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Advances in portfolio construction and implementation [[electronic resource] /] / edited by Stephen Satchell, Alan Scowcroft
| Advances in portfolio construction and implementation [[electronic resource] /] / edited by Stephen Satchell, Alan Scowcroft |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Amsterdam ; ; Oxford, : Butterworth-Heinemann, 2003 |
| Descrizione fisica | 1 online resource (384 p.) |
| Disciplina | 332.6 |
| Altri autori (Persone) |
SatchellS (Stephen)
ScowcroftAlan |
| Collana | Butterworth-Heinemann finance |
| Soggetto topico |
Portfolio management
Portfolio management - Mathematical models Investments |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-96633-5
9786610966332 1-4175-0763-2 0-08-047184-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Advances in Portfolio Construction and Implementation; Copyright Page; Contents; List of Contributors; Introduction; Chapter 1. A review of portfolio planning: models and systems; 1.1 Introduction and Overview; 1.2 Alternative Computational Models; 1.3 Symmetric and Asymmetric Measures of Risk; 1.4 Computational Models in Practice; 1.5 Preparation of Data: Financial Data Marts; 1.6 Solution Methods; 1.7 Computational Experience; 1.8 Discussions and Conclusions; 1.9 Appendix 1: Piecewise Linear Approximation of the Quadratic Form
1.10 Appendix 2: Comparative Computational Views of the Alternative ModelsReferences; Web References; Acknowledgements; Chapter 2. Generalized mean-variance analysis and robust portfolio diversification; 2.1 Introduction; 2.2 Generalized Mean-Variance Analysis; 2.3 The State Preference Theory Approach to Portfolio Construction; 2.4 Implementation and Simulation; 2.5 Conclusions and Suggested Further Work; References; Chapter 3. Portfolio construction from mandate to stock weight: a practitioner's perspective; 3.1 Introduction; 3.2 Allocating Tracking Error for Multiple Portfolio Funds 3.3 Tracking Errors for Arbitrary Portfolios3.4 Active CAPM, or How Far Should a Bet be Taken?; 3.5 Implementing Ideas in Real Stock Portfolios; 3.6 Conclusions; References; Chapter 4. Enhanced indexation; 4.1 Introduction; 4.2 Constructing a Consistent View; 4.3 Enhanced Indexing; 4.4 An Illustrative Example: Top-down or Bottom-up?; 4.5 Conclusions; 4.6 Appendix 1: Derivation of the Theil-Goldberger Mixed Estimator; 4.7 Appendix 2: Optimization; References; Notes; Chapter 5. Portfolio management under taxes; 5.1 Introduction; 5.2 Do Taxes Really Matter to Investors and Managers? 5.3 The Core Problems5.4 The State of the Art; 5.5 The Multi-Period Aspect; 5.6 Loss Harvesting; 5.7 After-Tax Benchmarks; 5.8 Conclusions; References; Chapter 6. Using genetic algorithms to construct portfolios; 6.1 Limitations of Traditional Mean-Variance Portfolio Optimization; 6.2 Selecting a Method to Limit the Number of Securities in the Final Portfolio; 6.3 Practical Construction of a Genetic Algorithm-Based Optimizer; 6.4 Performance of Genetic Algorithm; 6.5 Conclusions; References; Chapter 7. Near-uniformly distributed, stochastically generated portfolios 7.1 Introduction - A Tractable N-Dimensional Experimental Control7.2 Applications; 7.3 Dynamic Constraints; 7.4 Results from the Dynamic Constraints Algorithm; 7.5 Problems and Limitations with Dynamic Constraints Algorithm; 7.6 Improvements to the Distribution; 7.7 Results of the Dynamic Constraints with Local Density Control; 7.8 Conclusions; 7.9 Further Work; 7.10 Appendix 1: Review of Holding Distribution in Low Dimensions with Minimal Constraints; 7.11 Appendix 2: Probability Distribution of Holding Weight in Monte Carlo Portfolios in N Dimensions with Minimal Constraints 7.12 Appendix 3: The Effects of Simple Holding Constraints on Expected Distribution of Asset Holding Weights |
| Record Nr. | UNINA-9910456029303321 |
| Amsterdam ; ; Oxford, : Butterworth-Heinemann, 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in portfolio construction and implementation / / edited by Stephen Satchell, Alan Scowcroft
| Advances in portfolio construction and implementation / / edited by Stephen Satchell, Alan Scowcroft |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Amsterdam ; ; Oxford, : Butterworth-Heinemann, 2003 |
