Rights Contact Login For More Details
More About This Title The Mathematics of Banking and Finance
The Mathematics of Banking offers an intermediate guide to the various techniques used in the industry, and a consideration of how each one should be approached. Written in a practical style, it will enable readers to quickly appreciate the purpose of the techniques and, through illustrations, see how they can be applied in practice. Coverage is extensive and includes techniques such as VaR analysis, Monte Carlo simulation, extreme value theory, variance and many others.
- A practical review of mathematical techniques needed in banking which does not expect a high level of mathematical competence from the reader
MICHAEL COX has spent 25 years teaching quantitative methods to a wide variety of undergraduate students in departments ranging from agriculture, engineering, history, economics, business and medicine. For over 20 years he has taught both statistics and management science to MBA students.
During his career he has published some 50 referred papers in such diverse areas as statistical process control, total quality management, multidimensional scaling and the analytical hierarchy process. In addition Michael has co-authored two text books and developed a major piece of software.
Michael works in applicable mathematics, the solution of real world problems employing statistical and management science techniques. Most of this research has included computer applications.
1 Introduction to How to Display Data and the Scatter Plot.
2 Bar Charts.
4 Probability Theory.
5 Standard Terms in Statistics.
7 Probability Distribution Functions.
8 Normal Distribution.
9 Comparison of the Means, Sample Sizes and Hypothesis Testing.
10 Comparison of Variances.
11 Chi-squared Goodness of Fit Test.
12 Analysis of Paired Data.
13 Linear Regression.
14 Analysis of Variance.
15 Design and Approach to the Analysis of Data.
16 Linear Programming: Graphical Method.
17 Linear Programming: Simplex Method.
18 Transport Problems.
19 Dynamic Programming.
20 Decision Theory.
21 Inventory and Stock Control.
22 Simulation: Monte Carlo Methods.
23 Reliability: Obsolescence.
24 Project Evaluation.
25 Risk and Uncertainty.
26 Time Series Analysis.
28 Value at Risk.
29 Sensitivity Analysis.
30 Scenario Analysis.
31 An Introduction to Neural Networks.
Appendix Mathematical Symbols and Notation.