Simulation and Monte Carlo: with applications in finance and MCMC
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York ; Chichester
Wiley
2007
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XIV, 333 S. graph. Darst. |
| ISBN: | 0470854944 9780470854945 0470854952 9780470854952 |
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| 100 | 1 | |a Dagpunar, John |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Simulation and Monte Carlo |b with applications in finance and MCMC |c J. S. Dagpunar |
| 264 | 1 | |a New York ; Chichester |b Wiley |c 2007 | |
| 300 | |a XIV, 333 S. |b graph. Darst. | ||
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| 500 | |a Includes bibliographical references and index | ||
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| 650 | 4 | |a Monte-Carlo, Méthode de | |
| 650 | 4 | |a Simulation, Méthodes de | |
| 650 | 4 | |a Monte Carlo method | |
| 650 | 4 | |a Simulation method | |
| 650 | 4 | |a Business mathematics | |
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| adam_text | SIMULATION AND MONTE CARLO WITH APPLICATIONS IN FINANCE AND MCMC J. S.
DAGPUNAR SCHOOL OF MATHEMATICS UNIVERSITY OF EDINBURGH, UK »ICENTENNIAL
1 8 O 7 WILEY 2 O O 7 JOHN WILEY & SONS, LTD CONTENTS 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 2.1.1 MIXED LINEAR CONGRUENTIAL GENERATORS 2.1.2
MULTIPLICATIVE LINEAR CONGRUENTIAL GENERATORS 2.2 THEORETICAL TESTS FOR
RANDOM NUMBERS 2.2.1 PROBLEMS OF INCREASING DIMENSION 2.3 SHUFFLED
GENERATOR 2.4 EMPIRICAL TESTS 2.4.1 FREQUENCY TEST 2.4.2 SERIAL TEST
2.4.3 OTHER 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 4.1.1 BOX-MUELLER
METHOD 4.1.2 AN IMPROVED ENVELOPE REJECTION METHOD 4.2 LOGNORMAL
DISTRIBUTION VIII CONTENTS 4.3 BIVARIATE NORMAL DENSITY 63 4.4 GAMMA
DISTRIBUTION 64 4.4.1 CHENG S LOG-LOGISTIC METHOD 65 4.5 BETA
DISTRIBUTION 67 4.5.1 BETA LOG-LOGISTIC METHOD 67 4.6 CHI-SQUARED
DISTRIBUTION 69 4.7 STUDENT S T DISTRIBUTION 69 4.8 GENERALIZED INVERSE
GAUSSIAN DISTRIBUTION 71 4.9 POISSON DISTRIBUTION 73 4.10 BINOMIAL
DISTRIBUTION 74 4.11 NEGATIVE BINOMIAL DISTRIBUTION 74 4.12 PROBLEMS 75
5 VARIANCE REDUCTION 79 5.1 ANTITHETIC VARIATES 79 5.2 IMPORTANCE
