Buch, Englisch, 404 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1700 g
With Stochastic Processes and an Introduction to Mathematical Finance
Buch, Englisch, 404 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1700 g
Reihe: Undergraduate Texts in Mathematics
ISBN: 978-0-387-95578-0
Verlag: Springer
This book provides an introduction to probability theory and its applications. The emphasis is on essential probabilistic reasoning, which is illustrated with a large number of samples. The fourth edition adds material related to mathematical finance as well as expansions on stable laws and martingales.
From the reviews: "Almost thirty years after its first edition, this charming book continues to be an excellent text for teaching and for self study." -- STATISTICAL PAPERS
Zielgruppe
Lower undergraduate
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
Weitere Infos & Material
1 Set.- 1.1 Sample sets.- 1.2 Operations with sets.- 1.3 Various relations.- 1.4 Indicator.- Exercises.- 2 Probability.- 2.1 Examples of probability.- 2.2 Definition and illustrations.- 2.3 Deductions from the axioms.- 2.4 Independent events.- 2.5 Arithmetical density.- Exercises.- 3 Counting.- 3.1 Fundamental rule.- 3.2 Diverse ways of sampling.- 3.3 Allocation models; binomial coefficients.- 3.4 How to solve it.- Exercises.- 4 Random Variables.- 4.1 What is a random variable?.- 4.2 How do random variables come about?.- 4.3 Distribution and expectation.- 4.4 Integer-valued random variables.- 4.5 Random variables with densities.- 4.6 General case.- Exercises.- Appendix 1: Borel Fields and General Random Variables.- 5 Conditioning and Independence.- 5.1 Examples of conditioning.- 5.2 Basic formulas.- 5.3 Sequential sampling.- 5.4 Pólya’s urn scheme.- 5.5 Independence and relevance.- 5.6 Genetical models.- Exercises.- 6 Mean, Variance, and Transforms.- 6.1 Basic properties of expectation.- 6.2 The density case.- 6.3 Multiplication theorem; variance and covariance.- 6.4 Multinomial distribution.- 6.5 Generating function and the like.- Exercises.- 7 Poisson and Normal Distributions.- 7.1 Models for Poisson distribution.- 7.2 Poisson process.- 7.3 From binomial to normal.- 7.4 Normal distribution.- 7.5 Central limit theorem.- 7.6 Law of large numbers.- Exercises.- Appendix 2: Stirling’s Formula and de Moivre-Laplace’ Theorem.- 8 From Random Walks to Markov Chains.- 8.1 Problems of the wanderer or gambler.- 8.2 Limiting schemes.- 8.3 Transition probabilities.- 8.4 Basic structure of Markov chains.- 8.5 Further developments.- 8.6 Steady state.- 8.7 Winding up (or down?).- Exercises.- Appendix 3: Martingale.- 9 Mean-Variance Pricing Model.- 9.1 An investments primer.- 9.2 Asset return and risk.- 9.3 Portfolio allocation.- 9.4 Diversification.- 9.5 Mean-variance optimization.- 9.6 Asset return distributions.- 9.7 Stable probability distributions.- Exercises.- Appendix 4: Pareto and Stable Laws.- 10 Option Pricing Theory.- 10.1 Options basics.- 10.2 Arbitrage-free pricing: 1-period model.- 10.3 Arbitrage-free pricing: N-period model.- 10.4 Fundamental asset pricing theorems.- Exercises.- General References.- Answers to Problems.- Values of the Standard Normal Distribution Function.
