Temporal Aggregation of Multivariate GARCH Processes PDF Download
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Author: Christian M. Hafner
Publisher:
ISBN:
Category :
Languages : en
Pages : 43
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Author: Christian M. Hafner
Publisher:
ISBN:
Category :
Languages : en
Pages : 43
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Author: Feike C. Drost
Publisher:
ISBN:
Category : Heteroscedasticity
Languages : en
Pages : 54
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Author: Theo Nijman
Publisher:
ISBN: 9788477933359
Category : Time-series analysis
Languages : en
Pages : 46
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Author: Binay Kumar Ray
Publisher:
ISBN:
Category :
Languages : en
Pages : 8
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Book Description
Beginning with the mean variance analysis of portfolio and asset returns, volatility has become central to much of modern finance theory. In recent times, empirical work involving high frequency financial time series data has focused on volatility of asset return. It has been observed that the asset returns exhibit changes, which are not independent over time. Rather, large changes tend to followed by large changes of either sign - small changes tend to be followed by small changes. That is, big shocks are clustered together. GARCH models are used to parameterize conditional heteroskedasticity. It is little known about the impact of temporal aggregation upon GARCH process that conditional heteroskedasticity disappears if the sampling time interval increases to infinity (Drost and Nijman (July 1993). Important applications for persistence variance in GARCH (1,1) model are represented by sum of the coefficients lagged squared disturbance and that of past variance coefficients b1.
Author: Andrea Silvestrini
Publisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 68
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Author: Thomas Breuer
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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We examine the properties of temporally aggregated distributions when one period changes follow a strong GARCH process. Our main results: (1) We derive explicit expressions for the conditional volatility and kurtosis of the aggregated distribution. (2) As the time horizon gets longer the conditional aggregated kurtosis approaches three (resp. a different constant, for stock variables) or infinity depending on whether or not a simple inequality in term of the GARCH parameters is satisfied. (3) Given that the aggregation of a strong GARCH process is not any more a strong GARCH process, the question arises for which data frequency a description by a strong GARCH process fits the data best. We propose a quasi maximum likelihood method to determine the optimal data frequency for a GARCH description. (4) For models with different basic frequency and with different residual distributions we perform out of sample tests of three months density forecasts on the basis of daily market prices. It turns out that low frequency models with longer basic periods and fewer aggregation steps fare better than high frequency models. This seems to imply that for high frequency models the advantage of having more data available for estimation is outweighed by the disadvantage of aggregation magnifying estimation errors.
Author: Christian Francq
Publisher: John Wiley & Sons
ISBN: 1119957397
Category : Mathematics
Languages : en
Pages : 469
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Book Description
This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation and tests. The book also provides coverage of several extensions such as asymmetric and multivariate models and looks at financial applications. Key features: Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. Numerous illustrations and applications to real financial series are provided. Supporting website featuring R codes, Fortran programs and data sets. Presents a large collection of problems and exercises. This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
Author: Dominique M. Guillaume
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
In this paper, we show that the use of an alternative time scale can eliminate the inefficiencies in the estimation of a GARCH model caused by intra-daily seasonal patterns. Even so, however, the temporal aggregation properties of the GARCH model do not hold at the intra-daily frequencies, revealing the presence of several time-horizons components. Besides, distinct characteristics were identified in the very short (less than 2 hours) and the very long (several months) run. Finally, the out-of-sample predictive power of GARCH for the volatility was found to be lower than the historical volatility itself implying the presence of other sources of heterogeneity.
Author: Robert A. Meyers
Publisher: Springer Science & Business Media
ISBN: 1441977007
Category : Business & Economics
Languages : en
Pages : 919
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Book Description
Finance, Econometrics and System Dynamics presents an overview of the concepts and tools for analyzing complex systems in a wide range of fields. The text integrates complexity with deterministic equations and concepts from real world examples, and appeals to a broad audience.
Author: John Geweke
Publisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 47
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