Simulation and Monte Carlo (eBook)

With Applications in Finance and MCMC

J. S. Dagpunar (Autor)

eBook Download: PDF

2007 | 1. Auflage
348 Seiten
John Wiley & Sons (Verlag)
978-0-470-06134-3 (ISBN)

Infos

Simulation and Monte Carlo is aimed at students studying for
degrees in Mathematics, Statistics, Financial Mathematics,
Operational Research, Computer Science, and allied subjects, who
wish an up-to-date account of the theory and practice of
Simulation. Its distinguishing features are in-depth accounts of
the theory of Simulation, including the important topic of variance
reduction techniques, together with illustrative applications in
Financial Mathematics, Markov chain Monte Carlo, and Discrete Event
Simulation.

Each chapter contains a good selection of exercises and
solutions with an accompanying appendix comprising a Maple
worksheet containing simulation procedures. The worksheets can also
be downloaded from the web site supporting the book. This
encourages readers to adopt a hands-on approach in the effective
design of simulation experiments.

Arising from a course taught at Edinburgh University over
several years, the book will also appeal to practitioners working
in the finance industry, statistics and operations research.

Autor

J. S. Dagpunar is the author of Simulation and Monte Carlo: With Applications in Finance and MCMC, published by Wiley.

Inhalt

Preface.

Glossary.

1 Introduction to simulation and Monte Carlo.

1.1 Evaluating a definite integral.

1.2 Monte Carlo is integral estimation.

1.3 An example.

1.4 A simulation using Maple.

1.5 Problems.

2 Uniform random numbers.

2.1 Linear congruential generators.

2.2 Theoretical tests for random numbers.

2.3 Shuffled generator.

2.4 Empirical tests.

2.5 Combinations of generators.

2.6 The seed(s) in a random number generator.

2.7 Problems.

3 General methods for generating random variates.

3.1 Inversion of the cumulative distribution function.

3.2 Envelope rejection.

3.3 Ratio of uniforms method.

3.4 Adaptive rejection sampling.

3.5 Problems.

4 Generation of variates from standard distributions.

4.1 Standard normal distribution.

4.2 Lognormal distribution.

4.3 Bivariate normal density.

4.4 Gamma distribution.

4.5 Beta distribution.

4.6 Chi-squared distribution.

4.7 Student's t distribution.

4.8 Generalized inverse Gaussian distribution.

4.9 Poisson distribution.

4.10 Binomial distribution.

4.11 Negative binomial distribution.

4.12 Problems.

5 Variance reduction.

5.1 Antithetic variates.

5.2 Importance sampling.

5.3 Stratified sampling.

5.4 Control variates.

5.5 Conditional Monte Carlo.

5.6 Problems.

6 Simulation and finance.

6.1 Brownian motion.

6.2 Asset price movements.

6.3 Pricing simple derivatives and options.

6.4 Asian options.

6.5 Basket options.

6.6 Stochastic volatility.

6.7 Problems.

7 Discrete event simulation.

7.1 Poisson process.

7.2 Time-dependent Poisson process.

7.3 Poisson processes in the plane.

7.4 Markov chains.

7.5 Regenerative analysis.

7.6 Simulating a G/G/1 queueing system using the three-phase
method.

7.7 Simulating a hospital ward.

7.8 Problems.

8 Markov chain Monte Carlo.

8.1 Bayesian statistics.

8.2 Markov chains and the Metropolis-Hastings (MH)
algorithm.

8.3 Reliability inference using an independence sampler.

8.4 Single component Metropolis-Hastings and Gibbs
sampling.

8.5 Other aspects of Gibbs sampling.

8.6 Problems.

9 Solutions.

9.1 Solutions 1.

9.2 Solutions 2.

9.3 Solutions 3.

9.4 Solutions 4.

9.5 Solutions 5.

9.6 Solutions 6.

9.7 Solutions 7.

9.8 Solutions 8.

Appendix 1: Solutions to problems in Chapter 1.

Appendix 2: Random Number Generators.

Appendix 3: Computations of acceptance probabilities.

Appendix 4: Random variate generators (standard
distributions).

Appendix 5: Variance Reduction.

Appendix 6: Simulation and Finance.

Appendix 7: Discrete event simulation.

Appendix 8: Markov chain Monte Carlo.

References.

Index.

Rezensionen

?This book would be immensely useful for any practitioner seeking
to learn more about this field, as well as for lecturers seeking a
reliable and informative text.? ( Significance, September
2009)

"The book does a nice job of discussing, developing, and
presenting the mathematical aspects of random processes, random
number generation, and Markov chain Monte Carlo (MCMC) methods. I
particularly like the notation used and the depth of proofs
offered; they are technically correct, well organized, and nicely
presented." (Journal of the American Statistical
Association, June 2008)

?Dagpunar presents a textbook based on 20-hour courses he
has taught for advanced students of mathematics and students of
financial mathematics.? (SciTech Book Reviews, June
2007)

"?excellent for students and practitioners who don't have
previous experience with simulation methods?a great contribution."
(MAA Reviews, April 5, 2007)

Produkt-Details

• Erscheint lt. Verlag: 4.4.2007
• Reihe/Serie: Wiley Series in Probability and Statistics
• Reihe/Serie: Wiley Series in Probability and Statistics
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik
• Schlagworte: Angewandte Wahrscheinlichkeitsrechnung u. Statistik • Applied Probability & Statistics • Computational & Graphical Statistics • Rechnergestützte u. graphische Statistik • Rechnergestützte u. graphische Statistik • Simulation • Statistics • Statistik • Technische Statistik
• ISBN-10: 0-470-06134-0 / 0470061340
• ISBN-13: 978-0-470-06134-3 / 9780470061343