| Descrizione fisica | 1 online resource (384 p.) |
| Disciplina | 332.6 |
| Altri autori (Persone) |
SatchellS (Stephen)
ScowcroftAlan |
| Collana | Butterworth-Heinemann finance |
| Soggetto topico |
Portfolio management
Portfolio management - Mathematical models Investments |
| ISBN |
1-280-96633-5
9786610966332 1-4175-0763-2 0-08-047184-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Advances in Portfolio Construction and Implementation; Copyright Page; Contents; List of Contributors; Introduction; Chapter 1. A review of portfolio planning: models and systems; 1.1 Introduction and Overview; 1.2 Alternative Computational Models; 1.3 Symmetric and Asymmetric Measures of Risk; 1.4 Computational Models in Practice; 1.5 Preparation of Data: Financial Data Marts; 1.6 Solution Methods; 1.7 Computational Experience; 1.8 Discussions and Conclusions; 1.9 Appendix 1: Piecewise Linear Approximation of the Quadratic Form
1.10 Appendix 2: Comparative Computational Views of the Alternative ModelsReferences; Web References; Acknowledgements; Chapter 2. Generalized mean-variance analysis and robust portfolio diversification; 2.1 Introduction; 2.2 Generalized Mean-Variance Analysis; 2.3 The State Preference Theory Approach to Portfolio Construction; 2.4 Implementation and Simulation; 2.5 Conclusions and Suggested Further Work; References; Chapter 3. Portfolio construction from mandate to stock weight: a practitioner's perspective; 3.1 Introduction; 3.2 Allocating Tracking Error for Multiple Portfolio Funds 3.3 Tracking Errors for Arbitrary Portfolios3.4 Active CAPM, or How Far Should a Bet be Taken?; 3.5 Implementing Ideas in Real Stock Portfolios; 3.6 Conclusions; References; Chapter 4. Enhanced indexation; 4.1 Introduction; 4.2 Constructing a Consistent View; 4.3 Enhanced Indexing; 4.4 An Illustrative Example: Top-down or Bottom-up?; 4.5 Conclusions; 4.6 Appendix 1: Derivation of the Theil-Goldberger Mixed Estimator; 4.7 Appendix 2: Optimization; References; Notes; Chapter 5. Portfolio management under taxes; 5.1 Introduction; 5.2 Do Taxes Really Matter to Investors and Managers? 5.3 The Core Problems5.4 The State of the Art; 5.5 The Multi-Period Aspect; 5.6 Loss Harvesting; 5.7 After-Tax Benchmarks; 5.8 Conclusions; References; Chapter 6. Using genetic algorithms to construct portfolios; 6.1 Limitations of Traditional Mean-Variance Portfolio Optimization; 6.2 Selecting a Method to Limit the Number of Securities in the Final Portfolio; 6.3 Practical Construction of a Genetic Algorithm-Based Optimizer; 6.4 Performance of Genetic Algorithm; 6.5 Conclusions; References; Chapter 7. Near-uniformly distributed, stochastically generated portfolios 7.1 Introduction - A Tractable N-Dimensional Experimental Control7.2 Applications; 7.3 Dynamic Constraints; 7.4 Results from the Dynamic Constraints Algorithm; 7.5 Problems and Limitations with Dynamic Constraints Algorithm; 7.6 Improvements to the Distribution; 7.7 Results of the Dynamic Constraints with Local Density Control; 7.8 Conclusions; 7.9 Further Work; 7.10 Appendix 1: Review of Holding Distribution in Low Dimensions with Minimal Constraints; 7.11 Appendix 2: Probability Distribution of Holding Weight in Monte Carlo Portfolios in N Dimensions with Minimal Constraints 7.12 Appendix 3: The Effects of Simple Holding Constraints on Expected Distribution of Asset Holding Weights |
| Record Nr. | UNINA-9910823089103321 |
| Amsterdam ; ; Oxford, : Butterworth-Heinemann, 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The analytics of risk model validation [[electronic resource] /] / edited by George Christodoulakis, Stephen Satchell
| The analytics of risk model validation [[electronic resource] /] / edited by George Christodoulakis, Stephen Satchell |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Amsterdam, : Academic Press, 2008 |
| Descrizione fisica | 1 online resource (217 p.) |
| Disciplina |
336.3
658.155015118 |
| Altri autori (Persone) |
ChristodoulakisGeorge
SatchellS (Stephen) |
| Collana | Quantitative finance series |
| Soggetto topico | Risk management - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-07150-1
