Synchronization Analysis of Language and Physiology in Human Dyads.

We studied the synchronization dynamics of a therapist and patient during a psychotherapy session. This investigation was developed in order to explore a new possible perspective and methodology for studying the expression of emotions. More specifically, literature concerning synchronization of in-session non-verbal variables emphasises its positive correlation with empathy and therapeutic outcomes. We compared the dynamics of galvanic skin response (GSR) and linguistic prosody, chosen as indicators of emotional expression in different domains. We studied their synchronization through complementary methodologies: Recurrence Quantification Analysis (RQA) and Principal Component Analysis (PCA), Markov Transition Matrix (MTM) and Cross-Recurrence Quantification Analysis (CRQA). We investigated the nonlinearity of GSR in terms of self-similarity and power-law, as emerged in autocorrelation functions and signal variations. We considered time-lagged correlations as a measure of dynamical systems' memory. This article concludes by highlighting the importance of a deeper study of all variables related to the psychotherapeutic process and their synchronization in order to extend our knowledge of general human dynamics.

Interplay between past market correlation structure changes and future volatility outbursts.

We report significant relations between past changes in the market correlation structure and future changes in the market volatility. This relation is made evident by using a measure of "correlation structure persistence" on correlation-based information filtering networks that quantifies the rate of change of the market dependence structure. We also measured changes in the correlation structure by means of a "metacorrelation" that measures a lagged correlation between correlation matrices computed over different time windows. Both methods show a deep interplay between past changes in correlation structure and future changes in volatility and we demonstrate they can anticipate market risk variations and this can be used to better forecast portfolio risk. Notably, these methods overcome the curse of dimensionality that limits the applicability of traditional econometric tools to portfolios made of a large number of assets. We report on forecasting performances and statistical significance of both methods for two different equity datasets. We also identify an optimal region of parameters in terms of True Positive and False Positive trade-off, through a ROC curve analysis. We find that this forecasting method is robust and it outperforms logistic regression predictors based on past volatility only. Moreover the temporal analysis indicates that methods based on correlation structural persistence are able to adapt to abrupt changes in the market, such as financial crises, more rapidly than methods based on past volatility.

Relation between financial market structure and the real economy: comparison between clustering methods.

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].