Statistics in Finance

  • ID: 2175133
  • Book
  • 354 Pages
  • John Wiley and Sons Ltd
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The chapters in this book describe various aspects of the application of statistical methods in finance. It will interest and attract statisticians to this area, illustrate some of the many ways that statistical tools are used in financial applications, and give some indication of problems which are still outstanding. The statisticians will be stimulated to learn more about the kinds of models and techniques outlined in the book – both the domain of finance and the science of statistics will benefit from increased awareness by statisticians of the problems, models, and techniques applied in financial applications. For this reason, extensive references are given. The level of technical detail varies between the chapters. Some present broad non–technical overviews of an area, while others describe the mathematical niceties. This illustrates both the range of possibilities available in the area for statisticians, while simultaneously giving a flavour of the different kinds of mathematical and statistical skills required. Whether you favour data analysis or mathematical manipulation, if you are a statistician there are problems in finance which are appropriate to your skills.
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List of contributors.


1. Introduction (David J. Hand and Saul D. Jacka).


2. The Relationship Between Finance and Actuarial Science (Philip Booth and Paul King).

2.1 Actuaries and investment.

2.2 Introduction.

2.3 Mean variance models in finance.

2.4 The capital asset pricing model.

2.5 Risk adjusted discount rates.

2.6 Insights and limitations of portfolio selection models.

2.7 Principles of asset allocation.

2.8 Developing mean variance models: the inclusion of liabilities.

2.9 Generalising mean variance models.

2.10 Immunisation.

2.11 Stochastic asset–liability modelling.

2.12 Conclusions.

3. Actuarial Applications of Generalised Linear Models (Steven Haberman and Arthur E. Renshaw).

3.1 Introduction.

3.2 Introduction to generalised linear models.

3.3 Generalised linear models.

3.4 Survival modelling and graduation.

3.5 Multiple state models.

3.6 Risk classification.

3.7 Premium rating.

3.8 Claims reserving in non–life insurance.

3.9 Conclusions.


4. Consumer Credit and Statistics (David J. Hand).

4.1 Introduction.

4.2 The judgemental approach to granting credit.

4.3 What scoring can be used for.

4.4 What is a scorecard and how to construct one.

4.5 The data.

4.6 Reject inference.

4.7 Population drift.

4.8 Reject option.

4.9 Assessing performance.

4.10 Legal issues.

4.11 Consumer versus corporate loan.

4.12 Conclusions.

5. Methodologies for Classifying Applicants for Credit (Lyn C. Thomas).

5.1 History of credit scoring.

5.2 Credit scoring: the art and the objective.

5.3 Regression and logistic regression approaches.

5.4 Other statistical approaches.

5.5 Mathematical programming.

5.6 Neural networks and expert systems.

5.7 Genetic algorithms.

5.8 Conclusions.

6. Credit Scoring and Quality Management (Kevin J. Leonard).

6.1 Risk management.

6.2 What is credit scoring.

6.3 Using the scorecards.

6.4 Examination of detailed reports.

6.5 Total quality management in information systems (TQMIS).

6.6 The need for change in continuous improvement.

7. Consumer Credit and Business Cycles (Jonathan Crook).

7.1 Introduction and summary.

7.2 Descriptive trends.

7.3 Credit and business cycle theories.

7.4 Microeconomic models of consumer credit.

7.5 Summary.


8. Probability in France: an introduction (Saul D. Jacka).

8.1 A brief survey.

8.2 Some reminders on stochastic calculus.

8.3 The Black Scholes framework.

8.4 Arbitrage and option pricing in a general securities market'.

8.5 Changes of measure.

8.6 American options.

8.7 Some final remarks.

9. Introduction to Financial Economics (Stewart D. Hodges).

9.1 Introduction.

9.2 The role of the capital market.

9.3 The state preference framework.

9.4 The expected utility paradigm.

9.5 Summary.

10. American Options (Damien Lamberton).

10.1 Optimal stopping: discrete time.

10.2 Optimal stopping: continuous time.

10.3 The value function of an American option.

10.4 Numerical methods.

11. Notes on Term Structure Models (Saul D. Jacka).

11.1 Introduction.

11.2 Spot–rate models.

11.3 Forward rate models.

11.4 Affine jump models.

11.5 Pricing formulate.

12. Default Risk (Dilip Madan).

12.1 Introduction.

12.2 A general perspective on the modelling of default risk.

12.3 The European option theoretic approach.

12.4 The barrier option theoretic approach.

12.5 Inaccessible default times.

12.6 Adjusting discount rates for default exposure.

12.7 The defaultable HJM model.

12.8 Conclusions.

13. Non–parametric Methods and Option Pricing (Eric Ghysels, Éric Renault, Olivier Torrès and Valentin Patilea).

13.1 Introduction.

13.2 Non–parametric model–free option pricing.

13.3 Non–parametric specification of equivalent martingale measures.

13.4 Extended Black and Scholes models and objective driven inference.

14. Stochastic Volatility (David G. Hobson).

14.1 Volatility and the need for stochastic volatility models.

14.2 Non–constant volatility models.

14.3 Option pricing for stochastic volatility models.

14.4 Discrete–time models.

14.5 Conclusions.

15. Market Time and Asset Price Movements: Theory and Estimation (Eric Ghysels, Christian Gouriéroux and Joanna Jasiak).

15.1 Introduction.

15.2 Study of mean and covariance functions.

15.3 Examples.

15.4 Statistical inference for subordinated stochastic processes.

15.5 Testing the hypothesis of time deformation.

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David J. Hand
Saul D. Jacka
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