9786611071509 0-08-055388-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; The Analytics of Risk Model Validation; Copyright Page; Table of Contents; About the editors; About the contributors; Preface; Chapter 1 Determinants of small business default; Abstract; 1. Introduction; 2. Data, methodology and summary statistics; 3. Empirical results of small business default; 4. Conclusion; References; Notes; Chapter 2 Validation of stress testing models; Abstract; 1. Why stress test?; 2. Stress testing basics; 3. Overview of validation approaches; 4. Subsampling tests; 5. Ideal scenario validation; 6. Scenario validation; 7. Cross-segment validation
8. Back-casting 9. Conclusions; References; Chapter 3 The validity of credit risk model validation methods; Abstract; 1. Introduction; 2. Measures of discriminatory power; 3. Uncertainty in credit risk model validation; 4. Confidence interval for ROC; 5. Bootstrapping; 6. Optimal rating combinations; 7. Concluding remarks; References; Chapter 4 A moments-based procedure for evaluating risk forecasting models; Abstract; 1. Introduction; 2. Preliminary analysis; 3. The likelihood ratio test; 4. A moments test of model adequacy; 5. An illustration; 6. Conclusions; 7. Acknowledgements; References Notes Appendix; 1. Error distribution; 2. Two-piece normal distribution; 3. t-Distribution; 4. Skew-t distribution; Chapter 5 Measuring concentration risk in credit portfolios; Abstract; 1. Concentration risk and validation; 2. Concentration risk and the IRB model; 3. Measuring name concentration; 4. Measuring sectoral concentration; 5. Numerical example; 6. Future challenges of concentration risk measurement; 7. Summary; References; Notes; Appendix A.1: IRB risk weight functions and concentration risk; Appendix A.2: Factor surface for the diversification factor; Appendix A.3 Chapter 6 A simple method for regulators to cross-check operational risk loss models for banks Abstract; 1. Introduction; 2. Background; 3. Cross-checking procedure; 4. Justification of our approach; 5. Justification for a lower bound using the lognormal distribution; 6. Conclusion; References; Chapter 7 Of the credibility of mapping and benchmarking credit risk estimates for internal rating systems; Abstract; 1. Introduction; 2. Why does the portfolio's structure matter?; 3. Credible credit ratings and credible credit risk estimates; 4. An empirical illustration; 5. Credible mapping 6. Conclusions 7. Acknowledgements; References; Appendix; 1. Further elements of modern credibility theory; 2. Proof of the credibility fundamental relation; 3. Mixed Gamma-Poisson distribution and negative binomial; 4. Calculation of the Bühlmann credibility estimate under the Gamma-Poisson model; 5. Calculation of accuracy ratio; Chapter 8 Analytic models of the ROC curve: Applications to credit rating model validation; Abstract; 1. Introduction; 2. Theoretical implications and applications; 3. Choices of distributions; 4. Performance evaluation on the AUROC estimation with simulated data 5. Summary |
| Record Nr. | UNINA-9910450624003321 |
| Amsterdam, : Academic Press, 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The analytics of risk model validation / / edited by George Christodoulakis, Stephen Satchell
| The analytics of risk model validation / / edited by George Christodoulakis, Stephen Satchell |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Amsterdam, : Academic Press, 2008 |
| Descrizione fisica | 1 online resource (217 p.) |
| Disciplina |
336.3
658.155015118 |
| Altri autori (Persone) |
ChristodoulakisGeorge
SatchellS (Stephen) |
| Collana | Quantitative finance series |
| Soggetto topico | Risk management - Mathematical models |
| ISBN |
1-281-07150-1
9786611071509 0-08-055388-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; The Analytics of Risk Model Validation; Copyright Page; Table of Contents; About the editors; About the contributors; Preface; Chapter 1 Determinants of small business default; Abstract; 1. Introduction; 2. Data, methodology and summary statistics; 3. Empirical results of small business default; 4. Conclusion; References; Notes; Chapter 2 Validation of stress testing models; Abstract; 1. Why stress test?; 2. Stress testing basics; 3. Overview of validation approaches; 4. Subsampling tests; 5. Ideal scenario validation; 6. Scenario validation; 7. Cross-segment validation
