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Asset price dynamics, volatility, and prediction [[electronic resource] /] / Stephen J. Taylor
| Asset price dynamics, volatility, and prediction [[electronic resource] /] / Stephen J. Taylor |
| Autore | Taylor Stephen (Stephen J.) |
| Edizione | [Course Book] |
| Pubbl/distr/stampa | Princeton, N.J., : Princeton University Press, 2007, c2005 |
| Descrizione fisica | 1 online resource (988 p.) |
| Disciplina | 332.60151962 |
| Soggetto topico |
Capital assets pricing model
Finance - Mathematical models |
| ISBN |
1-282-99204-X
9786612992049 1-4008-3925-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Preface -- 1. Introduction -- Part I. Foundations -- 2. Prices and Returns -- 3. Stochastic Processes: Definitions and Examples -- 4. Stylized Facts for Financial Returns -- Part II. Conditional Expected Returns -- 5. The Variance-Ratio Test of the RandomWalk Hypothesis -- 6. Further Tests of the RandomWalk Hypothesis -- 7. Trading Rules and Market Efficiency -- Part III. Volatility Processes -- 8. An Introduction to Volatility -- 9. ARCH Models: Definitions and Examples -- 10. ARCH Models: Selection and Likelihood Methods -- 11. Stochastic Volatility Models -- Part IV. High-Frequency Methods -- 12. High-Frequency Data and Models -- Part V. Inferences from Option Prices -- 13. Continuous-Time Stochastic Processes -- 14. Option Pricing Formulae -- 15. Forecasting Volatility -- 16. Density Prediction for Asset Prices -- Symbols -- References -- Author Index -- Subject Index |
| Record Nr. | UNINA-9910807815503321 |
Taylor Stephen (Stephen J.)
|
||
| Princeton, N.J., : Princeton University Press, 2007, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian methods in finance [[electronic resource] /] / Svetlozar T. Rachev ... [et al.]
| Bayesian methods in finance [[electronic resource] /] / Svetlozar T. Rachev ... [et al.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2008 |
| Descrizione fisica | 1 online resource (351 p.) |
| Disciplina |
332
332.01519542 |
| Altri autori (Persone) | RachevS. T (Svetlozar Todorov) |
| Collana | The Frank J. Fabozzi series |
| Soggetto topico |
Finance - Mathematical models
Bayesian statistical decision theory |
| ISBN |
1-119-20214-0
1-281-21726-3 9786611217266 0-470-24924-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Methods in Finance; Contents; Preface; About the Authors; CHAPTER 1 Introduction; A FEW NOTES ON NOTATION; OVERVIEW; CHAPTER 2 The Bayesian Paradigm; THE LIKELIHOOD FUNCTION; THE BAYES' THEOREM; SUMMARY; CHAPTER 3 Prior and Posterior Information, Predictive Inference; PRIOR INFORMATION; POSTERIOR INFERENCE; BAYESIAN PREDICTIVE INFERENCE; ILLUSTRATION: POSTERIOR TRADE-OFF AND THENORMAL MEAN PARAMETER; SUMMARY; APPENDIX: DEFINITIONS OF SOME UNIVARIATE AND MULTIVARIATE STATISTICAL DISTRIBUTIONS; CHAPTER 4 Bayesian Linear Regression Model; THE UNIVARIATE LINEAR REGRESSION MODEL
THE MULTIVARIATE LINEAR REGRESSION MODELSUMMARY; CHAPTER 5 Bayesian Numerical Computation; MONTE CARLO INTEGRATION; ALGORITHMS FOR POSTERIOR SIMULATION; APPROXIMATION METHODS: LOGISTIC REGRESSION; SUMMARY; CHAPTER 6 Bayesian Framework for Portfolio Allocation; CLASSICAL PORTFOLIO SELECTION; BAYESIAN PORTFOLIO SELECTION; SHRINKAGE ESTIMATORS; UNEQUAL HISTORIES OF RETURNS; SUMMARY; CHAPTER 7 Prior Beliefs and Asset Pricing Models; PRIOR BELIEFS AND ASSET PRICING MODELS; MODEL UNCERTAINTY; SUMMARY; APPENDIX A: NUMERICAL SIMULATION OF THE PREDICTIVE DISTRIBUTION APPENDIX B: LIKELIHOOD FUNCTION OF A CANDIDATE MODELCHAPTER 8 The Black-Litterman Portfolio Selection Framework; PRELIMINARIES; COMBINING MARKET EQUILIBRIUM AND INVESTOR VIEWS; THE CHOICE OF τ AND ω; THE OPTIMAL PORTFOLIO ALLOCATION; INCORPORATING TRADING STRATEGIES INTO THE BLACK-LITTERMAN MODEL; ACTIVE PORTFOLIO MANAGEMENT AND THE BLACK-LITTERMAN MODEL; COVARIANCE MATRIX ESTIMATION; SUMMARY; CHAPTER 9 Market Efficiency and Return Predictability; TESTS OF MEAN-VARIANCE EFFICIENCY; INEFFICIENCY MEASURES IN TESTING THE CAPM; TESTING THE APT; RETURN PREDICTABILITY ILLUSTRATION: PREDICTABILITY AND THE INVESTMENT HORIZONSUMMARY; APPENDIX: VECTOR AUTOREGRESSIVE SETUP; CHAPTER 10 Volatility Models; GARCH MODELS OF VOLATILITY; STOCHASTIC VOLATILITY MODELS; ILLUSTRATION: FORECASTING VALUE-AT-RISK; AN ARCH-TYPE MODEL OR A STOCHASTIC VOLATILITY MODEL?; WHERE DO BAYESIAN METHODS FIT?; CHAPTER 11 Bayesian Estimation of