Abstract
| Abstract: Financial data, such as returns on investments, typically exhibit some non-standard features: long memory or long range dependence (LRD) and heavy tails. Therefore, any mathematical model approximating the evolution of asset price should be able to generate these properties. This can be done through the use of a long memory stochastic volatility (LMSV) model. In this article, we are interested in the estimation of Value-at-Risk (VaR) and Expected Shortfall (ES) for such models. We show that long memory may or may not have a detrimental effect on rates of convergence of the estimators of VaR and ES. |
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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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