8. Back-casting 9. Conclusions; References; Chapter 3 The validity of credit risk model validation methods; Abstract; 1. Introduction; 2. Measures of discriminatory power; 3. Uncertainty in credit risk model validation; 4. Confidence interval for ROC; 5. Bootstrapping; 6. Optimal rating combinations; 7. Concluding remarks; References; Chapter 4 A moments-based procedure for evaluating risk forecasting models; Abstract; 1. Introduction; 2. Preliminary analysis; 3. The likelihood ratio test; 4. A moments test of model adequacy; 5. An illustration; 6. Conclusions; 7. Acknowledgements; References Notes Appendix; 1. Error distribution; 2. Two-piece normal distribution; 3. t-Distribution; 4. Skew-t distribution; Chapter 5 Measuring concentration risk in credit portfolios; Abstract; 1. Concentration risk and validation; 2. Concentration risk and the IRB model; 3. Measuring name concentration; 4. Measuring sectoral concentration; 5. Numerical example; 6. Future challenges of concentration risk measurement; 7. Summary; References; Notes; Appendix A.1: IRB risk weight functions and concentration risk; Appendix A.2: Factor surface for the diversification factor; Appendix A.3 Chapter 6 A simple method for regulators to cross-check operational risk loss models for banks Abstract; 1. Introduction; 2. Background; 3. Cross-checking procedure; 4. Justification of our approach; 5. Justification for a lower bound using the lognormal distribution; 6. Conclusion; References; Chapter 7 Of the credibility of mapping and benchmarking credit risk estimates for internal rating systems; Abstract; 1. Introduction; 2. Why does the portfolio's structure matter?; 3. Credible credit ratings and credible credit risk estimates; 4. An empirical illustration; 5. Credible mapping 6. Conclusions 7. Acknowledgements; References; Appendix; 1. Further elements of modern credibility theory; 2. Proof of the credibility fundamental relation; 3. Mixed Gamma-Poisson distribution and negative binomial; 4. Calculation of the Bühlmann credibility estimate under the Gamma-Poisson model; 5. Calculation of accuracy ratio; Chapter 8 Analytic models of the ROC curve: Applications to credit rating model validation; Abstract; 1. Introduction; 2. Theoretical implications and applications; 3. Choices of distributions; 4. Performance evaluation on the AUROC estimation with simulated data 5. Summary |
| Record Nr. | UNINA-9910821434503321 |
| Amsterdam, : Academic Press, 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting expected returns in the financial markets [[electronic resource] /] / edited by Stephen Satchell
| Forecasting expected returns in the financial markets [[electronic resource] /] / edited by Stephen Satchell |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Academic Press, 2007 |
| Descrizione fisica | 1 online resource (286 p.) |
| Disciplina | 332.63/2042 |
| Altri autori (Persone) | SatchellS (Stephen) |
| Collana | Quantitative finance series |
| Soggetto topico |
Stock price forecasting - Mathematics
Securities - Prices - Mathematical models Investment analysis - Mathematics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-05765-7
9786611057657 0-08-055067-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Market efficiency and forecasting -- A step-by-step guide to the Black-Litterman model -- A demystification of the Black-Litterman model : managing quantitative and traditional portfolio construction -- Optimal portfolios from ordering information -- Some choices in forecast construction -- Bayesian analysis of the Black-Scholes option price -- Bayesian forecasting of options prices: a natural framework for pooling historical and implied volatility information -- Robust optimization for utilizing forecasted returns in institutional investment -- Cross-sectional stock returns in the UK market : the role of liquidity risk -- The information horizon- optimal holding period, strategy aggression and model combination in a multi-horizon framework -- Optimal forecasting horizon for skilled investors -- Investments as bets in the binomial asset pricing model -- The hidden binomial economy and the role of forecasts in determining prices. |
