The Mathematics of Banking and Finance
Wiley Finance Series
1. Edition March 2006
310 Pages, Hardcover
Wiley & Sons Ltd
Short Description
Mathematics has become even more widespread with operational risk modeling now being developed to add to market and credit risk modeling. Boards are confronted with data they cannot understand and are using data in a way that probably is not appropriate. Written as an intermediate-level guide, Mathematics of Banking explains various mathematical techniques available to banks, enables readers to quickly appreciate the purpose of the technique, and, through illustrations, see how it can be applied in practice.
The world of business has changed significantly in recent years. Why? Because business has increasingly made more use of the probability, statistical methods and analytic techniques in areas where they previously were not used at all.
You may feel rather uncomfortable about all of this. Perhaps you gave up mathematics, wondering what the relevance of the subject really was. Others may just need to be reminded what some of the techniques actually mean.
Mathematics of Banking takes the type of techniques that you may come across in the developing world of banking, including operational risk, and explains when the techniques could be used and what if any limitations there are to these techniques.
The book 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 other important models.
1 Introduction to How to Display Data and the Scatter Plot.
2 Bar Charts.
3 Histograms.
4 Probability Theory.
5 Standard Terms in Statistics.
6 Sampling.
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.
27 Reliability.
28 Value at Risk.
29 Sensitivity Analysis.
30 Scenario Analysis.
31 An Introduction to Neural Networks.
Appendix Mathematical Symbols and Notation.
Index.
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.
