  Big data and the use of advanced technologies are relevant topics in the
financial market. In this context, complex networks became extremely useful in
describing the structure of complex financial systems. In particular, the time
evolution property of the stock markets have been described by temporal
networks. However, these approaches fail to consider the interactions over time
between assets. To overcome this drawback, financial markets can be described
by multiplex networks where the different relations between nodes can be
conveniently expressed structuring the network through different layers. To
catch this kind of interconnections we provide new local clustering
coefficients for multiplex networks, looking at the network from different
perspectives depending on the node position, as well as a global clustering
coefficient for the whole network. We also prove that all the well-known
expressions for clustering coefficients existing in the literature, suitably
extended to the multiplex framework, may be unified into our proposal. By means
of an application to the multiplex temporal financial network, based on the
returns of the S\&P100 assets, we show that the proposed measures prove to be
effective in describing dependencies between assets over time.