SAMPLING 82 5.2.1 EXCEEDANCE PROBABILITIES FOR SUMS OF I.I.D. RANDOM
VARIABLES 86 5.3 STRATIFIED SAMPLING 89 5.3.1 A STRATIFICATION EXAMPLE
92 5.3.2 POST STRATIFICATION 96 5.4 CONTROL VARIATES 98 5.5 CONDITIONAL
MONTE CARLO 101 5.6 PROBLEMS 103 6 SIMULATION AND FINANCE 107 6.1
BROWNIAN MOTION 108 6.2 ASSET PRICE MOVEMENTS 109 6.3 PRICING SIMPLE
DERIVATIVES AND OPTIONS 111 6.3.1 EUROPEAN CALL 113 6.3.2 EUROPEAN PUT
114 6.3.3 CONTINUOUS INCOME 115 6.3.4 DELTA HEDGING 115 6.3.5 DISCRETE
HEDGING 116 6.4 ASIAN OPTIONS 118 6.4.1 NAIVE SIMULATION 118 6.4.2
IMPORTANCE AND STRATIFIED VERSION 119 6.5 BASKET OPTIONS 123 6.6
STOCHASTIC VOLATILITY 126 6.7 PROBLEMS 130 7 DISCRETE EVENT SIMULATION
135 7.1 POISSON PROCESS 136 7.2 TIME-DEPENDENT POISSON PROCESS 140 7.3
POISSON PROCESSES IN THE PLANE 141 7.4 MARKOV CHAINS 142 7.4.1
DISCRETE-TIME MARKOV CHAINS 142 7.4.2 CONTINUOUS-TIME MARKOV CHAINS 143
7.5 REGENERATIVE ANALYSIS 144 7.6 SIMULATING A G/G/1 QUEUEING SYSTEM
USING THE THREE-PHASE METHOD 146 7.7 SIMULATING A HOSPITAL WARD 149 7.8
PROBLEMS 151 8 MARKOV CHAIN MONTE CARLO 157 8.1 BAYESIAN STATISTICS 157
8.2 MARKOV CHAINS AND THE METROPOLIS-HASTINGS (MH) ALGORITHM 159 8.3
RELIABILITY INFERENCE USING AN INDEPENDENCE SAMPLER 163 8.4 SINGLE
COMPONENT METROPOLIS-HASTINGS AND GIBBS SAMPLING 165 8.4.1 ESTIMATING
MULTIPLE FAILURE RATES 167 8.4.2 CAPTURE-RECAPTURE 171 8.4.3 MINIMAL
REPAIR 172 8.5 OTHER ASPECTS OF GIBBS SAMPLING 176 8.5.1 SLICE SAMPLING
176 8.5.2 COMPLETIONS 178 8.6 PROBLEMS 179 9 SOLUTIONS 187 9.1 SOLUTIONS
1 187 9.2 SOLUTIONS 2 187 9.3 SOLUTIONS 3 190 9.4 SOLUTIONS 4 191 9.5
SOLUTIONS 5 195 9.6 SOLUTIONS 6 196 9.7 SOLUTIONS 7 202 9.8 SOLUTIONS 8
205 APPENDIX 1: SOLUTIONS TO PROBLEMS IN CHAPTER 1 209 APPENDIX 2:
RANDOM NUMBER GENERATORS 227 APPENDIX 3: COMPUTATIONS OF ACCEPTANCE
PROBABILITIES 229 APPENDIX 4: RANDOM VARIATE GENERATORS (STANDARD
DISTRIBUTIONS) 233 APPENDIX 5: VARIANCE REDUCTION 239 APPENDIX 6:
SIMULATION AND FINANCE 249 APPENDIX 7: DISCRETE EVENT SIMULATION 283
APPENDIX 8: MARKOV CHAIN MONTE CARLO 299 REFERENCES 325 INDEX 329
|
| adam_txt |
SIMULATION AND MONTE CARLO WITH APPLICATIONS IN FINANCE AND MCMC J. S.