ARCH-Type Volatility Models; BAYESIAN ESTIMATION OF THE SIMPLE GARCH(1,1) MODEL; MARKOV REGIME-SWITCHING GARCH MODELS; SUMMARY; APPENDIX: GRIDDY GIBBS SAMPLER; CHAPTER 12 Bayesian Estimation of Stochastic Volatility Models PRELIMINARIES OF SV MODEL ESTIMATIONTHE SINGLE-MOVE MCMC ALGORITHM FOR SV MODEL ESTIMATION; THE MULTIMOVE MCMC ALGORITHM FOR SV MODEL ESTIMATION; JUMP EXTENSION OF THE SIMPLE SV MODEL; VOLATILITY FORECASTING AND RETURN PREDICTION; SUMMARY; APPENDIX: KALMAN FILTERING AND SMOOTHING; CHAPTER 13 Advanced Techniques for Bayesian Portfolio Selection; DISTRIBUTIONAL RETURN ASSUMPTIONS ALTERNATIVE TO NORMALITY; PORTFOLIO SELECTION IN THE SETTING OF NONNORMALITY: PRELIMINARIES; MAXIMIZATION OF UTILITY WITH HIGHER MOMENTS; EXTENDING THE BLACK-LITTERMAN APPROACH: COPULA OPINION POOLING EXTENDING THE BLACK-LITTERMAN APPROACH: STABLE DISTRIBUTION |
| Record Nr. | UNINA-9910145695803321 |
| Hoboken, N.J., : Wiley, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian methods in finance [[electronic resource] /] / Svetlozar T. Rachev ... [et al.]
| Bayesian methods in finance [[electronic resource] /] / Svetlozar T. Rachev ... [et al.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2008 |
| Descrizione fisica | 1 online resource (351 p.) |
| Disciplina |
332
332.01519542 |
| Altri autori (Persone) | RachevS. T (Svetlozar Todorov) |
| Collana | The Frank J. Fabozzi series |
| Soggetto topico |
Finance - Mathematical models
Bayesian statistical decision theory |
| ISBN |
1-119-20214-0
1-281-21726-3 9786611217266 0-470-24924-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Methods in Finance; Contents; Preface; About the Authors; CHAPTER 1 Introduction; A FEW NOTES ON NOTATION; OVERVIEW; CHAPTER 2 The Bayesian Paradigm; THE LIKELIHOOD FUNCTION; THE BAYES' THEOREM; SUMMARY; CHAPTER 3 Prior and Posterior Information, Predictive Inference; PRIOR INFORMATION; POSTERIOR INFERENCE; BAYESIAN PREDICTIVE INFERENCE; ILLUSTRATION: POSTERIOR TRADE-OFF AND THENORMAL MEAN PARAMETER; SUMMARY; APPENDIX: DEFINITIONS OF SOME UNIVARIATE AND MULTIVARIATE STATISTICAL DISTRIBUTIONS; CHAPTER 4 Bayesian Linear Regression Model; THE UNIVARIATE LINEAR REGRESSION MODEL
THE MULTIVARIATE LINEAR REGRESSION MODELSUMMARY; CHAPTER 5 Bayesian Numerical Computation; MONTE CARLO INTEGRATION; ALGORITHMS FOR POSTERIOR SIMULATION; APPROXIMATION METHODS: LOGISTIC REGRESSION; SUMMARY; CHAPTER 6 Bayesian Framework for Portfolio Allocation; CLASSICAL PORTFOLIO SELECTION; BAYESIAN PORTFOLIO SELECTION; SHRINKAGE ESTIMATORS; UNEQUAL HISTORIES OF RETURNS; SUMMARY; CHAPTER 7 Prior Beliefs and Asset Pricing Models; PRIOR BELIEFS AND ASSET PRICING MODELS; MODEL UNCERTAINTY; SUMMARY; APPENDIX A: NUMERICAL SIMULATION OF THE PREDICTIVE DISTRIBUTION APPENDIX B: LIKELIHOOD FUNCTION OF A CANDIDATE MODELCHAPTER 8 The Black-Litterman Portfolio Selection Framework; PRELIMINARIES; COMBINING MARKET EQUILIBRIUM AND INVESTOR VIEWS; THE CHOICE OF τ AND ω; THE OPTIMAL PORTFOLIO ALLOCATION; INCORPORATING TRADING STRATEGIES INTO THE BLACK-LITTERMAN MODEL; ACTIVE PORTFOLIO MANAGEMENT AND THE BLACK-LITTERMAN MODEL; COVARIANCE MATRIX ESTIMATION; SUMMARY; CHAPTER 9 Market Efficiency and Return Predictability; TESTS OF MEAN-VARIANCE EFFICIENCY; INEFFICIENCY MEASURES IN TESTING THE CAPM; TESTING THE APT; RETURN PREDICTABILITY ILLUSTRATION: PREDICTABILITY AND THE INVESTMENT HORIZONSUMMARY; APPENDIX: VECTOR AUTOREGRESSIVE SETUP; CHAPTER 10 Volatility Models; GARCH MODELS OF VOLATILITY; STOCHASTIC VOLATILITY MODELS; ILLUSTRATION: FORECASTING VALUE-AT-RISK; AN ARCH-TYPE MODEL OR A STOCHASTIC VOLATILITY MODEL?; WHERE DO BAYESIAN METHODS FIT?; CHAPTER 11 Bayesian Estimation of ARCH-Type Volatility Models; BAYESIAN ESTIMATION OF THE SIMPLE GARCH(1,1) MODEL; MARKOV REGIME-SWITCHING GARCH MODELS; SUMMARY; APPENDIX: GRIDDY GIBBS SAMPLER; CHAPTER 12 Bayesian Estimation of Stochastic Volatility Models PRELIMINARIES OF SV MODEL ESTIMATIONTHE SINGLE-MOVE MCMC ALGORITHM FOR SV MODEL ESTIMATION; THE MULTIMOVE MCMC ALGORITHM FOR SV MODEL ESTIMATION; JUMP EXTENSION OF THE SIMPLE SV MODEL; VOLATILITY FORECASTING AND RETURN PREDICTION; SUMMARY; APPENDIX: KALMAN FILTERING AND SMOOTHING; CHAPTER 13 Advanced Techniques for Bayesian Portfolio Selection; DISTRIBUTIONAL RETURN ASSUMPTIONS ALTERNATIVE TO NORMALITY; PORTFOLIO SELECTION IN THE SETTING OF NONNORMALITY: PRELIMINARIES; MAXIMIZATION OF UTILITY WITH HIGHER MOMENTS; EXTENDING THE BLACK-LITTERMAN APPROACH: COPULA OPINION POOLING EXTENDING THE BLACK-LITTERMAN APPROACH: STABLE DISTRIBUTION |
