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El. knyga: Quantitative Methods in Finance using R

  • Formatas: 208 pages
  • Išleidimo metai: 04-Jul-2022
  • Leidėjas: Open University Press
  • Kalba: eng
  • ISBN-13: 9780335251278
Kitos knygos pagal šią temą:
  • Formatas: 208 pages
  • Išleidimo metai: 04-Jul-2022
  • Leidėjas: Open University Press
  • Kalba: eng
  • ISBN-13: 9780335251278
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The book will form a solid foundation to support the transition of students into the world of work or further research.

Professor Jane M Binner, Chair of Finance, Department of Finance, University of Birmingham, UK

In over 20 years of teaching quantitative methods, I have rarely come across a book such as this which meets/exceeds all the expectations of its intended audience so well

Tuan Yu, Lecturer, Kent Business School, Canterbury, UK

This is a fantastic book for anyone wanting to understand, learn and apply quantitative methods in finance using R 

Professor Raphael Markellos, Professor of Finance, Norwich Business School, UK







Quantitative Methods in Finance Using R draws on the extensive teaching and research expertise of John Fry and Matt Burke, covering a wide range of quantitative methods in Finance that utilise the freely downloadable R software. With software playing an increasingly important role in finance, this book is a must-have introduction for finance students who want to explore how they can undertake their own quantitative analyses in dissertation and project work.

Assuming no prior knowledge, and taking a holistic approach, this brand new title guides you from first principles and help to build your confidence in tackling large data sets in R. 

Complete with examples and exercises with worked solutions, Fry and Burke demonstrate how to use the R freeware for regression and linear modelling, with attention given to presentation and the importance of good writing and presentation skills in project work and data analysis more generally.

Through this book, you will develop your understanding of:

Descriptive statistics

Inferential statistics

Regression

Analysis of variance

Probability regression models

Mixed models

Financial and non-financial time series







John Fry is a senior lecturer in Applied Mathematics at the University of Hull. Fry has a PhD in Mathematical Finance from the University of Sheffield. His main research interests span mathematical finance, econophysics, statistics and operations research. 

Matt Burke is a senior lecturer in Finance at Sheffield Hallam University. He holds a PhD in Finance from the University of East Anglia. Burkes main research interests lie in asset pricing and climate finance. 
Chapter
1. Introduction
Chapter
2. Summary statistics and elementary data presentation 
Chapter
3. Basic hypothesis tests
Chapter
4. An introduction to regression
Chapter
5. The extra sum of squares principle and regression modelling
assumptions 
Chapter
6. Violations of regression modelling assumptions autocorrelation 
Chapter
7. Violations of regression modelling assumptions
multicollinearity 
Chapter
8. Dummy variable regression models 
Chapter
9. Qualitative response regression models 
Chapter
10. Linear mixed and generalised linear mixed models 
Chapter
11. Non-financial time series models 
Chapter
12. Modelling financial price data  
Chapter
13. ARCH/GARCH models
Matthew Burke is director of a group focusing on remote database administration: implementation, monitoring, backup and recovery, tuning, and high availability (Implicit Technical Solutions, Remote Management Services: http://www.implicit-its.com). The group is currently servicing the needs of numerous clients in both domestic and international locations. He is recognized within the firm as a technical leader in the areas of Real Application Clusters, Parallel Query, and Replication. Matthew Burke has over two decades of Oracle database administration and development experience. He specializes as an oracle Application DBA in managing all technical aspects of the Oracle E-Business Suite.