Detecting anomalies and dependence structures in high dimensional, high frequency financial data
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Host institution: Bucharest University of Economic Studies, Romania (ASE)
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Starting month: M9
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Duration: 36 months
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Pillar 1: Foundation of data science (BabeÈ™-Bolyai University, 4 ECTs), Work Package 1
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Work Packages: WP1, WP6, WP7, WP8
Objectives
Herding, a well-known financial anomaly, is thought to cause high volatility, volatile prices, and low liquidity (Bikhchandani and Sharma, 2000). Greed and herd behaviour caused the seventeenth-century tulip mania, the 1995–2000 Internet bubble, and the 2015 Chinese stock market crash. This project studies high-dimensional sentiment networks and herd behaviour on the stock market. To better fit investor sentiment, the project will calibrate the option pricing model, Stochastic Volatility and Correlated Jump (SVCJ).
Expected Results
The project will detect anomalies like herd behaviour and dependence structures in high-dimensional, high-frequency financial data. We plan to create a tail event-driven network that graphs or matrices the interconnections of a large panel to understand sentiment network mechanics. That will inform our herd behaviour detection and option pricing model calibration. Results will be disseminated through publications in prestigious journals available via public repositories, presentations at prestigious conferences, and knowledge exchange.
Planned Secondments
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Intesa Sanpaolo (INT), Prof. Dr. Marco Bianchetti, M18, 18 months, Analyse quantitative modelling & technology and risk
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Athena Research and Innovation Centre (ARC) Greece, Prof. Dr. Ioannis Emiris, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
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