DC8: Detecting anomalies and dependence structures in high dimensional, high frequency financial data

Objectives

Herding, a well-known financial anomaly, is thought to cause high volatility, volatile prices, and low liquidity. 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. 1) Publications in prestigious journals available via public repositories, 2) Presentations at prestigious conferences, and 3) Knowledge exchange.

Secondments (2)

Recruitment & Hosting Details

DC 8 ASE Bucharest University of Economic Studies M9 36

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DC 8 ASE ASE Month 9 36 months D 4.2

Detecting anomalies and dependence structures in high dimensional, high frequency financial data (WP 1)

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. 1) Publications in

prestigious journals available via public repositories, 2) Presentations at prestigious conferences, and 3) Knowledge exchange

Planned secondments: DeutscheBank, Roman Timofeev, M27, 6 months, contribute datasets, expertise on applications of AI and anomaly

detection and early warning systems, as well as expertise on predictive analytics, semantic analysis and risk management.

Royalton, Dr. Michael Althof, M33, 12 months, for training in portfolio optimization of ETFs ,

Fellow Host institution PhD enrolment Start date Duration Deliverables

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Deliverables