activity
20182022
most citedMultiscaling and rough volatility: an empirical investigation

2 citations · 2 across the 1 of their papers we have counts for

collaborators

7 papers

q-fin.ST20222 cited

Multiscaling and rough volatility: an empirical investigation

Giuseppe Brandi, T. Di Matteo

Pricing derivatives goes back to the acclaimed Black and Scholes model. However, such a modeling approach is known not to be able to reproduce some of the financial stylized facts,…

q-fin.ST2020

The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool

Ioannis P. Antoniades, Giuseppe Brandi, L. G. Magafas +1

The dynamical evolution of multiscaling in financial time series is investigated using time-dependent Generalized Hurst Exponents (GHE), , for various values of the parameter…

q-fin.RM2020

A new multilayer network construction via Tensor learning

Giuseppe Brandi, T. Di Matteo

Multilayer networks proved to be suitable in extracting and providing dependency information of different complex systems. The construction of these networks is difficult and is mo…

stat.ML2020

Predicting Multidimensional Data via Tensor Learning

Giuseppe Brandi, T. Di Matteo

The analysis of multidimensional data is becoming a more and more relevant topic in statistical and machine learning research. Given their complexity, such data objects are usually…

q-fin.RM2020

On the statistics of scaling exponents and the Multiscaling Value at Risk

Giuseppe Brandi, T. Di Matteo

Scaling and multiscaling financial time series have been widely studied in the literature. The research on this topic is vast and still flourishing. One way to analyze the scaling…

q-fin.RM2019

Unveil stock correlation via a new tensor-based decomposition method

Giuseppe Brandi, Ruggero Gramatica, Tiziana Di Matteo

Portfolio allocation and risk management make use of correlation matrices and heavily rely on the choice of a proper correlation matrix to be used. In this regard, one important qu…