| Record Nr. | UNINA-9910817059303321 |
| Hoboken, N.J., : Wiley, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian risk management : a guide to model risk and sequential learning in financial markets / / Matt Sekerke
| Bayesian risk management : a guide to model risk and sequential learning in financial markets / / Matt Sekerke |
| Autore | Sekerke Matt |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (238 p.) |
| Disciplina | 332/.041501519542 |
| Collana | Wiley Finance Series |
| Soggetto topico |
Finance - Mathematical models
Financial risk management - Mathematical models Bayesian statistical decision theory |
| ISBN |
1-118-74750-X
1-118-86478-6 1-118-74745-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Preface; Acknowledgments; Chapter 1 Models for Discontinuous Markets; Risk Models and Model Risk; Time-Invariant Models and Crisis; Ergodic Stationarity in Classical Time Series Analysis; Recalibration Does Not Overcome the Limits of a Time-Invariant Model; Bayesian Probability as a Means of Handling Discontinuity; Accounting for Parameter and Model Uncertainty; Responding to Changes in the Market Environment; Time-Invariance and Objectivity; Part 1 Capturing Uncertainty in Statistical Models
Chapter 2 Prior Knowledge, Parameter Uncertainty, and EstimationEstimation with Prior Knowledge: The Beta-Bernoulli Model; Encoding Prior Knowledge in the Beta-Bernoulli Model; Impact of the Prior on the Posterior Distribution; Shrinkage and Bias; Efficiency; Hyperparameters and Sufficient Statistics; Conjugate Prior Families; Prior Parameter Distributions as Hypotheses: The Normal Linear Regression Model; Classical Analysis of the Normal Linear Regression Model; Estimation; Hypothesis Testing; Bayesian Analysis of the Normal Linear Regression Model Hypothesis Testing with Parameter DistributionsComparison; Decisions after Observing the Data: The Choice of Estimators; Decisions and Loss; Loss and Prior Information; Chapter 3 Model Uncertainty; Bayesian Model Comparison; Bayes Factors; Marginal Likelihoods; Parsimony; Bayes Factors versus Information Criteria; Bayes Factors versus Likelihood Ratios; Models as Nuisance Parameters; The Space of Models; Mixtures of Models; Uncertainty in Pricing Models; Front-Office Models; The Statistical Nature of Front-Office Models; A Note on Backtesting Part 2 Sequential Learning with Adaptive Statistical ModelsChapter 4 Introduction to Sequential Modeling; Sequential Bayesian Inference; Achieving Adaptivity via Discounting; Discounting in the Beta-Bernoulli Model; Discounting in the Linear Regression Model; Comparison with the Time-Invariant Case; Accounting for Uncertainty in Sequential Models; Chapter 5 Bayesian Inference in State-Space Time Series Models; State Space Models of Time Series; The Filtering Problem; The Smoothing Problem; Dynamic Linear Models; General Form; Polynomial Trend Components; Seasonal Components Regression ComponentsBuilding DLMs with Components; Recursive Relationships in the DLM; Filtering Recursion; Smoothing Recursion; Predictive Distributions and Forecasting; Variance Estimation; Univariate Case; Multivariate Case; Sequential Model Comparison; Chapter 6 Sequential Monte Carlo Inference; Nonlinear and Non-Normal Models; Gibbs Sampling; Forward-Filtering Backward-Sampling; State Learning with Particle Filters; The Particle Set; A First Particle Filter: The Bootstrap Filter; The Auxiliary Particle Filter; Joint Learning of Parameters and States; The Liu-West Filter Improving Efficiency with Sufficient Statistics |
| Record Nr. | UNINA-9910131451603321 |
|
Sekerke Matt
|
||
| Hoboken, New Jersey : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian risk management : a guide to model risk and sequential learning in financial markets / / Matt Sekerke
| Bayesian risk management : a guide to model risk and sequential learning in financial markets / / Matt Sekerke |
| Autore | Sekerke Matt |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (238 p.) |
| Disciplina | 332/.041501519542 |
| Collana | Wiley Finance Series |
| Soggetto topico |
Finance - Mathematical models
Financial risk management - Mathematical models Bayesian statistical decision theory |
| ISBN |
1-118-74750-X
