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Author: Katja Gisler Publisher: ISBN: Category : Languages : en Pages :
Book Description
Mit der Verfügbarkeit von Hochfrequenz-Daten sind neue Modellansätze für Varianzen und Kovarianzen entstanden, die so genannten realisierten Kovarianzmodelle. Während sich die meisten dieser realisierten Kovarianzmodelle auf einen univariaten Ansatz konzentrieren, verfolgt diese Masterarbeit das Ziel die gesamte Kovarianzmatrix zu modellieren. Infolgedessen präsentieren wir eine Erweiterung des univariaten heterogenen autoregressiven Modells für realisierte Volatilität (HAR-RV) von Corsi (2009), indem wir ein Vektor-HAR analog zum vektor-autoregressiven (VAR) Standard- Modell entwickeln und dabei die realisierte Bipower Kovariation bzw. Variation als konsistenten Schätzer für die integrierte Kovarianz bzw. Varianz verwenden. Wie andere multivariate Kovarianzmodelle leidet das multivariate HAR Modell für realisierte Bipower Variation (MHAR-BV) allerdings am sogenannten "Fluch der Dimensionalität". Wir reduzieren daher die Anzahl der Parameter anhand ökonometrischer Überlegungen. Das resultierende restringierte multivariate HAR-BV Modell zeigt, dass Kovarianzen bei der multivariaten Modelierung von realisierter Varianz eine signifikante Rolle spielen. Basierend auf dem restringierten Modell, führen wir anschliessend noch eine Spillover Analyse analog zu Diebold and Yilmaz (2009) durch. Diese Analyse zeigt nochmals auf, dass Spillovers entlang von realisierter Varianz und Kovarianz wichtig sind und insbesondere auf ökonomische Events reagieren, welche wir während der letzten Finanzkrise beobachten konnten.
Author: Katja Gisler Publisher: ISBN: Category : Languages : en Pages :
Book Description
Mit der Verfügbarkeit von Hochfrequenz-Daten sind neue Modellansätze für Varianzen und Kovarianzen entstanden, die so genannten realisierten Kovarianzmodelle. Während sich die meisten dieser realisierten Kovarianzmodelle auf einen univariaten Ansatz konzentrieren, verfolgt diese Masterarbeit das Ziel die gesamte Kovarianzmatrix zu modellieren. Infolgedessen präsentieren wir eine Erweiterung des univariaten heterogenen autoregressiven Modells für realisierte Volatilität (HAR-RV) von Corsi (2009), indem wir ein Vektor-HAR analog zum vektor-autoregressiven (VAR) Standard- Modell entwickeln und dabei die realisierte Bipower Kovariation bzw. Variation als konsistenten Schätzer für die integrierte Kovarianz bzw. Varianz verwenden. Wie andere multivariate Kovarianzmodelle leidet das multivariate HAR Modell für realisierte Bipower Variation (MHAR-BV) allerdings am sogenannten "Fluch der Dimensionalität". Wir reduzieren daher die Anzahl der Parameter anhand ökonometrischer Überlegungen. Das resultierende restringierte multivariate HAR-BV Modell zeigt, dass Kovarianzen bei der multivariaten Modelierung von realisierter Varianz eine signifikante Rolle spielen. Basierend auf dem restringierten Modell, führen wir anschliessend noch eine Spillover Analyse analog zu Diebold and Yilmaz (2009) durch. Diese Analyse zeigt nochmals auf, dass Spillovers entlang von realisierter Varianz und Kovarianz wichtig sind und insbesondere auf ökonomische Events reagieren, welche wir während der letzten Finanzkrise beobachten konnten.
Author: Luc Bauwens Publisher: John Wiley & Sons ISBN: 1118272056 Category : Business & Economics Languages : en Pages : 566
Book Description
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.
Author: Roxana Halbleib Publisher: ISBN: Category : Languages : en Pages : 40
Book Description
This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model.
Author: Mark Watson Publisher: Oxford University Press ISBN: 0199549494 Category : Business & Economics Languages : en Pages : 432
Book Description
A volume that celebrates and develops the work of Nobel Laureate Robert Engle, it includes original contributions from some of the world's leading econometricians that further Engle's work in time series economics
Author: Luc Bauwens Publisher: John Wiley & Sons ISBN: 0470872519 Category : Business & Economics Languages : en Pages : 566
Book Description
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.
Author: Gianluca Cubadda Publisher: ISBN: Category : Languages : en Pages : 27
Book Description
This paper compares the forecasting performances of both univariate and multivariate models for realized volatilities series. We consider realized volatility measures of the returns of 13 major banks traded in the NYSE. Since our variables are characterized by the presence of long range dependence, we use several modelling approaches that are able to capture such feature. We look at the forecasting accuracy of the considered models to make inference on the underlying mechanism that has generated volatilities of the assets. Our main conclusion is that the contagion effect among the considered volatilities is small or, at least, not well captured by the considered multivariate models.
Author: James H. Bray Publisher: SAGE ISBN: 9780803923102 Category : Mathematics Languages : en Pages : 84
Book Description
Bray's monograph considers the multivariate form of analysis of variance (MANOVA). It is a technique which can be used in such different academic disciplines as psychology, sociology, biology, and education.
Author: Andrew C. Harvey Publisher: Financial Times/Prentice Hall ISBN: 9780745012001 Category : Time-series analysis Languages : en Pages : 308
Book Description
A companion volume to The Econometric Analysis of Time series, this book focuses on the estimation, testing and specification of dynamic models which are not based on any behavioural theory. It covers univariate and multivariate time series and emphasizes autoregressive moving-average processes.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
Models based on Realized Volatility (RV) obtained from high-frequency returns have recently gained a lot of attraction. One very successful example is the heterogeneous autoregressive ("HAR")-model, which has also been shown to be suitable for forecasting Realized Correlation (RC). However, the HAR-model has not yet been used in a feasible, formally multivariate, setting. I will advocate a factor approach based on PCA as a parsimonious and tractable way for modelling RV and RC in a high-dimensional setting and provide evidence of the superiority of this approach compared to multiple univariate (SUR) models.
Author: Katja Gisler
Author: Katja Gisler
Author: Luc Bauwens
Author: Roxana Halbleib
Author: Mark Watson
Author: Robert Charles Lee (III.)
Author: Luc Bauwens
Author: Gianluca Cubadda
Author: James H. Bray
Author: Andrew C. Harvey
Author: