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Contagion phenomena with applications in finance / / Serge Darolles, Christian Gourieroux
| Contagion phenomena with applications in finance / / Serge Darolles, Christian Gourieroux |
| Autore | Darolles Serge |
| Pubbl/distr/stampa | Oxford, UK : , : Elsevier Science, , 2015 |
| Descrizione fisica | 1 online resource (168 p.) |
| Disciplina | 332 |
| Collana | Quantitative finance set |
| Soggetto topico |
Financial crises
International finance Financial risk management |
| ISBN | 0-08-100478-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Contagion Phenomena with Applications in Finance; Copyright; Contents; Introduction; Chapter 1: Contagion and Causality in Static Models; 1.1. Linear Dependence in a Static Model; 1.2. Nonlinear Dependence in a Static Model; 1.3. Model with Exogenous Switching Regimes; 1.4. Chapter 1 Highlights; 1.5. Appendices; Chapter 2: Contagion in Structural VARMA Models; 2.1. Shocks in a Dynamic Model; 2.2. A Vector Autoregressive Moving Average (VARMA) Model with Independent Errors; 2.3. Non-Fundamentalness; 2.4. Chapter 2 Highlights; 2.5. Appendices
Chapter 3: Common Frailty Versus Contagion in Linear Dynamic Models3.1. Linear Dynamic Model with Common Factor and Contagion; 3.2. Observable Versus Latent Factors; 3.3. Shocks, Impulse Response Functions and Stress; 3.4. Constrained Models and Misspecification; 3.5. The Literature; 3.6. Chapter 3 Highlights; 3.7. Appendices; Chapter 4: Applications of Linear Dynamic Models; 4.1. Portfolio Management; 4.2. Contagion Among Banks; 4.3. Chapter 4 Highlights; 4.4. Appendices; Chapter 5: Common Frailty and Contagion in Nonlinear Dynamic Models; 5.1. Specifications 5.2. Stochastic Volatility Model5.3. Application to Portfolio Management; 5.4. Chapter 5 Highlights; 5.5. Appendices; Chapter 6: An Application of Nonlinear Dynamic Models: The Hedge Fund Survival; 6.1. HF Liquidation Data; 6.2. Dynamic Poisson Model; 6.3. Results; 6.4. Stress-Tests; 6.5. Chapter 6 Highlights; 6.6. Appendices; Bibliography; Index; Back Cover |
| Record Nr. | UNINA-9910583055503321 |
Darolles Serge
|
||
| Oxford, UK : , : Elsevier Science, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multi-factor models and signal processing techniques [[electronic resource] ] : application to quantitative finance / / Serge Darolles, Patrick Duvaut, Emmanuelle Jay
| Multi-factor models and signal processing techniques [[electronic resource] ] : application to quantitative finance / / Serge Darolles, Patrick Duvaut, Emmanuelle Jay |
| Autore | Darolles Serge |
| Pubbl/distr/stampa | London, : ISTE, 2013 |
| Descrizione fisica | 1 online resource (188 p.) |
| Disciplina | 621.382 |
| Altri autori (Persone) |
DuvautPatrick
JayEmmanuelle |
| Collana | Digital signal and image processing series |
| Soggetto topico |
Signal processing - Mathematical models
Finance - Mathematical models |
| ISBN |
1-118-57749-3
1-118-57738-8 1-118-57740-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Contents; Foreword; Introduction; Notations and Acronyms; Chapter 1. Factor Models andGeneral Definition; 1.1. Introduction; 1.2. What are factor models?; 1.2.1. Notations; 1.2.2. Factor representation; 1.3. Why factor models in finance?; 1.3.1. Style analysis; 1.3.2. Optimal portfolio allocation; 1.4. How to build factor models?; 1.4.1. Factor selection; 1.4.2. Parameters estimation; 1.5. Historical perspective; 1.5.1. CAPM and Sharpe's market model; 1.5.2. APT for arbitrage pricing theory; 1.6. Glossary Volatility; Chapter 2. Factor Selection; 2.1. Introduction
2.2. Qualitative know-how2.2.1. Fama and French model; 2.2.2. The Chen et al. model; 2.2.3. The risk-based factor model of Fung and Hsieh; 2.3. Quantitative methods based on eigenfactors; 2.3.1. Notation; 2.3.2. Subspace methods: the Principal Component Analysis; 2.4. Model order choice; 2.4.1. Information criteria; 2.5. Appendix 1: Covariance matrix estimation; 2.5.1. Sample mean; 2.5.2. Sample covariance matrix; 2.5.3. Robust covariance matrix estimation: M-estimators; 2.6. Appendix 2: Similarity of the eigenfactor selection with the MUSIC algorithm; 2.7. Appendix 3: Large panel data 2.7.1. Large panel data criteria2.8. Chapter 2 highlights; Chapter 3. Least Squares Estimation(LSE) and Kalman Filtering (KF)for Factor Modeling:A Geometrical Perspective; 3.1. Introduction; 3.2. Why LSE and KF in factor modeling?; 3.2.1. Factor model per return; 3.2.2. Alpha and beta estimation per return; 3.3. LSE setup; 3.3.1. Current observation window and block processing; 3.3.2. LSE regression; 3.4. LSE objective