Stochastic Modelling of Big Data in Finance
1st Edition
Published on 8. November 2022
Book
Hardback
280 pages
978-1-032-20926-5 (ISBN)
Description
Stochastic Modelling of Big Data in Finance provides a rigorous overview and exploration of stochastic modelling of big data in finance (BDF). The book describes various stochastic models, including multivariate models, to deal with big data in finance. This includes data in high-frequency and algorithmic trading, specifically in limit order books (LOB), and shows how those models can be applied to different datasets to describe the dynamics of LOB, and to figure out which model is the best with respect to a specific data set. The results of the book may be used to also solve acquisition, liquidation and market making problems, and other optimization problems in finance.
Features
Self-contained book suitable for graduate students and post-doctoral fellows in financial mathematics and data science, as well as for practitioners working in the financial industry who deal with big data
All results are presented visually to aid in understanding of concepts
Dr. Anatoliy Swishchuk is a Professor in Mathematical Finance at the Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada. He got his B.Sc. and M.Sc. degrees from Kyiv State University, Kyiv, Ukraine. He earned two doctorate degrees in Mathematics and Physics (PhD and DSc) from the prestigious National Academy of Sciences of Ukraine (NASU), Kiev, Ukraine, and is a recipient of NASU award for young scientist with a gold medal for series of research publications in random evolutions and their applications.
Dr. Swishchuk is a chair and organizer of finance and energy finance seminar 'Lunch at the Lab' at the Department of Mathematics and Statistics. Dr. Swishchuk is a Director of Mathematical and Computational Finance Laboratory at the University of Calgary. He was a steering committee member of the Professional Risk Managers International Association (PRMIA), Canada (2006-2015), and is a steering committee member of Global Association of Risk Professionals (GARP), Canada (since 2015).
Dr. Swishchuk is a creator of mathematical finance program at the Department of Mathematics & Statistics. He is also a proponent for a new specialization "Financial and Energy Markets Data Modelling" in the Data Science and Analytics program. His research areas include financial mathematics, random evolutions and their applications, biomathematics, stochastic calculus, and he serves on editorial boards for four research journals. He is the author of more than 200 publications, including 15 books and more than 150 articles in peer-reviewed journals. In 2018 he received a Peak Scholar award.
Features
Self-contained book suitable for graduate students and post-doctoral fellows in financial mathematics and data science, as well as for practitioners working in the financial industry who deal with big data
All results are presented visually to aid in understanding of concepts
Dr. Anatoliy Swishchuk is a Professor in Mathematical Finance at the Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada. He got his B.Sc. and M.Sc. degrees from Kyiv State University, Kyiv, Ukraine. He earned two doctorate degrees in Mathematics and Physics (PhD and DSc) from the prestigious National Academy of Sciences of Ukraine (NASU), Kiev, Ukraine, and is a recipient of NASU award for young scientist with a gold medal for series of research publications in random evolutions and their applications.
Dr. Swishchuk is a chair and organizer of finance and energy finance seminar 'Lunch at the Lab' at the Department of Mathematics and Statistics. Dr. Swishchuk is a Director of Mathematical and Computational Finance Laboratory at the University of Calgary. He was a steering committee member of the Professional Risk Managers International Association (PRMIA), Canada (2006-2015), and is a steering committee member of Global Association of Risk Professionals (GARP), Canada (since 2015).
Dr. Swishchuk is a creator of mathematical finance program at the Department of Mathematics & Statistics. He is also a proponent for a new specialization "Financial and Energy Markets Data Modelling" in the Data Science and Analytics program. His research areas include financial mathematics, random evolutions and their applications, biomathematics, stochastic calculus, and he serves on editorial boards for four research journals. He is the author of more than 200 publications, including 15 books and more than 150 articles in peer-reviewed journals. In 2018 he received a Peak Scholar award.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Illustrations
106 s/w Abbildungen, 53 farbige Abbildungen, 106 s/w Zeichnungen, 53 farbige Zeichnungen, 56 s/w Tabellen
56 Tables, black and white; 53 Line drawings, color; 106 Line drawings, black and white; 53 Illustrations, color; 106 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 21 mm
Weight
625 gr
ISBN-13
978-1-032-20926-5 (9781032209265)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Anatoliy Swishchuk
Stochastic Modelling of Big Data in Finance
E-Book
11/2022
1st Edition
Chapman & Hall/CRC
€111.99
Available for download
Anatoliy Swishchuk
Stochastic Modelling of Big Data in Finance
E-Book
11/2022
1st Edition
Chapman & Hall/CRC
€111.99
Available for download
Person
Dr. Anatoliy Swishchuk is a Professor in Mathematical Finance at the Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada. He got his B.Sc. and M.Sc. degrees from Kyiv State University, Kyiv, Ukraine. He earned two doctorate degrees in Mathematics and Physics (PhD and DSc) from the prestigious National Academy of Sciences of Ukraine (NASU), Kiev, Ukraine, and is a recipient of NASU award for young scientist with a gold medal for series of research publications in random evolutions and their applications.
