| Document Type
|
:
|
BL
|
| Record Number
|
:
|
662130
|
| Doc. No
|
:
|
dltt
|
| Main Entry
|
:
|
Sekerke, Matt.
|
| Title & Author
|
:
|
Bayesian risk management : : a guide to model risk and sequential learning in financial markets /\ Matt Sekerke
|
| Series Statement
|
:
|
The Wiley finance series
|
| Page. NO
|
:
|
1 online resource
|
| ISBN
|
:
|
9781118747452 (pdf)
|
|
|
:
|
: 1118747453 (pdf)
|
|
|
:
|
: 9781118747506 (epub)
|
|
|
:
|
: 111874750X (epub)
|
|
|
:
|
9781118708606 (cloth)
|
|
|
:
|
: 9781118864784
|
|
|
:
|
: 1118864786
|
|
|
:
|
: 1118708601 (cloth)
|
|
|
:
|
: 9781118708606 (cloth)
|
| Bibliographies/Indexes
|
:
|
Includes bibliographical references and index
|
| Contents
|
:
|
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 -- Capturing Uncertainty in Statistical Models -- Prior Knowledge, Parameter Uncertainty, and Estimation -- Estimation 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 Distributions -- Comparison -- Decisions after Observing the Data: The Choice of Estimators -- Decisions and Loss -- Loss and Prior Information -- 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 -- Sequential Learning with Adaptive Statistical Models -- 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 -- Bayesian Inference in State-Space Tie 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 Components -- Building 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 -- 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 -- Particle Learning -- Sequential Model Comparison -- Sequential Models of Financial Risk -- Volatility Modeling -- Single-Asset Volatility -- Classical Models with Conditional Volatility -- Rolling-Window-Based Methods -- GARCH Models -- Bayesian Models -- Volatility Modeling with the DLM -- State-Space Models of Stochastic Volatility -- Comparison -- Volatility for Multiple Assets -- EWMA and Inverted-Wishart Estimates -- Decompositions of the Covariance Matrix -- Time-Varying Correlations -- Asset-Pricing Models and Hedging -- Derivative Pricing in the Schwartz Model -- State Dynamics -- Describing Futures Prices as a Function of Latent Factors -- Continuous- and Discrete-Time Factor Dynamics -- Model-Implied Prices and the Observation Equation -- Online State-Space Model Estimates of Derivative Prices -- Estimation with the Liu-West Filter -- Prior Information -- Estimation Results -- Estimation Results with Discounting -- Hedging with the Time-Varying Schwartz Model -- Connection with Term-Structure Models -- Models for Portfolios of Assets -- Bayesian Risk Management -- From Risk Measurement to Risk Management -- Results -- Time Series Analysis without Time-Invariance -- Preserving Prior Knowledge -- Information Transmission and Loss -- Bayesian State-Space Models of Time Series -- Real-Time Metrics for Model Risk -- Adaptive Estimates without Recalibration -- Prior Information as an Instrument of Corporate Governance
|
| Subject
|
:
|
Finance-- Mathematical models.
|
| Subject
|
:
|
Financial risk management-- Mathematical models.
|
| Subject
|
:
|
Bayesian statistical decision theory.
|
| Dewey Classification
|
:
|
332/.041501519542
|
| LC Classification
|
:
|
HG106
|
| Added Entry
|
:
|
Ohio Library and Information Network.
|