Name
Abstract | ||
|---|---|---|
Statistical physics of complex systems exploits network theory not only to model, but also to effectively extract information from many dynamical real-world systems. A pivotal case of study is given by financial systems: market prediction represents an unsolved scientific challenge yet with crucial implications for society, as financial crises have devastating effects on real economies. Thus, nowadays the quest for a robust estimator of market efficiency is both a scientific and institutional priority. In this work we study the visibility graphs built from the time series of several trade market indices. We propose a validation procedure for each link of these graphs against a null hypothesis derived from ARCH-type modeling of such series. Building on this framework, we devise a market indicator that turns out to be highly correlated and even predictive of financial instability periods. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Risk Management | Complex system,Graph,Economics,Visibility,Visibility graph,Null hypothesis,Exploit,Robust statistics,Network theory,Finance |
DocType | Volume | Citations |
Journal | abs/1710.10980 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matteo Serafino | 1 | 0 | 1.01 |
| Andrea Gabrielli | 2 | 14 | 3.81 |
| Guido Caldarelli | 3 | 382 | 40.76 |
| Giulio Cimini | 4 | 126 | 13.77 |