| Record Nr. | UNINA-9910457671603321 |
| Amsterdam ; ; Boston, : Academic Press, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting expected returns in the financial markets / / edited by Stephen Satchell
| Forecasting expected returns in the financial markets / / edited by Stephen Satchell |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Academic Press, 2007 |
| Descrizione fisica | 1 online resource (286 p.) |
| Disciplina | 332.63/2042 |
| Altri autori (Persone) | SatchellS (Stephen) |
| Collana | Quantitative finance series |
| Soggetto topico |
Stock price forecasting - Mathematics
Securities - Prices - Mathematical models Investment analysis - Mathematics |
| ISBN |
1-281-05765-7
9786611057657 0-08-055067-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Market efficiency and forecasting -- A step-by-step guide to the Black-Litterman model -- A demystification of the Black-Litterman model : managing quantitative and traditional portfolio construction -- Optimal portfolios from ordering information -- Some choices in forecast construction -- Bayesian analysis of the Black-Scholes option price -- Bayesian forecasting of options prices: a natural framework for pooling historical and implied volatility information -- Robust optimization for utilizing forecasted returns in institutional investment -- Cross-sectional stock returns in the UK market : the role of liquidity risk -- The information horizon- optimal holding period, strategy aggression and model combination in a multi-horizon framework -- Optimal forecasting horizon for skilled investors -- Investments as bets in the binomial asset pricing model -- The hidden binomial economy and the role of forecasts in determining prices. |
| Record Nr. | UNINA-9910828006003321 |
| Amsterdam ; ; Boston, : Academic Press, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting volatility in the financial markets [[electronic resource] /] / edited by John Knight, Stephen Satchell
| Forecasting volatility in the financial markets [[electronic resource] /] / edited by John Knight, Stephen Satchell |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007 |
| Descrizione fisica | 1 online resource (428 p.) |
| Disciplina | 332.66/2042 |
| Altri autori (Persone) |
KnightJohn L
SatchellS (Stephen) |
| Collana | Quantitative finance series |
| Soggetto topico |
Options (Finance) - Mathematical models
Securities - Prices - Mathematical models Stock price forecasting - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-96289-5
9786610962891 0-08-047142-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Forecasting Volatility in the Financial Markets; Copyright Page; Table of Contents; List of contributors; Preface to Third Edition; Introduction; Chapter 1 Volatility modelling and forecasting in finance; 1.1 Introduction; 1.2 Autoregressive moving average models; 1.3 Changes in volatility; 1.3.1 Volatility in financial time series: stylized facts; 1.3.2 The basic set-up; 1.4 ARCH models; 1.4.1 Generalized ARCH; 1.4.2 Integrated ARCH; 1.4.3 Exponential ARCH; 1.4.4 ARCH-M model; 1.4.5 Fractionally integrated ARCH; 1.4.6 Other univariate ARCH formulations
1.4.7 Multivariate ARCH models1.5 Stochastic variance models; 1.5.1 From continuous time financial models to discrete time SV models; 1.5.2 Persistence and the SV model; 1.5.3 Long memory SV models; 1.5.4 Risk-return trade-off in SV models; 1.5.5 Multivariate SV models; 1.6 Structural changes in the underlying process; 1.6.1 Regime switching models; 1.6.2 Extensions of the regime switching models; 1.7 Threshold models; 1.7.1 Self-exciting threshold models; 1.7.2 Open loop threshold models; 1.7.3 Closed loop threshold models; 1.7.4 Smooth threshold autoregressive models 1.7.5 Identification in SETAR models1.7.6 A threshold AR(1) model; 1.7.7 A threshold MA model; 1.7.8 Threshold models and asymmetries in volatility; 1.7.9 Testing for non-linearity; 1.7.10 Threshold estimation and prediction of TAR models; 1.8 Volatility forecasting; 1.8.1 Volatility forecasting based on time-series models; 1.8.2 Volatility