1-118-86478-6 1-118-74745-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Preface; Acknowledgments; Chapter 1 Models for Discontinuous Markets; Risk Models and Model Risk; Time-Invariant Models and Crisis; Ergodic Stationarity in Classical Time Series Analysis; Recalibration Does Not Overcome the Limits of a Time-Invariant Model; Bayesian Probability as a Means of Handling Discontinuity; Accounting for Parameter and Model Uncertainty; Responding to Changes in the Market Environment; Time-Invariance and Objectivity; Part 1 Capturing Uncertainty in Statistical Models
Chapter 2 Prior Knowledge, Parameter Uncertainty, and EstimationEstimation with Prior Knowledge: The Beta-Bernoulli Model; Encoding Prior Knowledge in the Beta-Bernoulli Model; Impact of the Prior on the Posterior Distribution; Shrinkage and Bias; Efficiency; Hyperparameters and Sufficient Statistics; Conjugate Prior Families; Prior Parameter Distributions as Hypotheses: The Normal Linear Regression Model; Classical Analysis of the Normal Linear Regression Model; Estimation; Hypothesis Testing; Bayesian Analysis of the Normal Linear Regression Model Hypothesis Testing with Parameter DistributionsComparison; Decisions after Observing the Data: The Choice of Estimators; Decisions and Loss; Loss and Prior Information; Chapter 3 Model Uncertainty; Bayesian Model Comparison; Bayes Factors; Marginal Likelihoods; Parsimony; Bayes Factors versus Information Criteria; Bayes Factors versus Likelihood Ratios; Models as Nuisance Parameters; The Space of Models; Mixtures of Models; Uncertainty in Pricing Models; Front-Office Models; The Statistical Nature of Front-Office Models; A Note on Backtesting Part 2 Sequential Learning with Adaptive Statistical ModelsChapter 4 Introduction to Sequential Modeling; Sequential Bayesian Inference; Achieving Adaptivity via Discounting; Discounting in the Beta-Bernoulli Model; Discounting in the Linear Regression Model; Comparison with the Time-Invariant Case; Accounting for Uncertainty in Sequential Models; Chapter 5 Bayesian Inference in State-Space Time Series Models; State Space Models of Time Series; The Filtering Problem; The Smoothing Problem; Dynamic Linear Models; General Form; Polynomial Trend Components; Seasonal Components Regression ComponentsBuilding DLMs with Components; Recursive Relationships in the DLM; Filtering Recursion; Smoothing Recursion; Predictive Distributions and Forecasting; Variance Estimation; Univariate Case; Multivariate Case; Sequential Model Comparison; Chapter 6 Sequential Monte Carlo Inference; Nonlinear and Non-Normal Models; Gibbs Sampling; Forward-Filtering Backward-Sampling; State Learning with Particle Filters; The Particle Set; A First Particle Filter: The Bootstrap Filter; The Auxiliary Particle Filter; Joint Learning of Parameters and States; The Liu-West Filter Improving Efficiency with Sufficient Statistics |
| Record Nr. | UNINA-9910810481303321 |
|
Sekerke Matt
|
||
| Hoboken, New Jersey : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Building automated trading systems [[electronic resource] ] : with an introduction to Visual C++.NET 2005 / / Benjamin Van Vliet
| Building automated trading systems [[electronic resource] ] : with an introduction to Visual C++.NET 2005 / / Benjamin Van Vliet |
| Autore | Van Vliet Benjamin |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Elsevier/Academic Press, c2007 |
| Descrizione fisica | 1 online resource (331 p.) |
| Disciplina | 332.64/20285513 |
| Collana | The financial market technology series |
| Soggetto topico |
Electronic trading of securities
Finance - Mathematical models Microsoft .NET |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-04897-6
9786611048976 0-08-047625-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Building Automated Trading Systems: With an Introduction to Visual C++.NET 2005; Copyright Page; Table of Contents; Acknowledgments; CHAPTER 1. Introduction; 1.1. ISO C++; 1.2. Structure of This Book; Section I: Introduction to Visual C++.NET 2005; CHAPTER 2. The .NET Framework; 2.1. MS Visual Studio 2005 Project Structure; 2.2. What is C++/CLI?; 2.3. Why Visual C++.NET?; 2.4. The VC++.NET Compiler; 2.5. What About Speed?; 2.6. The .NET Framework; 2.7. Sample Code: MessageBox_Example; 2.8. Sample Code: StringConcat_Example; 2.9. Sample Code: Debug_Example; 2.10. Versioning