Dr. Swishchuk is a chair and organizer of finance and energy finance seminar 'Lunch at the Lab' at the Department of Mathematics and Statistics. Dr. Swishchuk is a Director of Mathematical and Computational Finance Laboratory at the University of Calgary. He was a steering committee member of the Professional Risk Managers International Association (PRMIA), Canada (2006-2015), and is a steering committee member of Global Association of Risk Professionals (GARP), Canada (since 2015).
Dr. Swishchuk is a creator of mathematical finance program at the Department of Mathematics & Statistics. He is also a proponent for a new specialization "Financial and Energy Markets Data Modelling" in the Data Science and Analytics program. His research areas include financial mathematics, random evolutions and their applications, biomathematics, stochastic calculus, and he serves on editorial boards for four research journals. He is the author of more than 200 publications, including 15 books and more than 150 articles in peer-reviewed journals. In 2018 he received a Peak Scholar award.
Dr. Swishchuk is a chair and organizer of finance and energy finance seminar 'Lunch at the Lab' at the Department of Mathematics and Statistics. Dr. Swishchuk is a Director of Mathematical and Computational Finance Laboratory at the University of Calgary. He was a steering committee member of the Professional Risk Managers International Association (PRMIA), Canada (2006-2015), and is a steering committee member of Global Association of Risk Professionals (GARP), Canada (since 2015).
Dr. Swishchuk is a creator of mathematical finance program at the Department of Mathematics & Statistics. He is also a proponent for a new specialization "Financial and Energy Markets Data Modelling" in the Data Science and Analytics program. His research areas include financial mathematics, random evolutions and their applications, biomathematics, stochastic calculus, and he serves on editorial boards for four research journals. He is the author of more than 200 publications, including 15 books and more than 150 articles in peer-reviewed journals. In 2018 he received a Peak Scholar award.
Content
1. A Brief Introduction: Stochastic Modelling of Big Data in Finance. 1.1. Introduction. 1.2. Big Data in Finance: Limit Order Books. 1.3. Stochastic Modelling of Big Data in Finance: Limit Order Books (LOB). 1.4 Illustration and Justification of Our Method to Study Big Data in Finance. 1.5. Methodological Aspects of Using the Models. 1.6. Conclusion. I. Semi-Markovian Modelling of Big Data in Finance. 2. A Semi-Markovian Modelling of Big Data in Finance. 2.1. Introduction. 2.2. A Semi-Markovian Modeling of Limit Order Markets. 2.3. Main Probabilistic Results. 2.4. Diffusion Limit of the Price Process. 2.5. Numerical Results. 2.6. More Big Data. 2.7. Conclusion. 3. General Semi-Markovian Modelling of Big Data in Finance. 3.1. Introduction. 3.2. Reviewing the Assumptions with Our New Data Sets. 3.3. General Semi-Markov Model for the Limit Order Book with Two States. 3.4. General Semi-Markov Model for the Limit Order Book with arbitrary number of states. 3.5. Discussion on Price Spreads. 3.6. Conclusion. II. Modelling of Big Data in Finance with Hawkes Processes. 4. A Brief Introduction to Hawkes Processes. 4.1. Introduction. 4.2. Definition of Hawkes Processes (HPs). 4.3. Compound Hawkes Processes. 4.4. Limit Theorems for Hawkes Processes: LLN and FCLT. 4.5. Limit Theorems for Poisson Processes: LLN and FCLT. 4.6. Stylized Properties of Hawkes Process. 4.7. Conclusion. 5. Stochastic Modelling of Big Data in Finance with CHP. 5.1. Introduction. 5.2. Definitions of HP, CHP and RSCHP. 5.3. Diffusion Limits and LLNs for CHP and RSCHP in Limit Order Books. 5.4. Numerical Examples and Parameters Estimations. 5.5. Conclusion. 6. Stochastic Modelling of Big Data in Finance with GCHP. 6.1. A Brief Introduction and Literature Review. 6.2. Diffusion Limits and LLNs. 6.3. Empirical Results. 6.4. Conclusion. 7. Quantitative and Comparative Analyses of Big Data with GCHP. 7.1. Introduction. 7.2. Theoretical Analysis. 7.3. Application. 7.4. Hawkes Process and Models Calibrations. 7.5. Error Measurement. 7.6. Conclusion. III. Multivariate Modelling of Big Data in Finance. 8. Multivariate General Compound Hawkes Processes in BDF. 8.1. Introduction. 8.2. Hawkes Processes and Limit Theorems. 8.3. Multivariate General Compound Hawkes Processes (MGCHP) and Limit Theorems. 8.4. FCLT II for MGCHP: Deterministic Centralization. 8.5. Numerical Example. 8.6. Conclusion. 9. Multivariate General Compound Point Processes in BDF. 9.1. Introduction. 9.2. Definition of Multivariate General Compound Point Process (MGCPP). 9.3. LLNs and Diffusion Limits for MGCPP. 9.4. Diffusion Limit for the MGCPP: Deterministic Centralization. 9.5. Conclusion. IV. Appendix: Basics in Stochastic Processes