12

543-600

58

 

John Wiley & Sons, Inc. (Visit publisher website)

2005

English

 

 

 

Advanced

 

 

Quantitative analysis, estimation, time series analysis, simulation, stochastic processes, Markov process, Markov chain simulation, Gibbs sampling, Bayesian inference, posterior distributions, conjugate prior distributions, Metropolis algorithm, Metropolis–Hasting algorithm, Griddy Gibbs sampler, linear regression with time series errors, missing values and outliers, missing values, outlier detection, stochastic volatility models, estimation of univariate models, multivariate stochastic volatility models, forward filtering and backward sampling (FFBS), Markov switching models

 

This chapter explains a Bayesian computational toolbox for a wide range of empirical finance models. It starts with the basic idea of Markov Chain simulation and its relationship with Bayesian Inference. While the discussion mainly focuses at the Gibbs Sampler, some alternative ways such as Metropolis and griddy Gibbs are also covered. The chapter effectively uses examples of practical and theoretical interest, including linear regression with time series errors, univariate and multivariate stochastic volatility models, Markov switching models and outliers detection (for interest rate data). The chapter ends with the algorithm of forward filtering and backward sampling which is particularly effective for stochastic volatility models. This reading assumes knowledge of integral calculus, Bayes Theorem and elementary Bayesian inference.

 

12.1 Markov Chain Simulation
12.2 Gibbs Sampling
12.3 Bayesian Inference
12.3.1 Posterior Distributions
12.3.2 Conjugate Prior Distributions
12.4 Alternative Algorithms
12.4.1 Metropolis Algorithm
12.4.2 Metropolis–Hasting Algorithm
12.4.3 Griddy Gibbs
12.5 Linear Regression with Time Series Errors
12.6 Missing Values and Outliers
12.6.1 Missing Values
12.6.2 Outlier Detection
12.7 Stochastic Volatility Models
12.7.1 Estimation of Univariate Models
12.7.2 Multivariate Stochastic Volatility Models
12.8 A New Approach to SV Estimation
12.9 Markov Switching Models
12.10 Forecasting
12.11 Other Applications
Exercises
References

 

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The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito`s Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods.

The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics:

* Analysis and application of univariate financial time series
* Return series of multiple assets
* Bayesian inference in finance methods

This new edition is a thoroughly revised and updated text, including the addition of S-Plus® commands and illustrations. Among the new material, readers will find:

* Consistent covariance estimation under heteroscedasticity and serial correlation
* Alternative approaches to volatility modeling
* Financial factor models
* State-space models
* Kalman filtering
* Estimation of stochastic diffusion models

This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.