Handbook of Financial Time Series / Edition 1

Handbook of Financial Time Series / Edition 1

ISBN-10:
3540712968
ISBN-13:
9783540712961
Pub. Date:
05/20/2009
Publisher:
Springer Berlin Heidelberg
ISBN-10:
3540712968
ISBN-13:
9783540712961
Pub. Date:
05/20/2009
Publisher:
Springer Berlin Heidelberg
Handbook of Financial Time Series / Edition 1

Handbook of Financial Time Series / Edition 1

Hardcover

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Overview

This handbook presents a collection of survey articles from a statistical as well as an econometric point of view on the broad and still rapidly developing field of financial time series. It includes most of the relevant topics in the field, from fundamental probabilistic properties of financial time series models to estimation, forecasting, model fitting, extreme value behavior and multivariate modeling for a wide range of GARCH, shastic volatility, and continuous-time models. The latter are especially important for modeling high frequency and irregularly observed financial time series and provide the foundation for estimating realized volatility. Cointegration and unit roots, which are extremely important concepts for understanding and modeling nonstationary time series, and several further relevant topics in the field of financial time series (i.e. nonparametric methods, copulas, structural breaks, high frequency data, resampling and bootstrap methods, and model selection for financial time series among others) are included in detail. All contributions are clearly written and provide, in a pedagogical manner, a broad and detailed overview of the major topics within financial time series.


Product Details

ISBN-13: 9783540712961
Publisher: Springer Berlin Heidelberg
Publication date: 05/20/2009
Edition description: 2009
Pages: 1050
Product dimensions: 6.40(w) x 9.50(h) x 1.60(d)

Table of Contents

Recent Developments in GARCH Modeling.- An Introduction to Univariate GARCH Models.- Stationarity, Mixing, Distributional Properties and Moments of GARCH(p, q)#x2013;Processes.- ARCH(#x221E;) Models and Long Memory Properties.- A Tour in the Asymptotic Theory of GARCH Estimation.- Practical Issues in the Analysis of Univariate GARCH Models.- Semiparametric and Nonparametric ARCH Modeling.- Varying Coefficient GARCH Models.- Extreme Value Theory for GARCH Processes.- Multivariate GARCH Models.- Recent Developments in Shastic Volatility Modeling.- Shastic Volatility: Origins and Overview.- Probabilistic Properties of Shastic Volatility Models.- Moment#x2013;Based Estimation of Shastic Volatility Models.- Parameter Estimation and Practical Aspects of Modeling Shastic Volatility.- Shastic Volatility Models with Long Memory.- Extremes of Shastic Volatility Models.- Multivariate Shastic Volatility.- Topics in Continuous Time Processes.- An Overview of Asset–Price Models.- Ornstein–Uhlenbeck Processes and Extensions.- Jump–Type Lévy Processes.- Lévy–Driven Continuous–Time ARMA Processes.- Continuous Time Approximations to GARCH and Shastic Volatility Models.- Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance.- Parametric Inference for Discretely Sampled Shastic Differential Equations.- Realized Volatility.- Estimating Volatility in the Presence of Market Microstructure Noise: A Review of the Theory and Practical Considerations.- Option Pricing.- An Overview of Interest Rate Theory.- Extremes of Continuous–Time Processes..- Topics in Cointegration and Unit Roots.- Cointegration: Overview and Development.- Time Series with Roots on or Near the Unit Circle.- Fractional Cointegration.- Special Topics –Risk.- Different Kinds of Risk.- Value–at–Risk Models.- Copula–Based Models for Financial Time Series.- Credit Risk Modeling.- Special Topics – Time Series Methods.- Evaluating Volatility and Correlation Forecasts.- Structural Breaks in Financial Time Series.- An Introduction to Regime Switching Time Series Models.- Model Selection.- Nonparametric Modeling in Financial Time Series.- Modelling Financial High Frequency Data Using Point Processes.- Special Topics – Simulation Based Methods.- Resampling and Subsampling for Financial Time Series.- Markov Chain Monte Carlo.- Particle Filtering.
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