Anatoliy Swishchuk

Stochastic Modelling of Big Data in Finance (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This book 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).

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 304
• Erscheinungstermin: 8. November 2022
• Englisch
• ISBN-13: 9781000776812
• Artikelnr.: 65687434

Autorenporträt

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.

Inhaltsangabe

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