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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 | ||
| ||