  This paper compares and contrasts stationarity between the conventional stock
market and cryptocurrency. The dataset used for the analysis is the intraday
price indices of the S&P500 from 1996 to 2023 and the intraday Bitcoin indices
from 2019 to 2023, both in USD. We adopt the definition of `wide sense
stationary', which constrains the time independence of the first and second
moments of a time series. The testing method used in this paper follows the
Wiener-Khinchin Theorem, i.e., that for a wide sense stationary process, the
power spectral density and the autocorrelation are a Fourier transform pair. We
demonstrate that localized stationarity can be achieved by truncating the time
series into segments, and for each segment, detrending and normalizing the
price return are required. These results show that the S&P500 price return can
achieve stationarity for the full 28-year period with a detrending window of 12
months and a constrained normalization window of 10 minutes. With truncated
segments, a larger normalization window can be used to establish stationarity,
indicating that within the segment the data is more homogeneous. For Bitcoin
price return, the segment with higher volatility presents stationarity with a
normalization window of 60 minutes, whereas stationarity cannot be established
in other segments.
