Luc Bauwens

Christian M. Hafner

Sebastien Laurent

A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.

Handbook of Volatility Models and Their Applications

Michel Lubrano

Jean-François Richard

This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.

Bayesian Inference in Dynamic Econometric Models

Professor of Economics Centre for Operations Research and Econometrics [Core] Luc Bauwens

Pierre Giot

The recent widespread availability of intraday tick-by-tick databases for stocks, options and currencies has had an important impact on research in applied financial econometrics and market microstructure. Econometric Modelling of Stock Market Intraday Activity focuses on the econometric modelling of intraday tick-by-tick transaction data (trades and quote) for stock traded on the New York Stock Exchange (NYSE). Recent quantitative modelling tools such as intraday duration models and GARCH modes are presented. A survey of trading mechanisms in financial markets and a review of market microstructure issues is also included, which allows to gain a better understanding of the motivation underlying the use of the quantitative models. In the empirical applications, the link is made with the models of the market microstructure literature that have proposed an explicit treatment of time in the trading process. Other empirical applications deal with the modelling of intraday volatility and intraday Value-at-Risk. Although the models are applied to data for stock traded on the NYSE, they are not specific to this exchange and could be used to analyze other existing trading mechanisms. Accordingly, this book should be of interest to academics and graduate students involved in empirical finance and applied econometrics, regulators working for exchanges, and practitioners in banks or brokerage firms.

Luc Bauwens

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