Higher-Frequency Analysis of Low-Frequency Data

Mallinger-Dogan, Mickaël; Szigety, Mark

doi:10.3905/jpm.2014.41.1.121

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Consequently, using a low-frequency dataset such as 12 h or 24 h appears to cause a loss of important information that influences the results of analysis. This problem has also been described in earlier studies such as [ 12 ]. However, existing studies mainly focused on daily data to detect communities in the cryptocurrency market.…”

Section: Experimental Results and Discussionsupporting

confidence: 64%

“…Generally, there are two common shortcomings with correlation-related studies. Firstly, one mainly uses a low-frequency dataset such as daily or monthly, and this might cause a loss of important information from each time series, hence failing to reflect their true nature [ 12 ]. This appears to be a major concern in the cryptocurrency market, since it is well-known for its high fluctuations in terms of price movement.…”

Section: Introductionmentioning

confidence: 99%

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The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Nguyen

Tan

Bezbradica

et al. 2022

Entropy

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We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.

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Section: Experimental Results and Discussionsupporting

confidence: 64%

Section: Introductionmentioning

confidence: 99%