We develop new classes of semiparametric multivariate time series models based on Multi-
Output Gaussian Processes and warped Multi-Output Gaussian Processes. These describe …

Discovering causal relationships in multivariate time series data has important applications
in finance. This chapter discusses temporal causal modeling (TCM), an approach that …

The ability to test for statistical causality in linear and nonlinear contexts, in stationary or non-
stationary settings, and to identify whether statistical causality influences trend of volatility …

The standard linear Granger causality test, based on the vector autoregressive model (VAR),
requires stationarity of the time series. A VAR model is fitted to the first-differences of the time …

The study of causal relations has been proved to be significant in acquisition,
understanding, and representation of human knowledge across physical and biological …

The ability to test for statistical causality in linear and non-linear contexts, in stationary or non-
stationary settings and to identify whether statistical causality influences trend of volatility …

Purpose–In all existing theoretical papers on causality it is assumed that the lag length is
known a priori. However, in applied research the lag length has to be selected before testing …

Writing this thesis provided me the chance to meet many exceptional people, travel the
world, and, most importantly, learn lots of things. In this regard, there are many people whom …

Financial data is generally time series in essence and thus suffers from three fundamental
issues: the mismatch in time resolution, the time-varying property of the distribution …

The study of the causal relationships in a stochastic process (Y_t,Z_t)_t∈Z is a subject of a
particular interest in finance and economy. A widely-used approach is to consider the notion …