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Found 6 papers, 0 papers with code
Multiscaling and rough volatility: an empirical investigation
We find that the model is able to reproduce multiscaling features of the prices' time series when a low value of the Hurst exponent is used but it fails to reproduce what the real data say.
The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool
These GHE patterns, distinguish in a statistically robust way, not only between time periods of uniscaling and multiscaling, but also among different types of multiscaling: symmetric multiscaling (M) and asymmetric multiscaling (A).
A new multilayer network construction via Tensor learning
Multilayer networks proved to be suitable in extracting and providing dependency information of different complex systems.
Predicting Multidimensional Data via Tensor Learning
In particular, in this paper, we propose a parsimonious tensor regression model that retains the intrinsic multidimensional structure of the dataset.
On the statistics of scaling exponents and the Multiscaling Value at Risk
We show that by using this statistical procedure in combination with the robustly estimated multiscaling exponents, the one year forecasted MSVaR mimics the VaR on the annual data for the majority of the stocks analyzed.
Unveil stock correlation via a new tensor-based decomposition method
To retrieve the factor components, we propose a new tensor decomposition (which we name Slice-Diagonal Tensor (SDT) factorization) and compare it to the two most used tensor decompositions, the Tucker and the PARAFAC.
