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.
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.