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|a 9780470531112
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|a 1118593642
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|a 1118593618
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|a HG106
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|a Brandimarte, Paolo
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| 245 |
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|a Handbook in Monte Carlo simulation
|b applications in financial engineering, risk management, and economics
|c Paolo Brandimarte
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| 260 |
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|a Hoboken, New Jersey
|b John Wiley & Sons
|c 2014
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| 300 |
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|a 1 online resource
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|a 3.7 Stochastic differential equations3.8 Dimensionality reduction; 3.9 Risk-neutral derivative pricing; For further reading; References; Chapter Four: Estimation and Fitting; 4.1 Basic inferential statistics in R; 4.2 Parameter estimation; 4.3 Checking the fit of hypothetical distributions; 4.4 Estimation of linear regression models by ordinary least squares; 4.5 Fitting time series models; 4.6 Subjective probability: The Bayesian view; For further reading; References; Part Three: Sampling and Path Generation; Chapter Five: Random Variate Generation
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|a Includes bibliographical references and index
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|a 2.3 Extension to higher dimensions: Product rules2.4 Alternative approaches for high-dimensional integration; 2.5 Relationship with moment matching; 2.6 Numerical integration in R; For further reading; References; Part Two: Input Analysis: Modeling and Estimation; Chapter Three: Stochastic Modeling in Finance and Economics; 3.1 Introductory examples; 3.2 Some common probability distributions; 3.3 Multivariate distributions: Covariance and correlation; 3.4 Modeling dependence with copulas; 3.5 Linear regression models: A probabilistic view; 3.6 Time series models
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|a Part Four: Output Analysis and Efficiency ImprovementChapter Seven: Output Analysis; 7.1 Pitfalls in output analysis; 7.2 Setting the number of replications; 7.3 A world beyond averages; 7.4 Good and bad news; For further reading; References; Chapter Eight: Variance Reduction Methods; 8.1 Antithetic sampling; 8.2 Common random numbers; 8.3 Control variates; 8.4 Conditional Monte Carlo; 8.5 Stratified sampling; 8.6 Importance sampling; For further reading; References; Chapter Nine: Low-Discrepancy Sequences; 9.1 Low-discrepancy sequences; 9.2 Halton sequences
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|a Half Title page; Title page; Copyright page; Preface; Part One: Overview and Motivation; Chapter One: Introduction to Monte Carlo Methods; 1.1 Historical origin of Monte Carlo simulation; 1.2 Monte Carlo simulation vs. Monte Carlo sampling; 1.3 System dynamics and the mechanics of Monte Carlo simulation; 1.4 Simulation and optimization; 1.5 Pitfalls in Monte Carlo simulation; 1.6 Software tools for Monte Carlo simulation; 1.7 Prerequisites; For further reading; References; Chapter Two: Numerical Integration Methods; 2.1 Classical quadrature formulas; 2.2 Gaussian quadrature
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|a 5.1 The structure of a Monte Carlo simulation5.2 Generating pseudorandom numbers; 5.3 The inverse transform method; 5.4 The acceptance-rejection method; 5.5 Generating normal variates; 5.6 Other ad hoc methods; 5.7 Sampling from copulas; For further reading; References; Chapter Six: Sample Path Generation for Continuous-Time Models; 6.1 Issues in path generation; 6.2 Simulating geometric Brownian motion; 6.3 Sample paths of short-term interest rates; 6.4 Dealing with stochastic volatility; 6.5 Dealing with jumps; For further reading; References
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|a Finance / Mathematical models / fast
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|a Finance / Mathematical models / http://id.loc.gov/authorities/subjects/sh85048260
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|a BUSINESS & ECONOMICS / Reference / bisacsh
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| 653 |
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|a Méthode de Monte-Carlo
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| 653 |
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|a Monte Carlo method / fast
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|a Économie politique / Modèles mathématiques
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|a Economics / Mathematical models / fast
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| 653 |
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|a BUSINESS & ECONOMICS / Economics / General / bisacsh
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| 653 |
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|a Finances / Modèles mathématiques
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| 653 |
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|a Economics / Mathematical models / http://id.loc.gov/authorities/subjects/sh85040857
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| 653 |
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|a Monte Carlo method / http://id.loc.gov/authorities/subjects/sh85087032
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 490 |
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|a Financial engineering and econometrics
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| 776 |
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|z 9780470531112
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|u https://learning.oreilly.com/library/view/~/9780470531112/?ar
|x Verlag
|3 Volltext
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|a 330.01/518282
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|a 332
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|a 330
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|a An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to
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