2.11. SummaryCHAPTER 3. Tracking References; 3.1. Sample Code: TrackingReference_Example; 3.2. Sample Code: TemplateFunction_Example; 3.3. ^Managed Handle; 3.4. Sample Code: Ref Type_Example; 3.5. Summary; CHAPTER 4. Classes and Objects; 4.1. Abstraction; 4.2. Encapsulation; 4.3. Inheritance; 4.4. Polymorphism; 4.5. Memory Management in .NET; 4.6. .NET Types; 4.7. Unmanaged Types; 4.8. Mixed Assemblies; 4.9. Summary; CHAPTER 5. Reference Types; 5.1. Sample Code: Ref Type_Example; 5.2. Delete and Dispose; 5.3. Finalize; 5.4. Sample Code: Finalize_Example; 5.5. Stack Semantics for Ref Types 5.6. Nullptr Reference5.7. This is Important; 5.8. Summary; CHAPTER 6. Value Types; 6.1. Sample Code: ValueTypes_Example; 6.2. Sample Code: PassingValueTypes_Example; 6.3. Summary; CHAPTER 7. Unmanaged Objects; 7.1. Sample Code: UnmanagedObject_Example; 7.2. Summary; CHAPTER 8. Composition; 8.1. Sample Code: Composition_Example; 8.2. Sample Code: UnmanagedComposition_Example; 8.3. Sample Code: ManagedComposition_Example; 8.4. Summary; CHAPTER 9. Properties; 9.1. Sample Code: Properties_Example; 9.2. Summary; CHAPTER 10. Structures and Enumerations; 10.1. Sample Code: ValueStructure_Example 10.2. Sample Code: ReferenceStructure_Example10.3. Sample Code: Enums_Example; 10.4. Summary; CHAPTER 11. Inheritance; 11.1. Access Modifiers; 11.2. Object Class; 11.3. Abstract and Sealed Classes; 11.4. Sample Code: Inheritance_Example; 11.5. Interfaces; 11.6. Sample Code: Interface_Example; 11.7. Runtime Callable Wrapper; 11.8. Summary; CHAPTER 12. Converting and Casting; 12.1. Converting; 12.2. Sample Code: Convert_Example; 12.3. Static Casting; 12.4. Sample Code: StaticCast_Example; 12.5. Dynamic Casting; 12.6. Sample Code: DynamicCast_Example; 12.7. Safe Casting 12.8. Sample Code: SafeCast_Example12.9. Summary; CHAPTER 13. Operator Overloading; 13.1. Sample Code: OpOverload_Example; 13.2. Summary; CHAPTER 14. Delegates and Events; 14.1. Delegates; 14.2. Sample Code: Delegates_Example; 14.3. Multicasting; 14.4. Sample Code: Multicast_Example; 14.5. Events; 14.6. Sample Code: Event_Example; 14.7. Wrappers; 14.8. Sample Code: Wrapper_Example; 14.9. Asynchronous Method Calls; 14.10. Sample Code: AsynchEvent_Example; 14.11. Summary; CHAPTER 15. Arrays; 15.1. Sample Code: ManagedArray_Example; 15.2. Sample Code: PassingArrays_Example; 15.3. Summary CHAPTER 16. Generating Random Numbers |
| Record Nr. | UNINA-9910457685403321 |
|
Van Vliet Benjamin
|
||
| Amsterdam ; ; Boston, : Elsevier/Academic Press, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Building automated trading systems [[electronic resource] ] : with an introduction to Visual C++.NET 2005 / / Benjamin Van Vliet
| Building automated trading systems [[electronic resource] ] : with an introduction to Visual C++.NET 2005 / / Benjamin Van Vliet |
| Autore | Van Vliet Benjamin |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Elsevier/Academic Press, c2007 |
| Descrizione fisica | 1 online resource (331 p.) |
| Disciplina | 332.64/20285513 |
| Collana | The financial market technology series |
| Soggetto topico |
Electronic trading of securities
Finance - Mathematical models Microsoft .NET |
| ISBN |
1-281-04897-6
9786611048976 0-08-047625-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Building Automated Trading Systems: With an Introduction to Visual C++.NET 2005; Copyright Page; Table of Contents; Acknowledgments; CHAPTER 1. Introduction; 1.1. ISO C++; 1.2. Structure of This Book; Section I: Introduction to Visual C++.NET 2005; CHAPTER 2. The .NET Framework; 2.1. MS Visual Studio 2005 Project Structure; 2.2. What is C++/CLI?; 2.3. Why Visual C++.NET?; 2.4. The VC++.NET Compiler; 2.5. What About Speed?; 2.6. The .NET Framework; 2.7. Sample Code: MessageBox_Example; 2.8. Sample Code: StringConcat_Example; 2.9. Sample Code: Debug_Example; 2.10. Versioning
2.11. SummaryCHAPTER 3. Tracking References; 3.1. Sample Code: TrackingReference_Example; 3.2. Sample Code: TemplateFunction_Example; 3.3. ^Managed Handle; 3.4. Sample Code: Ref Type_Example; 3.5. Summary; CHAPTER 4. Classes and Objects; 4.1. Abstraction; 4.2. Encapsulation; 4.3. Inheritance; 4.4. Polymorphism; 4.5. Memory Management in .NET; 4.6. .NET Types; 4.7. Unmanaged Types; 4.8. Mixed Assemblies; 4.9. Summary; CHAPTER 5. Reference Types; 5.1. Sample Code: Ref Type_Example; 5.2. Delete and Dispose; 5.3. Finalize; 5.4. Sample Code: Finalize_Example; 5.5. Stack Semantics for Ref Types 5.6. Nullptr Reference5.7. This is Important; 5.8. Summary; CHAPTER 6. Value Types; 6.1. Sample Code: ValueTypes_Example; 6.2. Sample Code: PassingValueTypes_Example; 6.3. Summary; CHAPTER 7. Unmanaged Objects; 7.1. Sample Code: UnmanagedObject_Example; 7.2. Summary; CHAPTER 8. Composition; 8.1. Sample Code: Composition_Example; 8.2. Sample Code: UnmanagedComposition_Example; 8.3. Sample Code: ManagedComposition_Example; 8.4. Summary; CHAPTER 9. Properties; 9.1. Sample Code: Properties_Example; 9.2. Summary; CHAPTER 10. Structures and Enumerations; 10.1. Sample Code: ValueStructure_Example 10.2. Sample Code: ReferenceStructure_Example10.3. Sample Code: Enums_Example; 10.4. Summary; CHAPTER 11. Inheritance; 11.1. Access Modifiers; 11.2. Object Class; 11.3. Abstract and Sealed Classes; 11.4. Sample Code: Inheritance_Example; 11.5. Interfaces; 11.6. Sample Code: Interface_Example; 11.7. Runtime Callable Wrapper; 11.8. Summary; CHAPTER 12. Converting and Casting; 12.1. Converting; 12.2. Sample Code: Convert_Example; 12.3. Static Casting; 12.4. Sample Code: StaticCast_Example; 12.5. Dynamic Casting; 12.6. Sample Code: DynamicCast_Example; 12.7. Safe Casting 12.8. Sample Code: SafeCast_Example12.9. Summary; CHAPTER 13. Operator Overloading; 13.1. Sample Code: OpOverload_Example; 13.2. Summary; CHAPTER 14. Delegates and Events; 14.1. Delegates; 14.2. Sample Code: Delegates_Example; 14.3. Multicasting; 14.4. Sample Code: Multicast_Example; 14.5. Events; 14.6. Sample Code: Event_Example; 14.7. Wrappers; 14.8. Sample Code: Wrapper_Example; 14.9. Asynchronous Method Calls; 14.10. Sample Code: AsynchEvent_Example; 14.11. Summary; CHAPTER 15. Arrays; 15.1. Sample Code: ManagedArray_Example; 15.2. Sample Code: PassingArrays_Example; 15.3. Summary CHAPTER 16. Generating Random Numbers |
| Record Nr. | UNINA-9910784350403321 |
|
Van Vliet Benjamin
|
||
| Amsterdam ; ; Boston, : Elsevier/Academic Press, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Building automated trading systems : with an introduction to Visual C++.NET 2005 / / Benjamin Van Vliet
| Building automated trading systems : with an introduction to Visual C++.NET 2005 / / Benjamin Van Vliet |
| Autore | Van Vliet Benjamin |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Elsevier/Academic Press, c2007 |
| Descrizione fisica | 1 online resource (331 p.) |
| Disciplina |
332.64/20285513
332.6420285513 |
| Collana | The financial market technology series |
| Soggetto topico |
Electronic trading of securities
Finance - Mathematical models Microsoft .NET Framework |
| ISBN |
1-281-04897-6
9786611048976 0-08-047625-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Building Automated Trading Systems: With an Introduction to Visual C++.NET 2005; Copyright Page; Table of Contents; Acknowledgments; CHAPTER 1. Introduction; 1.1. ISO C++; 1.2. Structure of This Book; Section I: Introduction to Visual C++.NET 2005; CHAPTER 2. The .NET Framework; 2.1. MS Visual Studio 2005 Project Structure; 2.2. What is C++/CLI?; 2.3. Why Visual C++.NET?; 2.4. The VC++.NET Compiler; 2.5. What About Speed?; 2.6. The .NET Framework; 2.7. Sample Code: MessageBox_Example; 2.8. Sample Code: StringConcat_Example; 2.9. Sample Code: Debug_Example; 2.10. Versioning
2.11. SummaryCHAPTER 3. Tracking References; 3.1. Sample Code: TrackingReference_Example; 3.2. Sample Code: TemplateFunction_Example; 3.3. ^Managed Handle; 3.4. Sample Code: Ref Type_Example; 3.5. Summary; CHAPTER 4. Classes and Objects; 4.1. Abstraction; 4.2. Encapsulation; 4.3. Inheritance; 4.4. Polymorphism; 4.5. Memory Management in .NET; 4.6. .NET Types; 4.7. Unmanaged Types; 4.8. Mixed Assemblies; 4.9. Summary; CHAPTER 5. Reference Types; 5.1. Sample Code: Ref Type_Example; 5.2. Delete and Dispose; 5.3. Finalize; 5.4. Sample Code: Finalize_Example; 5.5. Stack Semantics for Ref Types 5.6. Nullptr Reference5.7. This is Important; 5.8. Summary; CHAPTER 6. Value Types; 6.1. Sample Code: ValueTypes_Example; 6.2. Sample Code: PassingValueTypes_Example; 6.3. Summary; CHAPTER 7. Unmanaged Objects; 7.1. Sample Code: UnmanagedObject_Example; 7.2. Summary; CHAPTER 8. Composition; 8.1. Sample Code: Composition_Example; 8.2. Sample Code: UnmanagedComposition_Example; 8.3. Sample Code: ManagedComposition_Example; 8.4. Summary; CHAPTER 9. Properties; 9.1. Sample Code: Properties_Example; 9.2. Summary; CHAPTER 10. Structures and Enumerations; 10.1. Sample Code: ValueStructure_Example 10.2. Sample Code: ReferenceStructure_Example10.3. Sample Code: Enums_Example; 10.4. Summary; CHAPTER 