Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
The Handbook of Financial Time Series provides an up-to-date overview of the field and covers all relevant topics, both from a statistical and an econometrical point of view. Experts present, among others various aspects, the important GARCH and Stochastic Volatility classes, like for example distribution properties, estimation, forecasting and extreme value theory. The book also details processes in continuous time and cointegration since both play a very essential role in financial modeling. In addition, recent developments in nonparametric methods, copulas, structural breaks, high frequency data and many more topics are included in the handbook. Many outstanding authors have contributed to this encyclopedia, making the volume an excellent source of reference for scientists and researchers working in the field of financial time series.
Reviews / Votes
From the reviews:
"Academic researchers and graduate students in statistics, economics and financial engineering, Industry banking, investments and insurance. . The handbook is clearly written and provides a broad and detailed overview of the major topics within financial time series. . serves as a good reference for the financial time series methods and will be invaluable to many researchers. It also excels in giving very clear and concise description of a number of important methodologies." (Lasse Koskinen, International Statistical Review, Vol. 78 (1), 2010)
Series
Edition
Language
Place of publication
Publishing group
Target group
Professional and scholarly
Illustrations
File size
ISBN-13
978-3-540-71297-8 (9783540712978)
DOI
10.1007/978-3-540-71297-8
Schweitzer Classification
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 Stochastic Volatility Modeling.- Stochastic Volatility: Origins and Overview.- Probabilistic Properties of Stochastic Volatility Models.- Moment#x2013;Based Estimation of Stochastic Volatility Models.- Parameter Estimation and Practical Aspects of Modeling Stochastic Volatility.- Stochastic Volatility Models with Long Memory.- Extremes of Stochastic Volatility Models.- Multivariate Stochastic 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 Stochastic Volatility Models.- Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance.- Parametric Inference for Discretely Sampled Stochastic 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.