forecasting based on option ISD (Implied Standard Deviation); 1.9 Conclusion; References and further reading; Notes; Chapter 2 What good is a volatility model?; Abstract; 2.1 Introduction; 2.1.1 Notation; 2.1.2 Types of volatility models 2.2 Stylized facts about asset price volatility2.2.1 Volatility exhibits persistence; 2.2.2 Volatility is mean reverting; 2.2.3 Innovations may have an asymmetric impact on volatility; 2.2.4 Exogenous variables may influence volatility; 2.2.5 Tail probabilities; 2.2.6 Forecast evaluation; 2.3 An empirical example; 2.3.1 Summary of the data; 2.3.2 A volatility model; 2.3.3 Mean reversion and persistence in volatility; 2.3.4 An asymmetric volatility model; 2.3.5 A model with exogenous volatility regressors; 2.3.6 Aggregation of volatility models 2.4 Conclusions and challenges for future researchReferences; Notes; Chapter 3 Applications of portfolio variety; Abstract; 3.1 Introduction; 3.2 Some applications of variety; 3.3 Empirical research on variety; 3.4 Variety and risk estimation; 3.5 Variety as an explanation of active management styles; 3.6 Summary; References; Chapter 4 A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices; 4.1 Introduction; 4.2 Data; 4.3 Theory and empirical methodology; 4.3.1 Realized variance; 4.3.2 Optimal sampling frequency; 4.3.3 Estimation; 4.3.4 Forecasting 4.4 Initial data analysis |
| Record Nr. | UNINA-9910457681603321 |
| Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting volatility in the financial markets / / edited by John Knight, Stephen Satchell
| Forecasting volatility in the financial markets / / edited by John Knight, Stephen Satchell |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007 |
| Descrizione fisica | 1 online resource (428 p.) |
| Disciplina | 332.66/2042 |
| Altri autori (Persone) |
KnightJohn L
SatchellS (Stephen) |
| Collana | Quantitative finance series |
| Soggetto topico |
Options (Finance) - Mathematical models
Securities - Prices - Mathematical models Stock price forecasting - Mathematical models |
| ISBN |
1-280-96289-5
9786610962891 0-08-047142-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Forecasting Volatility in the Financial Markets; Copyright Page; Table of Contents; List of contributors; Preface to Third Edition; Introduction; Chapter 1 Volatility modelling and forecasting in finance; 1.1 Introduction; 1.2 Autoregressive moving average models; 1.3 Changes in volatility; 1.3.1 Volatility in financial time series: stylized facts; 1.3.2 The basic set-up; 1.4 ARCH models; 1.4.1 Generalized ARCH; 1.4.2 Integrated ARCH; 1.4.3 Exponential ARCH; 1.4.4 ARCH-M model; 1.4.5 Fractionally integrated ARCH; 1.4.6 Other univariate ARCH formulations
1.4.7 Multivariate ARCH models1.5 Stochastic variance models; 1.5.1 From continuous time financial models to discrete time SV models; 1.5.2 Persistence and the SV model; 1.5.3 Long memory SV models; 1.5.4 Risk-return trade-off in SV models; 1.5.5 Multivariate SV models; 1.6 Structural changes in the underlying process; 1.6.1 Regime switching models; 1.6.2 Extensions of the regime switching models; 1.7 Threshold models; 1.7.1 Self-exciting threshold models; 1.7.2 Open loop threshold models; 1.7.3 Closed loop threshold models; 1.7.4 Smooth threshold autoregressive models 1.7.5 Identification in SETAR models1.7.6 A threshold AR(1) model; 1.7.7 A threshold MA model; 1.7.8 Threshold models and asymmetries in volatility; 1.7.9 Testing for non-linearity; 1.7.10 Threshold estimation and prediction of TAR models; 1.8 Volatility forecasting; 1.8.1 Volatility forecasting based on time-series models; 1.8.2 Volatility forecasting based on option ISD (Implied Standard Deviation); 1.9 Conclusion; References and further reading; Notes; Chapter 2 What good is a volatility model?; Abstract; 2.1 Introduction; 2.1.1 Notation; 2.1.2 Types of volatility models 2.2 Stylized facts about asset price volatility2.2.1 Volatility exhibits persistence; 2.2.2 Volatility is mean reverting; 2.2.3 Innovations may have an asymmetric impact on volatility; 2.2.4 Exogenous variables may influence volatility; 2.2.5 Tail probabilities; 2.2.6 Forecast evaluation; 2.3 An empirical example; 2.3.1 Summary of the data; 2.3.2 A