11. Inheritance; 11.1. Access Modifiers; 11.2. Object Class; 11.3. Abstract and Sealed Classes; 11.4. Sample Code: Inheritance_Example; 11.5. Interfaces; 11.6. Sample Code: Interface_Example; 11.7. Runtime Callable Wrapper; 11.8. Summary; CHAPTER 12. Converting and Casting; 12.1. Converting; 12.2. Sample Code: Convert_Example; 12.3. Static Casting; 12.4. Sample Code: StaticCast_Example; 12.5. Dynamic Casting; 12.6. Sample Code: DynamicCast_Example; 12.7. Safe Casting 12.8. Sample Code: SafeCast_Example12.9. Summary; CHAPTER 13. Operator Overloading; 13.1. Sample Code: OpOverload_Example; 13.2. Summary; CHAPTER 14. Delegates and Events; 14.1. Delegates; 14.2. Sample Code: Delegates_Example; 14.3. Multicasting; 14.4. Sample Code: Multicast_Example; 14.5. Events; 14.6. Sample Code: Event_Example; 14.7. Wrappers; 14.8. Sample Code: Wrapper_Example; 14.9. Asynchronous Method Calls; 14.10. Sample Code: AsynchEvent_Example; 14.11. Summary; CHAPTER 15. Arrays; 15.1. Sample Code: ManagedArray_Example; 15.2. Sample Code: PassingArrays_Example; 15.3. Summary CHAPTER 16. Generating Random Numbers |
| Record Nr. | UNINA-9910817836303321 |
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Van Vliet Benjamin
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| Amsterdam ; ; Boston, : Elsevier/Academic Press, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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C# for financial markets / / Daniel J. Duffy and Andrea Germani
| C# for financial markets / / Daniel J. Duffy and Andrea Germani |
| Autore | Duffy Daniel J |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Chichester, : John Wiley & Sons, 2013 |
| Descrizione fisica | 1 online resource (xxii, 831 pages) : illustrations |
| Disciplina | 332.0285/5133 |
| Collana | Wiley finance |
| Soggetto topico |
Finance - Mathematical models
Finance - Data processing C# (Computer program language) |
| ISBN |
1-118-81857-1
1-299-18856-7 1-118-50281-7 1-118-50283-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
C# for Financial Markets; Contents; List of Figures; List of Tables; Introduction; 0.1 What Is This Book?; 0.2 Special Features in This Book; 0.3 Who Is This Book for and What Do You Learn?; 0.4 Structure of This Book; 0.5 C# Source Code; 1 Global Overview of the Book; 1.1 Introduction and Objectives; 1.2 Comparing C# and C++; 1.3 Using This Book; 2 C# Fundamentals; 2.1 Introduction and Objectives; 2.2 Background to C#; 2.3 Value Types, Reference Types and Memory Management; 2.4 Built-in Data Types in C#; 2.5 Character and String Types; 2.6 Operators; 2.7 Console Input and Output
2.8 User-defined Structs2.9 Mini Application: Option Pricing; 2.10 Summary and Conclusions; 2.11 Exercises and Projects; 3 Classes in C#; 3.1 Introduction and Objectives; 3.2 The Structure of a Class: Methods and Data; 3.3 The Keyword 'this'; 3.4 Properties; 3.5 Class Variables and Class Methods; 3.6 Creating and Using Objects in C#; 3.7 Example: European Option Price and Sensitivities; 3.7.1 Supporting Mathematical Functions; 3.7.2 Black-Scholes Formula; 3.7.3 C# Implementation; 3.7.4 Examples and Applications; 3.8 Enumeration Types; 3.9 Extension Methods 3.10 An Introduction to Inheritance in C#3.11 Example: Two-factor Payoff Hierarchies and Interfaces; 3.12 Exception Handling; 3.13 Summary and Conclusions; 3.14 Exercises and Projects; 4 Classes and C# Advanced Features; 4.1 Introduction and Objectives; 4.2 Interfaces; 4.3 Using Interfaces: Vasicek and Cox-Ingersoll-Ross (CIR) Bond and Option Pricing; 4.3.1 Defining Standard Interfaces; 4.3.2 Bond Models and Stochastic Differential Equations; 4.3.3 Option Pricing and the Visitor Pattern; 4.4 Interfaces in .NET and Some Advanced Features; 4.4.1 Copying Objects; 4.4.2 Interfaces and Properties 4.4.3 Comparing Abstract Classes and Interfaces4.4.4 Explicit Interfaces; 4.4.5 Casting an Object to an Interface; 4.5 Combining Interfaces, Inheritance and Composition; 4.5.1 Design Philosophy: Modular Programming; 4.5.2 A Model Problem and Interfacing; 4.5.3 Implementing the Interfaces; 4.5.4 Examples and Testing; 4.6 Introduction to Delegates and Lambda Functions; 4.6.1 Comparing Delegates and Interfaces; 4.7 Lambda Functions and Anonymous Methods; 4.8 Other Features in C#; 4.8.1 Static Constructors; 4.8.2 Finalisers; 4.8.3 Casting; 4.8.4 The var Keyword; 4.9 Advanced .NET Delegates 4.9.1 Provides and Requires Interfaces: Creating Plug-in Methods with Delegates4.9.2 Multicast Delegates; 4.9.3 Generic Delegate Types; 4.9.4 Delegates versus Interfaces, Again; 4.10 The Standard Event Pattern in .NET and the Observer Pattern; 4.11 Summary and Conclusions; 4.12 Exercises and Projects; 5 Data Structures and Collections; 5.1 Introduction and Objectives; 5.2 Arrays; 5.2.1 Rectangular and Jagged Arrays; 5.2.2 Bounds Checking; 5.3 Dates, Times and Time Zones; 5.3.1 Creating and Modifying Dates; 5.3.2 Formatting and Parsing Dates; 5.3.3 Working with Dates 5.4 Enumeration and Iterators |
