Big Data & Statistical Finance Tools
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Big Data & Statistical Finance Tools
Major problems with modern credit scoring data: 1. Growing amount of data – big data 2. Growing number of descriptors 3. Quality and trust – noisy data 4. Imbalanced data phenomena 5. Need for high quality of prediction
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- 0 Rating
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Requirements
- https://quantinar.com/course/540/multivariate-statistical-analysis?q=mva
General Overview
Description
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Big data – 7V dimensions according to Atos company
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Volume: size of data; Velocity: speed, displacement of data; Variety: diversity of data
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- Viscosity: measures the resistance to flow in the volume of data.
- Virality: measures how fast data is shared between nodes in a network.
- Veracity: trust and quality of the data.
- Value: what is the added value ?
A complete keynote may be downloaded on the extra ressources
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Meet the instructors !
Wolfgang Karl HÄRDLE attained his Dr. rer. nat. in Mathematics at Universität Heidelberg in 1982 and in 1988 his habilitation at Universität Bonn. He is Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin and the director of the Sino German Graduate School (洪堡大学 + 厦门大学) IRTG1792 on “High dimensional non stationary time series analysis”. He directs IDA Institute for Digital Assets,
University of Economic Studies, Bucharest, RO. His research focuses on data analytics, dimension reduction and quantitative finance. He has published over 30 books and more than 300 papers in top statistical, econometrics and finance journals. He is highly ranked and cited on Google Scholar, REPEC and SSRN. He has professional experience in financial engineering, S.M.A.R.T. (Specific, Measurable, Achievable, Relevant, Timely) data analytics, machine learning and cryptocurrency markets. He has created the www.quantlet.com platform, a cryptocurrency index, CRIX www.royalton-crix.com He is 玉山学者 （Yushan Scholar), web page hu.berlin/wkh