DAGPUNAR SCHOOL OF MATHEMATICS UNIVERSITY OF EDINBURGH, UK »ICENTENNIAL
1 8 O 7 WILEY 2 O O 7 JOHN WILEY & SONS, LTD CONTENTS 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 2.1.1 MIXED LINEAR CONGRUENTIAL GENERATORS 2.1.2
MULTIPLICATIVE LINEAR CONGRUENTIAL GENERATORS 2.2 THEORETICAL TESTS FOR
RANDOM NUMBERS 2.2.1 PROBLEMS OF INCREASING DIMENSION 2.3 SHUFFLED
GENERATOR 2.4 EMPIRICAL TESTS 2.4.1 FREQUENCY TEST 2.4.2 SERIAL TEST
2.4.3 OTHER 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 4.1.1 BOX-MUELLER
METHOD 4.1.2 AN IMPROVED ENVELOPE REJECTION METHOD 4.2 LOGNORMAL
DISTRIBUTION VIII CONTENTS 4.3 BIVARIATE NORMAL DENSITY 63 4.4 GAMMA
DISTRIBUTION 64 4.4.1 CHENG'S LOG-LOGISTIC METHOD 65 4.5 BETA
DISTRIBUTION 67 4.5.1 BETA LOG-LOGISTIC METHOD 67 4.6 CHI-SQUARED
DISTRIBUTION 69 4.7 STUDENT'S T DISTRIBUTION 69 4.8 GENERALIZED INVERSE
GAUSSIAN DISTRIBUTION 71 4.9 POISSON DISTRIBUTION 73 4.10 BINOMIAL
DISTRIBUTION 74 4.11 NEGATIVE BINOMIAL DISTRIBUTION 74 4.12 PROBLEMS 75
5 VARIANCE REDUCTION 79 5.1 ANTITHETIC VARIATES 79 5.2 IMPORTANCE
SAMPLING 82 5.2.1 EXCEEDANCE PROBABILITIES FOR SUMS OF I.I.D. RANDOM
VARIABLES 86 5.3 STRATIFIED SAMPLING 89 5.3.1 A STRATIFICATION EXAMPLE
92 5.3.2 POST STRATIFICATION 96 5.4 CONTROL VARIATES 98 5.5 CONDITIONAL
MONTE CARLO 101 5.6 PROBLEMS 103 6 SIMULATION AND FINANCE 107 6.1
BROWNIAN MOTION 108 6.2 ASSET PRICE MOVEMENTS 109 6.3 PRICING SIMPLE
DERIVATIVES AND OPTIONS 111 6.3.1 EUROPEAN CALL 113 6.3.2 EUROPEAN PUT
114 6.3.3 CONTINUOUS INCOME 115 6.3.4 DELTA HEDGING 115 6.3.5 DISCRETE
HEDGING 116 6.4 ASIAN OPTIONS 118 6.4.1 NAIVE SIMULATION 118 6.4.2
IMPORTANCE AND STRATIFIED VERSION 119 6.5 BASKET OPTIONS 123 6.6
STOCHASTIC VOLATILITY 126 6.7 PROBLEMS 130 7 DISCRETE EVENT SIMULATION
135 7.1 POISSON PROCESS 136 7.2 TIME-DEPENDENT POISSON PROCESS 140 7.3
POISSON PROCESSES IN THE PLANE 141 7.4 MARKOV CHAINS 142 7.4.1
DISCRETE-TIME MARKOV CHAINS 142 7.4.2 CONTINUOUS-TIME MARKOV CHAINS 143
7.5 REGENERATIVE ANALYSIS 144 7.6 SIMULATING A G/G/1 QUEUEING SYSTEM
USING THE THREE-PHASE METHOD 146 7.7 SIMULATING A HOSPITAL WARD 149 7.8
PROBLEMS 151 8 MARKOV CHAIN MONTE CARLO 157 8.1 BAYESIAN STATISTICS 157
8.2 MARKOV CHAINS AND THE METROPOLIS-HASTINGS (MH) ALGORITHM 159 8.3
RELIABILITY INFERENCE USING AN INDEPENDENCE SAMPLER 163 8.4 SINGLE
COMPONENT METROPOLIS-HASTINGS AND GIBBS SAMPLING 165 8.4.1 ESTIMATING
MULTIPLE FAILURE RATES 167 8.4.2 CAPTURE-RECAPTURE 171 8.4.3 MINIMAL
REPAIR 172 8.5 OTHER ASPECTS OF GIBBS SAMPLING 176 8.5.1 SLICE SAMPLING
176 8.5.2 COMPLETIONS 178 8.6 PROBLEMS 179 9 SOLUTIONS 187 9.1 SOLUTIONS
1 187 9.2 SOLUTIONS 2 187 9.3 SOLUTIONS 3 190 9.4 SOLUTIONS 4 191 9.5
SOLUTIONS 5 195 9.6 SOLUTIONS 6 196 9.7 SOLUTIONS 7 202 9.8 SOLUTIONS 8
205 APPENDIX 1: SOLUTIONS TO PROBLEMS IN CHAPTER 1 209 APPENDIX 2:
RANDOM NUMBER GENERATORS 227 APPENDIX 3: COMPUTATIONS OF ACCEPTANCE
PROBABILITIES 229 APPENDIX 4: RANDOM VARIATE GENERATORS (STANDARD
DISTRIBUTIONS) 233 APPENDIX 5: VARIANCE REDUCTION 239 APPENDIX 6:
SIMULATION AND FINANCE 249 APPENDIX 7: DISCRETE EVENT SIMULATION 283