and criterion; 3.5. How LSE is working (for LSE users and programmers); 3.6. Interpretation of the LSE solution; 3.6.1. Bias and variance 3.6.2. Geometrical interpretation of LSE3.7. Derivations of LSE solution; 3.8. Why KF and which setup?; 3.8.1. LSE method does not provide a recursive estimate; 3.8.2. The state space model and its recursive component; 3.8.3. Parsimony and orthogonality assumptions; 3.9. What are the main properties of the KF model?; 3.9.1. Self-aggregation feature; 3.9.2. Markovian property; 3.9.3. Innovation property; 3.10. What is the objective of KF?; 3.11. How does the KF work (for users and programmers)?; 3.11.1. Algorithm summary; 3.11.2. Initialization of the KF recursive equations 3.12. Interpretation of the KF updates3.12.1. Prediction filtering, equation [3.34]; 3.12.2. Prediction accuracy processing, equation [3.35]; 3.12.3. Correction filtering equations [3.36]-[3.37]; 3.12.4. Correction accuracy processing, equation [3.38]; 3.13. Practice; 3.13.1. Comparison of the estimation methods on synthetic data; 3.13.2. Market risk hedging given asingle-factor model; 3.13.3. Hedge fund style analysis using amulti-factor model; 3.14. Geometrical derivation of KF updating equations; 3.14.1. Geometrical interpretation of MSE criterion and the MMSE solution 3.14.2. Derivation of the prediction filtering update |
| Record Nr. | UNINA-9910139016803321 |
|
Darolles Serge
|
||
| London, : ISTE, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multi-factor models and signal processing techniques [[electronic resource] ] : application to quantitative finance / / Serge Darolles, Patrick Duvaut, Emmanuelle Jay
| Multi-factor models and signal processing techniques [[electronic resource] ] : application to quantitative finance / / Serge Darolles, Patrick Duvaut, Emmanuelle Jay |
| Autore | Darolles Serge |
| Pubbl/distr/stampa | London, : ISTE, 2013 |
| Descrizione fisica | 1 online resource (188 p.) |
| Disciplina | 621.382 |
| Altri autori (Persone) |
DuvautPatrick
JayEmmanuelle |
| Collana | Digital signal and image processing series |
| Soggetto topico |
Signal processing - Mathematical models
Finance - Mathematical models |
| ISBN |
1-118-57749-3
1-118-57738-8 1-118-57740-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Contents; Foreword; Introduction; Notations and Acronyms; Chapter 1. Factor Models andGeneral Definition; 1.1. Introduction; 1.2. What are factor models?; 1.2.1. Notations; 1.2.2. Factor representation; 1.3. Why factor models in finance?; 1.3.1. Style analysis; 1.3.2. Optimal portfolio allocation; 1.4. How to build factor models?; 1.4.1. Factor selection; 1.4.2. Parameters estimation; 1.5. Historical perspective; 1.5.1. CAPM and Sharpe's market model; 1.5.2. APT for arbitrage pricing theory; 1.6. Glossary Volatility; Chapter 2. Factor Selection; 2.1. Introduction
2.2. Qualitative know-how2.2.1. Fama and French model; 2.2.2. The Chen et al. model; 2.2.3. The risk-based factor model of Fung and Hsieh; 2.3. Quantitative methods based on eigenfactors; 2.3.1. Notation; 2.3.2. Subspace methods: the Principal Component Analysis; 2.4. Model order choice; 2.4.1. Information criteria; 2.5. Appendix 1: Covariance matrix estimation; 2.5.1. Sample mean; 2.5.2. Sample covariance matrix; 2.5.3. Robust covariance matrix estimation: M-estimators; 2.6. Appendix 2: Similarity of the eigenfactor selection with the MUSIC algorithm; 2.7. Appendix 3: Large panel data 2.7.1. Large panel data criteria2.8. Chapter 2 highlights; Chapter 3. Least Squares Estimation(LSE) and Kalman Filtering (KF)for Factor Modeling:A Geometrical Perspective; 3.1. Introduction; 3.2. Why LSE and KF in factor modeling?; 3.2.1. Factor model per return; 3.2.2. Alpha and beta estimation per return; 3.3. LSE setup; 3.3.1. Current observation window and block processing; 3.3.2. LSE regression; 3.4. LSE objective and criterion; 3.5. How LSE is working (for LSE users and programmers); 3.6. Interpretation of the LSE solution; 3.6.1. Bias and variance 3.6.2. Geometrical interpretation of LSE3.7. Derivations of LSE solution; 3.8. Why KF and which setup?; 3.8.1. LSE method does not provide a recursive estimate; 3.8.2. The state space model and its recursive component; 3.8.3. Parsimony and orthogonality assumptions; 3.9. What are the main properties of the KF model?; 3.9.1. Self-aggregation feature; 3.9.2. Markovian property; 3.9.3. Innovation property; 3.10. What is the objective of KF?; 3.11. How does the KF work (for users and programmers)?; 3.11.1. Algorithm summary; 3.11.2. Initialization of the KF recursive equations 3.12. Interpretation of the KF updates3.12.1. Prediction filtering, equation [3.34]; 3.12.2. Prediction accuracy processing, equation [3.35]; 3.12.3. Correction filtering equations [3.36]-[3.37]; 3.12.4. Correction accuracy processing, equation [3.38]; 3.13. Practice; 3.13.1. Comparison of the estimation methods on synthetic data; 3.13.2. Market risk hedging given asingle-factor model; 3.13.3. Hedge fund style analysis using amulti-factor model; 3.14. Geometrical derivation of KF updating equations; 3.14.1. Geometrical interpretation of MSE criterion and the MMSE solution 3.14.2. Derivation of the prediction filtering update |
