Dr. Mordecai chaired the first session of the conference with the following presentations:
- Elisa Alòs, Universitat Pompeu Fabra, Barcelona: The fractional Brownian motion in volatility modeling
- Rama Cont, University of Oxford: Rough volatility: Fact or artefact?
- Mikko Pakkanen, Imperial College London: A GMM approach to estimate the roughness of stochastic volatility
About Cournot Centre
The Cournot Centre was co-founded by Nobel Laureate Robert Solow, and is an independent research institute based in France. It is supported by the Cournot Foundation, which operates under the Fondation de France. The Centre adopted the name of pioneering economist, mathematician and philosopher Antoine Augustin Cournot (1801–77).
About Numerati® Partners LLC
Numerati® Partners LLC coordinates a data analytics and technology development ecosystem, with the mission of advancing and fostering the next generation of scalable data-intensive risk and liability management enterprises. The firm provides resources fundamental to advancing the development of nascent leading-edge inferential surveillance, monitoring, and predictive analytics technologies for deployment within the RiskTech domain: risk technologies associated with adaptive distributed, networked and embedded systems such as remote sensing, agent-oriented data analytics, computing and control systems. Numerati® Partners curates integrated RiskTech solutions as well as forensic and use-case applications in RiskTech sub-domains such as LitTech, RegTech, FinTech and InsurTech (litigation technology, regulation technology, financial technology and insurance technology).About RiskEcon® Lab @ Courant Institute
The mission of RiskEcon® Lab for Decision Metrics @ Courant Institute of Mathematical Sciences NYU is the development of experimental testbeds and analytics that employ high-dimensional datasets from innovative sources by applying a range of computational and analytical methods to commercial and industrial sensor networks and edge-computing for distributed, embedded and autonomous systems, focusing primarily on research and development (R&D) of remote and compressed sensing, anomaly detection, forensic analytics and statistical process control. By employing applied computational statistics within the context of robust and scalable data analytic solutions, our goal is robust integration of machine learning with signal processing for measurement and control, in order to conduct research fundamental to large-scale, real-world questions in risk and liability management. RiskEcon® Lab enables, facilitates and coordinates academic research focusing on these patterns and trends, through the development of commercially-viable, analytic applications employing computational statistical tools in conjunction with innovative and non-traditional data structures. In addition, the Lab’s activities involve the advancement of applied mathematical statistics and computational economics, through interdisciplinary post-doctoral, postgraduate, graduate research and education in data science and social computing in the public interest.