@Throughout banking, mathematical techniques are used. Some of these are within software products or models; mathematicians use others to analyse data. The current literature on the subject is either very basic or very advanced.
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.
目 錄
troduction 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
