Is the Subject Area "Coherence" applicable to this article? Yes No
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This is a stable coin, so it is pretty unique among other cryptocurrencies. Stable coins have the backing of fiat currencies such as the Euro or US dollar. This means that if an investor buys 1 Tether coin, they will have the same value of 1 fiat currency (backing the coin). Theoretically, what this means is that Tether will have a more stable value than other cryptocurrencies that suffer market volatility.
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Through instructions in their source codes, cryptocurrencies automatically adjust to the amount of mining power working to create new blockchain copies — copies become more difficult to create as mining power increases and easier to create as mining power decreases.
"It is one of many schemes by which naïve retail investors are drawn in and exploited by malevolent crypto promoters," Cornell University economist Eswar Prasad told the BBC.
Daily NFT sales volume on open-sea has started consolidating, and there is a good chance that the market had probably found a bottom, said Hitesh Malviya, Founder, itsblockchain.com.
An early-morning error led to the value of Bitcoin dropping nearly 87 percent on Binance's crypto exchange.
Crypto market capital shoots to $2.34 trillion with Ethereum occupying 20% of crypto economy
So what happened? And is there any hope for a recovery? To answer both, you have to look at quite a few factors.
Wavelet coherence plots as above highlight areas in the time-frequency space where the two series co-move. The warmer the colour, the higher the coherence (which can be interpreted as correlation) at that location in the time-frequency space; the colours used in this work range from dark blue (0, no coherence) to yellow (1, strong coherence). Statistically significant areas of coherence are surrounded by a thick black line.
Margin is a key part of leveraged trading. It is the term used to describe the initial deposit you put up to open and maintain a leveraged position. When you are trading cryptocurrencies on margin, remember that your margin requirement will change depending on your broker, and how large your trade size is.
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Although this test successfully detects single isolated bubbles, Phillips, Shi, and Yu [24] acknowledge it may suffer from reduced discriminatory power when applied to time series with multiple occurrences of bubbles. To overcome this weakness, a further enhancement was proposed, as a new method, called a generalized supremum ADF (GSADF) test. This test allows both the start and end points of data subsets to vary, which in turn enables the identification of multiple bubble regimes in one observed time series. The GSADF test is defined by
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