Long-Range Dependence in Financial Markets: a Moving Average Cluster Entropy Approach

A perspective is taken on the intangible complexity of economic and social systems by investigating the underlying dynamical processes that produce, store and transmit information in financial time series in terms of the \textit{moving average cluster entropy}. An extensive analysis has evidenced market and horizon dependence of the \textit{moving average cluster entropy} in real world financial assets. The origin of the behavior is scrutinized by applying the \textit{moving average cluster entropy} approach to long-range correlated stochastic processes as the Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Fractional Brownian motion (FBM). To that end, an extensive set of series is generated with a broad range of values of the Hurst exponent $H$ and of the autoregressive, differencing and moving average parameters $p,d,q$. A systematic relation between \textit{moving average cluster entropy}, \textit{Market Dynamic Index} and long-range correlation parameters $H$, $d$ is observed. This study shows that the characteristic behaviour exhibited by the horizon dependence of the cluster entropy is related to long-range positive correlation in financial markets. Specifically, long range positively correlated ARFIMA processes with differencing parameter $d\simeq 0.05$, $d\simeq 0.15$ and $d\simeq 0.25$ are consistent with \textit{moving average cluster entropy} results obtained in time series of DJIA, S\&P500 and NASDAQ

Diversity of Cardiologic Issues in a Contemporary Cohort of Women With Breast Cancer

Background: Women with breast cancer (BC) represent a special population particularly exposed to cardiovascular disease (CVD) risk. However, cardiologic assessment in BC is mostly limited to detection of left ventricular dysfunction cardiotoxicity (LVD-CTX) due to anticancer treatments. Our aim was to comprehensively investigate CV profile and events in a contemporary BC cohort. Methods and Results: Records of BC patients referred for a Cardio-Oncologic evaluation before starting anticancer treatments, between 2016 and 2019, were retrospectively reviewed (n = 508). Information regarding prevalence and control of CV risk factors, and novel CVD diagnoses were extracted. Occurrence of LVD-CTX, CV events other than LVD-CTX and mortality was assessed. Mean age of study population was 64 ± 13 years; 287 patients were scheduled to receive anthracycline and 165 anti-HER2 therapy. Overall, 53% of BC women had ≥2 CV risk factors, and 67% had at least one of arterial hypertension, dyslipidaemia or diabetes mellitus not adequately controlled. Eighteen (4%) patients were diagnosed a previously unknown CVD. Over a mean follow-up of 2.5 ± 1 years, 3% of BC patients developed LVD-CTX, 2% suffered from other CV events and 11% died. CV risk factors were not associated with LVD-CTX, except for family history of CAD. On the contrary, patients with other CV events exhibited a worse CV profile. Those who died more commonly experienced CV events other than LVD-CTX (p = 0.02). Conclusions: BC women show a suboptimal CV risk profile and are at risk of CV events not limited to LVD-CTX. A baseline Cardio-Oncologic evaluation was instrumental to implement CV prevention and to optimize CV therapies

Long-range dependence in financial markets: a moving average cluster entropy approach

A perspective is taken on the intangible complexity of economic and social systems by investigating the dynamical processes producing, storing and transmitting information in financial time series. An extensive analysis based on the moving average cluster entropy approach has evidenced market and horizon dependence in highest-frequency data of real world financial assets. The behavior is scrutinized by applying the moving average cluster entropy approach to long-range correlated stochastic processes as the Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Fractional Brownian motion (FBM). An extensive set of series is generated with a broad range of values of the Hurst exponent H and of the autoregressive, differencing and moving average parameters p,d,q . A systematic relation between moving average cluster entropy and long-range correlation parameters H, d is observed. This study shows that the characteristic behaviour exhibited by the horizon dependence of the cluster entropy is related to long-range positive correlation in financial markets. Specifically, long range positively correlated ARFIMA processes with differencing parameter d≃0.05 , d≃0.15 and d≃0.25 are consistent with moving average cluster entropy results obtained in time series of DJIA, S&P500 and NASDAQ. The findings clearly point to a variability of price returns, consistently with a price dynamics involving multiple temporal scales and, thus, short- and long-run volatility components. An important aspect of the proposed approach is the ability to capture detailed horizon dependence over relatively short horizons (one to twelve months) and thus its relevance to define risk analysis indices

Association of pemphigus and bullous pemphigoid with thyroid autoimmunity in Caucasian patients

6nononenoneAmeri, Pietro; Cinotti, Elisa; Mussap, Michele; Murialdo, Giovanni; Parodi, Aurora; Cozzani, EmanueleAmeri, Pietro; Cinotti, Elisa; Mussap, Michele; Murialdo, Giovanni; Parodi, Aurora; Cozzani, Emanuel