We address the problem of compressing correlated distributed video signals that are captured from a dynamic scene. The correlated video signals originate from cameras that are not co-located or that cannot cooperate to directly exploit their correlation. However, the decoder is able to exploit the coded information from all cameras to achieve the best reconstruction of the correlated video sign...

The intertemporal capital asset pricing model of Merton (1973) is examined using the dynamic conditional correlation (DCC) model of Engle (2002). The mean-reverting DCC model is used to estimate a stock’s (portfolio’s) conditional covariance with the market and test whether the conditional covariance predicts time-variation in the stock’s (portfolio’s) expected return. The risk-aversion coeffic...

We propose a new model for dynamic volatilities and correlations of skewed and heavytailed data. Our model endows the Generalized Hyperbolic distribution with time-varying parameters driven by the score of the observation density function. The key novelty in our approach is the fact that the skewed and fat-tailed shape of the distribution directly affects the dynamic behavior of the time-varyin...

This paper assesses the relative economic value of volatility and correlation timing in the context of asset allocation strategies. Using exchange rate data, we model the dynamic covariance matrix of daily returns by implementing a set of multivariate models based on Dynamic Conditional Correlation (DCC) model of Engle (2002). Our analysis takes a Bayesian approach in both estimation and asset ...

Based on a vector autoregressive model and dynamic conditional correlation generalized heteroskedasticity model, this study explores the relation between international crude oil market Chinese energy stock market. The findings suggest positive one-way spillover effect of returns China’s returns. Furthermore, two markets is time varying.

Recently a new dependence measure, the distance correlation, has been proposed to measure the dependence between continuous random variables. A nice property of this measure is that it can be consistently estimated with the empirical average of the products of certain distances between the sample points. Here we generalize this quantity to measure the conditional dependence between random varia...

The construction of score-driven filters for nonlinear time series models is described, and they are shown to apply over a wide range disciplines. Their theoretical practical advantages other methods highlighted. Topics covered include robust modeling, conditional heteroscedasticity, count data, dynamic correlation association, censoring, circular switching regimes.

In this paper, we describe the Beatsens Team solution of Emotion in Music task in MediaEval benchmarking campaign 2014. We extracted and designed several sets of features and used continuous conditional random field(CCRF) for dynamic emotion characterization task. The best runs for Pearson correlation are 0.23± 0.56 and 0.12± 0.55 of valence and arousal respectively, for RMSE are 0.12± 0.06 and...