| Record Nr. | UNINA-9910141502803321 |
|
Duffy Daniel J
|
||
| Chichester, : John Wiley & Sons, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
C# for financial markets / / Daniel J. Duffy and Andrea Germani
| C# for financial markets / / Daniel J. Duffy and Andrea Germani |
| Autore | Duffy Daniel J |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Chichester, : John Wiley & Sons, 2013 |
| Descrizione fisica | 1 online resource (xxii, 831 pages) : illustrations |
| Disciplina | 332.0285/5133 |
| Collana | Wiley finance |
| Soggetto topico |
Finance - Mathematical models
Finance - Data processing C# (Computer program language) |
| ISBN |
1-118-81857-1
1-299-18856-7 1-118-50281-7 1-118-50283-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
C# for Financial Markets; Contents; List of Figures; List of Tables; Introduction; 0.1 What Is This Book?; 0.2 Special Features in This Book; 0.3 Who Is This Book for and What Do You Learn?; 0.4 Structure of This Book; 0.5 C# Source Code; 1 Global Overview of the Book; 1.1 Introduction and Objectives; 1.2 Comparing C# and C++; 1.3 Using This Book; 2 C# Fundamentals; 2.1 Introduction and Objectives; 2.2 Background to C#; 2.3 Value Types, Reference Types and Memory Management; 2.4 Built-in Data Types in C#; 2.5 Character and String Types; 2.6 Operators; 2.7 Console Input and Output
2.8 User-defined Structs2.9 Mini Application: Option Pricing; 2.10 Summary and Conclusions; 2.11 Exercises and Projects; 3 Classes in C#; 3.1 Introduction and Objectives; 3.2 The Structure of a Class: Methods and Data; 3.3 The Keyword 'this'; 3.4 Properties; 3.5 Class Variables and Class Methods; 3.6 Creating and Using Objects in C#; 3.7 Example: European Option Price and Sensitivities; 3.7.1 Supporting Mathematical Functions; 3.7.2 Black-Scholes Formula; 3.7.3 C# Implementation; 3.7.4 Examples and Applications; 3.8 Enumeration Types; 3.9 Extension Methods 3.10 An Introduction to Inheritance in C#3.11 Example: Two-factor Payoff Hierarchies and Interfaces; 3.12 Exception Handling; 3.13 Summary and Conclusions; 3.14 Exercises and Projects; 4 Classes and C# Advanced Features; 4.1 Introduction and Objectives; 4.2 Interfaces; 4.3 Using Interfaces: Vasicek and Cox-Ingersoll-Ross (CIR) Bond and Option Pricing; 4.3.1 Defining Standard Interfaces; 4.3.2 Bond Models and Stochastic Differential Equations; 4.3.3 Option Pricing and the Visitor Pattern; 4.4 Interfaces in .NET and Some Advanced Features; 4.4.1 Copying Objects; 4.4.2 Interfaces and Properties 4.4.3 Comparing Abstract Classes and Interfaces4.4.4 Explicit Interfaces; 4.4.5 Casting an Object to an Interface; 4.5 Combining Interfaces, Inheritance and Composition; 4.5.1 Design Philosophy: Modular Programming; 4.5.2 A Model Problem and Interfacing; 4.5.3 Implementing the Interfaces; 4.5.4 Examples and Testing; 4.6 Introduction to Delegates and Lambda Functions; 4.6.1 Comparing Delegates and Interfaces; 4.7 Lambda Functions and Anonymous Methods; 4.8 Other Features in C#; 4.8.1 Static Constructors; 4.8.2 Finalisers; 4.8.3 Casting; 4.8.4 The var Keyword; 4.9 Advanced .NET Delegates 4.9.1 Provides and Requires Interfaces: Creating Plug-in Methods with Delegates4.9.2 Multicast Delegates; 4.9.3 Generic Delegate Types; 4.9.4 Delegates versus Interfaces, Again; 4.10 The Standard Event Pattern in .NET and the Observer Pattern; 4.11 Summary and Conclusions; 4.12 Exercises and Projects; 5 Data Structures and Collections; 5.1 Introduction and Objectives; 5.2 Arrays; 5.2.1 Rectangular and Jagged Arrays; 5.2.2 Bounds Checking; 5.3 Dates, Times and Time Zones; 5.3.1 Creating and Modifying Dates; 5.3.2 Formatting and Parsing Dates; 5.3.3 Working with Dates 5.4 Enumeration and Iterators |
| Record Nr. | UNINA-9910817063103321 |
|
Duffy Daniel J
|
||
| Chichester, : John Wiley & Sons, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
