A Survey of Long-Term Memory in the Digital Currency Index

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Alizadeh Shima; SAFARZADEH HOSSEIN

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT) Year:2019 | Volume:10 | Issue:40 Page(s): 169-183

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

988

Download:

Cites:

Information Journal Paper

Title

A Survey of Long-Term Memory in the Digital Currency Index

Author(s)

Alizadeh Shima | SAFARZADEH HOSSEIN | Issue Writer Certificate

Keywords

Crypto currencyQ1

Time seriesQ2

Long-term memoryQ2

Autoregressive Fractionally Integrated Moving AverageQ1

Abstract

Long-term memory, also referred to as long-range dependence, explains the correlation structure of values of a Time series at long intervals. According to the efficient market hypothesis, prices follow a randomized step process, so returns on assets can not be predicted based on past price changes. Long-term memory is a weak point of the business-market hypothesis. Long-term processes have important implications for asset yields and play a crucial role in Time series analysis. This study examines the existence of Long-term memory in the price index of crypto currencies equals $ 1 and lower for the period from September 1, 2015 to September 1, 2018. To estimate the parameter d, the OLS method is used in the EVIEWS software package. The ARFIMA model is used to test hypotheses. The results indicate that Long-term memory is in the currencies of DIGIBYTE, Dodge Coin, EMER Coin, BITSHARES, MAIDSAFE COIN, XEM, Redd Coin, NXT, Verge and Ripple, and on the other hand, three currencies of Byte Coin, SIA Coin, STELLAR lacks Long-term memory, and therefore these currencies are among the most efficient market products.

Cites

No record.

References

No record.

Cite

APA: Copy

Alizadeh, Shima, & SAFARZADEH, HOSSEIN. (2019). A Survey of Long-Term Memory in the Digital Currency Index. FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), 10(40 ), 169-183. SID. https://sid.ir/paper/197623/en

Vancouver: Copy

Alizadeh Shima, SAFARZADEH HOSSEIN. A Survey of Long-Term Memory in the Digital Currency Index. FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)[Internet]. 2019;10(40 ):169-183. Available from: https://sid.ir/paper/197623/en

IEEE: Copy

Shima Alizadeh, and HOSSEIN SAFARZADEH, “A Survey of Long-Term Memory in the Digital Currency Index,” FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), vol. 10, no. 40 , pp. 169–183, 2019, [Online]. Available: https://sid.ir/paper/197623/en

Related Journal Papers