volatility model; 2.3.3 Mean reversion and persistence in volatility; 2.3.4 An asymmetric volatility model; 2.3.5 A model with exogenous volatility regressors; 2.3.6 Aggregation of volatility models 2.4 Conclusions and challenges for future researchReferences; Notes; Chapter 3 Applications of portfolio variety; Abstract; 3.1 Introduction; 3.2 Some applications of variety; 3.3 Empirical research on variety; 3.4 Variety and risk estimation; 3.5 Variety as an explanation of active management styles; 3.6 Summary; References; Chapter 4 A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices; 4.1 Introduction; 4.2 Data; 4.3 Theory and empirical methodology; 4.3.1 Realized variance; 4.3.2 Optimal sampling frequency; 4.3.3 Estimation; 4.3.4 Forecasting 4.4 Initial data analysis |
| Record Nr. | UNINA-9910824456303321 |
| Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Linear factor models in finance [[electronic resource] /] / [edited by] John Knight and Stephen Satchell
| Linear factor models in finance [[electronic resource] /] / [edited by] John Knight and Stephen Satchell |
| Pubbl/distr/stampa | Amsterdam ; ; Oxford, : Elsevier Butterworth-Heinemann, 2005 |
| Descrizione fisica | 1 online resource (298 p.) |
| Disciplina | 332.015118 |
| Altri autori (Persone) |
KnightJohn L
SatchellS (Stephen) |
| Collana | Quantitative finance series |
| Soggetto topico |
Finance - Mathematical models
Mathematics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-63881-8
9786610638819 0-08-045532-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Linear Factor Models in Finance; Contents; List of contributors; Introduction; 1 Review of literature on multifactor asset pricing models; 1.1 Theoretical reasons for existence of multiple factors; 1.2 Empirical evidence of existence of multiple factors; 1.3 Estimation of factor pricing models; Bibliography; 2 Estimating UK factor models using the multivariate skew normal distribution; 2.1 Introduction; 2.2 The multivariate skew normal distribution and some of its properties; 2.3 Conditional distributions and factor models; 2.4 Data model choice and estimation; 2.5 Empirical study
2.5.1 Basic return statistics2.5.2 Overall model fit; 2.5.3 Comparison of parameter estimates; 2.5.4 Skewness parameters; 2.5.5 Tau and time-varying conditional variance; 2.6 Conclusions; Acknowledgement; References; 3 Misspecification in the linear pricing model; 3.1 Introduction; 3.2 Framework; 3.2.1 Arbitrage Pricing Theory; 3.2.2 Multivariate F test used in linear factor model; 3.2.3 Average F test used in linear factor model; 3.3 Distribution of the multivariate F test statistics under misspecification; 3.3.1 Exclusion of a set of factors from estimation 3.3.2 Time-varying factor loadings3.4 Simulation study; 3.4.1 Design; 3.4.2 Factors serially independent; 3.4.3 Factors autocorrelated; 3.4.4 Time-varying factor loadings; 3.4.5 Simulation results; 3.5 Conclusion; Appendix: Proof of proposition 3.1 and proposition 3.2; 4 Bayesian estimation of risk premia in an APT context; 4.1 Introduction; 4.2 The general APT framework; 4.2.1 The excess return generating process (when factors are traded portfolios); 4.2.2 The excess return generating process (when factors are macroeconomic variables or non-traded portfolios) 4.2.3 Obtaining the (K x 1) vector of risk premia l4.3 Introducing a Bayesian framework using a Minnesota prior (Litterman's prior); 4.3.1 Prior estimates of the risk premia; 4.3.2 Posterior estimates of the risk premia; 4.4 An empirical application; 4.4.1 Data; 4.4.2 Results; 4.5 Conclusion; References; Appendix; 5 Sharpe style analysis in the MSCI sector portfolios: a Monte Carlo integration approach; 5.1 Introduction; 5.2 Methodology; 5.2.1 A Bayesian decision-theoretic approach; 5.2.2 Estimation by Monte Carlo integration; 5.3 Style analysis in the MSCI sector portfolios; 5.4 Conclusions References6 Implication of the method of portfolio formation on asset pricing tests; 6.1 Introduction; 6.2 Models; 6.2.1 Asset pricing frameworks; 6.2.2 Specifications to be tested; 6.3 Implementation; 6.3.1 Multivariate F test; 6.3.2 Average F test; 6.3.3 Stochastic discount factor using GMM with Hansen and Jagannathan distance; 6.3.4 A look at the pricing errors under different tests; 6.4 Variables construction and data sources; 6.4.1 Data sources; 6.4.2 Independent variables: excess market return, size return factor and book-to-market return factor 6.4.3 Dependent variables: size-sorted portfolios, beta-sorted portfolios and individual assets |
