"data volatility" is an acceptable term in written English.
It is typically used to refer to situations in which the amount of data or its content changes frequently or unpredictably. For example: "The company's data volatility meant that the IT team had to constantly update their security protocols."
Based on storing data volatility, memories are basically classified into two categories, (i) volatile memory and (ii) nonvolatile memory.
Bloomberg's Scott Lanman: "The rising number of Americans filing for unemployment benefits is starting to worry some economists, though the latest increases came during holiday periods typically associated with data volatility.
Both types of memories can be further classified based on the memory technology that they use and based on data volatility as shown in the classification flow chart depicted in Figure 2.
"Data and volatility modelling" section examines asymmetric response of volatility to return in the Hungary stock market using asymmetric ARCH-types models.
Like the risk models that failed then, the standard deviation in a Sharpe ratio would use historic data on volatility and assume a normal distribution for market moves.
We look for similarities, point of abrupt changes, normalized data, return, volatility, graph, pure and noise part, correlation lengths, and signal-to-noise ratio.
However, another cross-cutting property of Big Data is volatility.
His earlier work was in statistical modeling of high frequency financial data, especially volatility clustering in stocks, bonds, and currencies.
Some banks also calibrated their models using 25 years worth of low volatility data.
But the data on daily volatility doesn't really convey just how rollercoaster-y this market has become, because in the last week alone we've regularly seen moves of five to seven per cent.
Discrepancies reveal characteristics of data that stochastic volatility models cannot approximate.
