Search results for " VAR model"
showing 4 items of 4 documents
Multivariate Frequency Domain Analysis of Causal Interactions in Physiological Time Series
2011
A common way of obtaining information about a physiological system is to measure one or more signals from the system, consider their temporal evolution in the form of numerical time series, and obtain quantitative indexes through the application of time series analysis techniques. While historical approaches to time series analysis were addressed to the study of single signals, recent advances have made it possible to study collectively the behavior of several signals measured simultaneously from the considered system. In fact, multivariate (MV) time series analysis is nowadays extensively used to characterize interdependencies among multiple signals collected from dynamical physiological s…
Is There a Connection between Sovereign CDS Spreads and the Stock Market? Evidence for European and US Returns and Volatilities
2020
This study complements the current literature, providing a thorough investigation of the lead&ndash
Pre- and post-ictal brain activity characterization using combined source decomposition and connectivity estimation in epileptic children
2019
In this research, the study of functional connectivity between sources of electroencephalogram (EEG) activity assessed for different classes (well before seizure, preictal and post-ictal) was performed. EEG recordings were acquired from 12 subjects with focal epilepsy. Then, ten common spatial patterns (CSP) were obtained for EEG segments describing 95% of Riemannian distance between pairs of classes, followed by estimation of multivariate autoregressive (MVAR) models’ coefficients. The MVAR models were further used to extract coherence as a functional connectivity measures. Our results show that the coherence between CSP sources differs between baseline and pre-ictal segments: it has the l…
Is Big Brother Watching Us? Google, Investor Sentiment and the Stock Market
2013
International audience; This paper proposes a novel measure of French investor sentiment based on the volume of internet search reported by Google Trends. We find that our sentiment indicator correlates well with alternative sentiment measures often used in the literature. Furthermore, we find that investor sentiment influences the behavior of mutual fund investors. The results also reveal evidence about short-run predictability in return. An increase in our sentiment index leads to short-term return reversal. The reversal pattern is more pronounced for smaller firms than larger firms, consistent with the predictions of noise trader's models.