| Record Nr. | UNINA-9910457338403321 |
| Amsterdam ; ; Oxford, : Elsevier Butterworth-Heinemann, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Linear factor models in finance / / [edited by] John Knight and Stephen Satchell
| Linear factor models in finance / / [edited by] John Knight and Stephen Satchell |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Oxford, : Elsevier Butterworth-Heinemann, 2005 |
| Descrizione fisica | 1 online resource (298 p.) |
| Disciplina | 332.015118 |
| Altri autori (Persone) |
KnightJohn L
SatchellS (Stephen) |
| Collana | Quantitative finance series |
| Soggetto topico |
Finance - Mathematical models
Mathematics |
| ISBN |
1-280-63881-8
9786610638819 0-08-045532-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Linear Factor Models in Finance; Contents; List of contributors; Introduction; 1 Review of literature on multifactor asset pricing models; 1.1 Theoretical reasons for existence of multiple factors; 1.2 Empirical evidence of existence of multiple factors; 1.3 Estimation of factor pricing models; Bibliography; 2 Estimating UK factor models using the multivariate skew normal distribution; 2.1 Introduction; 2.2 The multivariate skew normal distribution and some of its properties; 2.3 Conditional distributions and factor models; 2.4 Data model choice and estimation; 2.5 Empirical study
2.5.1 Basic return statistics2.5.2 Overall model fit; 2.5.3 Comparison of parameter estimates; 2.5.4 Skewness parameters; 2.5.5 Tau and time-varying conditional variance; 2.6 Conclusions; Acknowledgement; References; 3 Misspecification in the linear pricing model; 3.1 Introduction; 3.2 Framework; 3.2.1 Arbitrage Pricing Theory; 3.2.2 Multivariate F test used in linear factor model; 3.2.3 Average F test used in linear factor model; 3.3 Distribution of the multivariate F test statistics under misspecification; 3.3.1 Exclusion of a set of factors from estimation 3.3.2 Time-varying factor loadings3.4 Simulation study; 3.4.1 Design; 3.4.2 Factors serially independent; 3.4.3 Factors autocorrelated; 3.4.4 Time-varying factor loadings; 3.4.5 Simulation results; 3.5 Conclusion; Appendix: Proof of proposition 3.1 and proposition 3.2; 4 Bayesian estimation of risk premia in an APT context; 4.1 Introduction; 4.2 The general APT framework; 4.2.1 The excess return generating process (when factors are traded portfolios); 4.2.2 The excess return generating process (when factors are macroeconomic variables or non-traded portfolios) 4.2.3 Obtaining the (K x 1) vector of risk premia l4.3 Introducing a Bayesian framework using a Minnesota prior (Litterman's prior); 4.3.1 Prior estimates of the risk premia; 4.3.2 Posterior estimates of the risk premia; 4.4 An empirical application; 4.4.1 Data; 4.4.2 Results; 4.5 Conclusion; References; Appendix; 5 Sharpe style analysis in the MSCI sector portfolios: a Monte Carlo integration approach; 5.1 Introduction; 5.2 Methodology; 5.2.1 A Bayesian decision-theoretic approach; 5.2.2 Estimation by Monte Carlo integration; 5.3 Style analysis in the MSCI sector portfolios; 5.4 Conclusions References6 Implication of the method of portfolio formation on asset pricing tests; 6.1 Introduction; 6.2 Models; 6.2.1 Asset pricing frameworks; 6.2.2 Specifications to be tested; 6.3 Implementation; 6.3.1 Multivariate F test; 6.3.2 Average F test; 6.3.3 Stochastic discount factor using GMM with Hansen and Jagannathan distance; 6.3.4 A look at the pricing errors under different tests; 6.4 Variables construction and data sources; 6.4.1 Data sources; 6.4.2 Independent variables: excess market return, size return factor and book-to-market return factor 6.4.3 Dependent variables: size-sorted portfolios, beta-sorted portfolios and individual assets |
| Record Nr. | UNINA-9910809959403321 |
| Amsterdam ; ; Oxford, : Elsevier Butterworth-Heinemann, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||