Wavelets are wavelike functions used to transform signals into a representation which has time and frequency domain components. Visually wavelets appear as wave-like oscillations with an amplitude that starts at zero, increases, then returns to zero. Another way to consider a wavelet is as a bandpass filter, which can be applied to a time series under investigation, letting through only components of the time series within a finite range of frequencies to different extents depending on the energy spectrum of the wavelet. Wavelets take the form:
Identifying the determinants of asset prices is one of the most important questions in finance. Traditional asset pricing theories are founded on the idea that equity prices should be determined by fundamentals such as earnings (Gordon 1959, Campbell and Shiller 1988). In contrast, the behavioral finance literature posits that prices do not always follow fundamentals due to investor sentiment trading (Shiller 1981, Baker and Wurgler 2006, Stambaugh et al. 2012). Eventually however, equity prices seem to trace their fundamentals (Pa´stor and Veronesi, 2003, 2006, Bartram and Grinblatt 2018).

Since the first digital currencies were devised after the financial crisis, $2.3tn has been invested in them. But how do they, and other assets transacted on blockchain technology, actually work? And what risks do they pose? Read our explainers
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