Abstract
| Abstract: This paper proposes a two-level hierarchical Gaussian process that is dynamically modeled as a multivariate functional autoregressive model (MFAR). Through MFAR, we capture the cross-correlation amongst the functional time series. We investigate the Granger causalily between functional time series through the application of Bayesian analysis. We demonstrate the methodology through simulations and applications to yield curve data. |
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The aim of this special issue is to feature research papers on theory, methodology, and applications of models and methods for recent advances in statistical finance. We encourage submissions presenting original works on statistical, computational, and mathematical approaches to modelling and analysis of financial data. Innovative applications and case studies in financial statistics are welcome, especially related to novel methodological challenges in the treatment of big data and high-frequency data.
This special issue will bring together contributions from practitioners and researchers working on different aspects of statistical methods in finance, with methodological interests encompassing, but not limited to, the following domains:
The motivating application areas could be: For More Detail ...If you are a student and want your paper to be considered for student paper competition, then ask your supervisor to send a mail at statfin@cmi.ac.in, with a particular mention that you were the primary contributor and author of the paper by May 15, 2021.
You must submit your paper by May 15, 2021, to be considered for the competition. Mail your paper at statfin@cmi.ac.in
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