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10.1371/journal.pone.0195200 http://scihub22266oqcxt.onion/10.1371/journal.pone.0195200 C5905883!5905883 !29668765     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\29668765 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       PLoS+One 2018 ; 13 (4 ): e0195200 Nephropedia Template TP {{tp|p=29668765 |t=2018. Cryptocurrency price drivers: Wavelet coherence analysis revisited |pdf=|usr=}}{{29668765 }} gab.com Text Cryptocurrency price drivers: Wavelet coherence analysis revisited JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29668765 #FOAvip Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies. Twit Text FOAVip Cryptocurrency price drivers: Wavelet coherence analysis revisited ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29668765 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29668765 #FOAvip English Wikipedia <ref>{{Cite pmid|29668765 }}</ref> Cryptocurrency price drivers: Wavelet coherence analysis revisited #MMPMID29668765 Phillips RC ; Gorse D PLoS One 2018[]; 13 (4 ): e0195200 PMID29668765 show ga Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies. |*Models, Economic [MESH] |*Wavelet Analysis [MESH] |Algorithms [MESH]Cryptocurrency price drivers: Wavelet coherence analysis revisited (epmc).pdf DeepDyve Pubget Overpricing