Abstract
| Abstract: A pair of bouncing geometric Brownian motions (GBMs) is studied. The bouncing GBMs behave like GBMs except that, when they meet, they bounce off away from each other. The object of interest is the position process, which is defined as the position of the latest meeting point at each time. We study the distributions of the time and position of their meeting points, and show that the suitably scaled logarithmic position process converges weakly to a standard Brownian motion as the bounce size delta goes to 0. We also establish the convergence of the bouncing GBMs to mutually reflected GBMs as delta goes to 0. Finally, applying our model to limit order books, we derive a simple and effective prediction formula for trading prices. |
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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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