the volume of search queries indicates that Internet (online) information arrival reduces the risk premium in the Bitcoin market, which may improve market stability.(original abstract)

W artykule przeanalizowano właściwości dystrybucji związanych z heteroskedastycznymi punktami zwrotnymi nadwyżki bitcoinów, wykorzystując zapytania w wyszukiwarce Google jako przybliżenie czasu dostarczenia informacji w okresach miesięcznych od czerwca 2010 r. do maja 2019 r. W analizowanym okresie badanie pokazuje, że współczynniki zmienności stają się wysoce nieistotne statystycznie, gdy opóźniona liczebność zapytań w Google jest uwzględniona w równaniu wariancji warunkowej modelu GJR-GARCH-M. To sugeruje, iż liczba zapytań zapewnia znaczną moc wyjaśniającą w odniesieniu do wariantu heteroskedastycznego zwrotu nadmiaru bitcoinów, który można prześledzić na podstawie procesu ARCH zdefiniowanego w specyfikacji GJR-GARCH-M. Znaczny negatywny związek między zmiennością warunkową a liczbą zapytań wyszukiwania wskazuje, że pojawienie się informacji w Internecie (online) zmniejsza premię za ryzyko na rynku bitcoinów, co może poprawić stabilność rynku.(abstrakt oryginalny)

Słowa kluczowe

EN

Bitcoin; Data flow; GARCH model

PL

Bitcoin; Przepływ informacji; Model GARCH

• Czasopismo: Nauki o Finansach / Uniwersytet Ekonomiczny we Wrocławiu
• Rocznik: 2019
• Tom: 24
• Numer: nr 3
• Strony: 35--45

Twórcy

autor

Chamil W. Senarathne

• School of Economics, Wuhan University of Technology, China

autor

Tijana Šoja

• Central Bank of Bosnia and Herzegovina, Sarajevo, Bosnia and Herzegovina

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Identyfikatory

DOI

10.15611/fins.2019.3.04