| Record Nr. | UNISA-996205826003316 |
|
Darolles Serge
|
||
| London, : ISTE, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Multi-factor models and signal processing techniques [[electronic resource] ] : application to quantitative finance / / Serge Darolles, Patrick Duvaut, Emmanuelle Jay
| Multi-factor models and signal processing techniques [[electronic resource] ] : application to quantitative finance / / Serge Darolles, Patrick Duvaut, Emmanuelle Jay |
| Autore | Darolles Serge |
| Pubbl/distr/stampa | London, : ISTE, 2013 |
| Descrizione fisica | 1 online resource (188 p.) |
| Disciplina | 621.382 |
| Altri autori (Persone) |
DuvautPatrick
JayEmmanuelle |
| Collana | Digital signal and image processing series |
| Soggetto topico |
Signal processing - Mathematical models
Finance - Mathematical models |
| ISBN |
1-118-57749-3
1-118-57738-8 1-118-57740-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Contents; Foreword; Introduction; Notations and Acronyms; Chapter 1. Factor Models andGeneral Definition; 1.1. Introduction; 1.2. What are factor models?; 1.2.1. Notations; 1.2.2. Factor representation; 1.3. Why factor models in finance?; 1.3.1. Style analysis; 1.3.2. Optimal portfolio allocation; 1.4. How to build factor models?; 1.4.1. Factor selection; 1.4.2. Parameters estimation; 1.5. Historical perspective; 1.5.1. CAPM and Sharpe's market model; 1.5.2. APT for arbitrage pricing theory; 1.6. Glossary Volatility; Chapter 2. Factor Selection; 2.1. Introduction
2.2. Qualitative know-how2.2.1. Fama and French model; 2.2.2. The Chen et al. model; 2.2.3. The risk-based factor model of Fung and Hsieh; 2.3. Quantitative methods based on eigenfactors; 2.3.1. Notation; 2.3.2. Subspace methods: the Principal Component Analysis; 2.4. Model order choice; 2.4.1. Information criteria; 2.5. Appendix 1: Covariance matrix estimation; 2.5.1. Sample mean; 2.5.2. Sample covariance matrix; 2.5.3. Robust covariance matrix estimation: M-estimators; 2.6. Appendix 2: Similarity of the eigenfactor selection with the MUSIC algorithm; 2.7. Appendix 3: Large panel data 2.7.1. Large panel data criteria2.8. Chapter 2 highlights; Chapter 3. Least Squares Estimation(LSE) and Kalman Filtering (KF)for Factor Modeling:A Geometrical Perspective; 3.1. Introduction; 3.2. Why LSE and KF in factor modeling?; 3.2.1. Factor model per return; 3.2.2. Alpha and beta estimation per return; 3.3. LSE setup; 3.3.1. Current observation window and block processing; 3.3.2. LSE regression; 3.4. LSE objective and criterion; 3.5. How LSE is working (for LSE users and programmers); 3.6. Interpretation of the LSE solution; 3.6.1. Bias and variance 3.6.2. Geometrical interpretation of LSE3.7. Derivations of LSE solution; 3.8. Why KF and which setup?; 3.8.1. LSE method does not provide a recursive estimate; 3.8.2. The state space model and its recursive component; 3.8.3. Parsimony and orthogonality assumptions; 3.9. What are the main properties of the KF model?; 3.9.1. Self-aggregation feature; 3.9.2. Markovian property; 3.9.3. Innovation property; 3.10. What is the objective of KF?; 3.11. How does the KF work (for users and programmers)?; 3.11.1. Algorithm summary; 3.11.2. Initialization of the KF recursive equations 3.12. Interpretation of the KF updates3.12.1. Prediction filtering, equation [3.34]; 3.12.2. Prediction accuracy processing, equation [3.35]; 3.12.3. Correction filtering equations [3.36]-[3.37]; 3.12.4. Correction accuracy processing, equation [3.38]; 3.13. Practice; 3.13.1. Comparison of the estimation methods on synthetic data; 3.13.2. Market risk hedging given asingle-factor model; 3.13.3. Hedge fund style analysis using amulti-factor model; 3.14. Geometrical derivation of KF updating equations; 3.14.1. Geometrical interpretation of MSE criterion and the MMSE solution 3.14.2. Derivation of the prediction filtering update |
| Record Nr. | UNINA-9910826223603321 |
|
Darolles Serge
|
||
| London, : ISTE, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||