APPENDIX 8: MARKOV CHAIN MONTE CARLO 299 REFERENCES 325 INDEX 329 |
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| spelling | Dagpunar, John Verfasser aut Simulation and Monte Carlo with applications in finance and MCMC J. S. Dagpunar New York ; Chichester Wiley 2007 XIV, 333 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Mathématiques financières Monte-Carlo, Méthode de Simulation, Méthodes de Monte Carlo method Simulation method Business mathematics Zufallszahlen (DE-588)4124968-9 gnd rswk-swf Monte-Carlo-Simulation (DE-588)4240945-7 gnd rswk-swf Varianzanalyse (DE-588)4187413-4 gnd rswk-swf Markov-Ketten-Monte-Carlo-Verfahren (DE-588)4508520-1 gnd rswk-swf Monte-Carlo-Simulation (DE-588)4240945-7 s Markov-Ketten-Monte-Carlo-Verfahren (DE-588)4508520-1 s Zufallszahlen (DE-588)4124968-9 s Varianzanalyse (DE-588)4187413-4 s DE-604 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015514452&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Dagpunar, John Simulation and Monte Carlo with applications in finance and MCMC Mathématiques financières Monte-Carlo, Méthode de Simulation, Méthodes de Monte Carlo method Simulation method Business mathematics Zufallszahlen (DE-588)4124968-9 gnd Monte-Carlo-Simulation (DE-588)4240945-7 gnd Varianzanalyse (DE-588)4187413-4 gnd Markov-Ketten-Monte-Carlo-Verfahren (DE-588)4508520-1 gnd |
| subject_GND | (DE-588)4124968-9 (DE-588)4240945-7 (DE-588)4187413-4 (DE-588)4508520-1 |
| title | Simulation and Monte Carlo with applications in finance and MCMC |
| title_auth | Simulation and Monte Carlo with applications in finance and MCMC |
| title_exact_search | Simulation and Monte Carlo with applications in finance and MCMC |
| title_exact_search_txtP | Simulation and Monte Carlo with applications in finance and MCMC |
| title_full | Simulation and Monte Carlo with applications in finance and MCMC J. S. Dagpunar |
| title_fullStr | Simulation and Monte Carlo with applications in finance and MCMC J. S. Dagpunar |
| title_full_unstemmed | Simulation and Monte Carlo with applications in finance and MCMC J. S. Dagpunar |
| title_short | Simulation and Monte Carlo |
| title_sort | simulation and monte carlo with applications in finance and mcmc |
| title_sub | with applications in finance and MCMC |
| topic | Mathématiques financières Monte-Carlo, Méthode de Simulation, Méthodes de Monte Carlo method Simulation method Business mathematics Zufallszahlen (DE-588)4124968-9 gnd Monte-Carlo-Simulation (DE-588)4240945-7 gnd Varianzanalyse (DE-588)4187413-4 gnd Markov-Ketten-Monte-Carlo-Verfahren (DE-588)4508520-1 gnd |
| topic_facet | Mathématiques financières Monte-Carlo, Méthode de Simulation, Méthodes de Monte Carlo method Simulation method Business mathematics Zufallszahlen Monte-Carlo-Simulation Varianzanalyse Markov-Ketten-Monte-Carlo-Verfahren |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015514452&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT dagpunarjohn simulationandmontecarlowithapplicationsinfinanceandmcmc |