Predicting the Volatility of Cryptocurrency Time–Series

Research output: Contribution to book/anthology/report/proceedingBook chapterResearchpeer-review

Standard

Predicting the Volatility of Cryptocurrency Time–Series. / Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco.

Mathematical and Statistical Methods for Actuarial Sciences and Finance. ed. / Marco Corazza; María Durbán; Aurea Grané; Cira Perna; Marilena Sibillo. Springer, 2018. p. 203-207.

Research output: Contribution to book/anthology/report/proceedingBook chapterResearchpeer-review

Harvard

Catania, L, Grassi, S & Ravazzolo, F 2018, Predicting the Volatility of Cryptocurrency Time–Series. in M Corazza, M Durbán, A Grané, C Perna & M Sibillo (eds), Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, pp. 203-207, MAF Conference, Madrid, Spain, 04/04/2018. <https://brage.bibsys.no/xmlui/bitstream/handle/11250/2482825/WP_CAMP_3_2018.pdf>

APA

Catania, L., Grassi, S., & Ravazzolo, F. (2018). Predicting the Volatility of Cryptocurrency Time–Series. In M. Corazza, M. Durbán, A. Grané, C. Perna, & M. Sibillo (Eds.), Mathematical and Statistical Methods for Actuarial Sciences and Finance (pp. 203-207). Springer. https://brage.bibsys.no/xmlui/bitstream/handle/11250/2482825/WP_CAMP_3_2018.pdf

CBE

Catania L, Grassi S, Ravazzolo F. 2018. Predicting the Volatility of Cryptocurrency Time–Series. Corazza M, Durbán M, Grané A, Perna C, Sibillo M, editors. In Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer. pp. 203-207.

MLA

Catania, Leopoldo, Stefano Grassi and Francesco Ravazzolo "Predicting the Volatility of Cryptocurrency Time–Series"., Corazza, Marco, Durbán, María Grané, Aurea Perna, Cira Sibillo, Marilena (editors). Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer. 2018, 203-207.

Vancouver

Catania L, Grassi S, Ravazzolo F. Predicting the Volatility of Cryptocurrency Time–Series. In Corazza M, Durbán M, Grané A, Perna C, Sibillo M, editors, Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer. 2018. p. 203-207

Author

Catania, Leopoldo ; Grassi, Stefano ; Ravazzolo, Francesco. / Predicting the Volatility of Cryptocurrency Time–Series. Mathematical and Statistical Methods for Actuarial Sciences and Finance. editor / Marco Corazza ; María Durbán ; Aurea Grané ; Cira Perna ; Marilena Sibillo. Springer, 2018. pp. 203-207

Bibtex

@inbook{f2a02ed246f248628006486018e299cb,
title = "Predicting the Volatility of Cryptocurrency Time–Series",
abstract = "Cryptocurrencies have recently gained a lot of interest from investors,central banks and governments worldwide. The lack of any form of political regu-lation and their market far from being “efficient”, require new forms of regulationin the near future. From an econometric viewpoint, the process underlying the evo-lution of the cryptocurrencies{\textquoteright} volatility has been found to exhibit at the same timedifferences and similarities with other financial time–series, e.g. foreign exchangesreturns. This short note focuses on predicting the conditional volatility of the fourmost traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. We investi-gate the effect of accounting for long memory in the volatility process as well asits asymmetric reaction to past values of the series to predict: one day, one and twoweeks volatility levels.",
author = "Leopoldo Catania and Stefano Grassi and Francesco Ravazzolo",
note = "Selected short papers; MAF Conference ; Conference date: 04-04-2018 Through 06-04-2018",
year = "2018",
language = "English",
isbn = "978-3-319-89823-0",
pages = "203--207",
editor = "Marco Corazza and Mar{\'i}a Durb{\'a}n and Aurea Gran{\'e} and Cira Perna and Marilena Sibillo",
booktitle = "Mathematical and Statistical Methods for Actuarial Sciences and Finance",
publisher = "Springer",

}

RIS

TY - CHAP

T1 - Predicting the Volatility of Cryptocurrency Time–Series

AU - Catania, Leopoldo

AU - Grassi, Stefano

AU - Ravazzolo, Francesco

N1 - Selected short papers

PY - 2018

Y1 - 2018

N2 - Cryptocurrencies have recently gained a lot of interest from investors,central banks and governments worldwide. The lack of any form of political regu-lation and their market far from being “efficient”, require new forms of regulationin the near future. From an econometric viewpoint, the process underlying the evo-lution of the cryptocurrencies’ volatility has been found to exhibit at the same timedifferences and similarities with other financial time–series, e.g. foreign exchangesreturns. This short note focuses on predicting the conditional volatility of the fourmost traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. We investi-gate the effect of accounting for long memory in the volatility process as well asits asymmetric reaction to past values of the series to predict: one day, one and twoweeks volatility levels.

AB - Cryptocurrencies have recently gained a lot of interest from investors,central banks and governments worldwide. The lack of any form of political regu-lation and their market far from being “efficient”, require new forms of regulationin the near future. From an econometric viewpoint, the process underlying the evo-lution of the cryptocurrencies’ volatility has been found to exhibit at the same timedifferences and similarities with other financial time–series, e.g. foreign exchangesreturns. This short note focuses on predicting the conditional volatility of the fourmost traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. We investi-gate the effect of accounting for long memory in the volatility process as well asits asymmetric reaction to past values of the series to predict: one day, one and twoweeks volatility levels.

M3 - Book chapter

SN - 978-3-319-89823-0

SP - 203

EP - 207

BT - Mathematical and Statistical Methods for Actuarial Sciences and Finance

A2 - Corazza, Marco

A2 - Durbán, María

A2 - Grané, Aurea

A2 - Perna, Cira

A2 - Sibillo, Marilena

PB - Springer

T2 - MAF Conference

Y2 - 4 April 2018 through 6 April 2018

ER -