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Title:

An ACD-ECOGARCH(1,1) Model

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In this paper we introduce an ACD-ECOGARCH(1,1) model. An exponential autoregressive conditional duration model is used to describe the dependence structure in durations of ultra-high-frequency financial data. The innovation process of the ACD model then defines the interarrival times of a compound Poisson process. We use this compound Poisson p...

In this paper we introduce an ACD-ECOGARCH(1,1) model. An exponential autoregressive conditional duration model is used to describe the dependence structure in durations of ultra-high-frequency financial data. The innovation process of the ACD model then defines the interarrival times of a compound Poisson process. We use this compound Poisson process as the background driving Lévy process of an exponential continuous time GARCH(1,1) process. The dynamics of the random time transformed log-price process are then described by the latter process. To estimate its parameters we construct a quasi maximum likelihood estimator under the assumption that all jumps of the log-price process are observable. Finally, the model is fitted for illustrative purpose to General Motors tick-by-tick data of the New York Stock Exchange. Copyright The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oupjournals.org, Oxford University Press. Minimize

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article

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Title:

Statistical Models and Methods for Dependence in Insurance Data

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Abstract Copulas are becoming a quite flexible tool in modeling dependence among the components of a multivariate vector. In order to predict extreme losses in insurance and finance, extreme value copulas and tail copulas play a more important role than copulas. In this paper, we review some estimation and testing procedures for both extreme val...

Abstract Copulas are becoming a quite flexible tool in modeling dependence among the components of a multivariate vector. In order to predict extreme losses in insurance and finance, extreme value copulas and tail copulas play a more important role than copulas. In this paper, we review some estimation and testing procedures for both extreme value copulas and tail copulas, which received much less attention in the literature than corresponding studies of copulas. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2011-03-30

Source:

http://www-m4.ma.tum.de/Papers/Klueppelberg/HKP2010.pdf

http://www-m4.ma.tum.de/Papers/Klueppelberg/HKP2010.pdf Minimize

Document Type:

text

Language:

en

Subjects:

tail copula

tail copula Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

A FRACTIONALLY INTEGRATED ECOGARCH PROCESS

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In this paper we introduce a fractionally integrated exponential continuous time GARCH(p,d, q) process. It is defined in such a way that it is a continuous time extension of the discrete time FIEGARCH(p,d, q) process. We investigate stationarity and moment properties of the new model. It is also shown that the long memory effect introduced in th...

In this paper we introduce a fractionally integrated exponential continuous time GARCH(p,d, q) process. It is defined in such a way that it is a continuous time extension of the discrete time FIEGARCH(p,d, q) process. We investigate stationarity and moment properties of the new model. It is also shown that the long memory effect introduced in the log-volatility propagates to the volatility process. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-12-23

Source:

http://www-m4.ma.tum.de/pers/haug/HaugCzado2007.pdf

Document Type:

text

Language:

en

Subjects:

fractionally integrated ECOGARCH process ; long memory ; Lévy process ; stationarity ; stochastic volatility

fractionally integrated ECOGARCH process ; long memory ; Lévy process ; stationarity ; stochastic volatility Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

MULTIVARIATE ECOGARCH PROCESSES

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A multivariate extension of the exponential continuous time GARCH(p,q) model (ECOGARCH) is introduced and studied. Stationarity and mixing properties of the new stochastic volatility model are investigated and ways to model a component-wise leverage effect are presented.

A multivariate extension of the exponential continuous time GARCH(p,q) model (ECOGARCH) is introduced and studied. Stationarity and mixing properties of the new stochastic volatility model are investigated and ways to model a component-wise leverage effect are presented. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-12-23

Source:

http://www-m4.ma.tum.de/Papers/Haug/HaugStelzer2009.pdf

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Appl. Stochastic Models Bus. Ind., 2006; 22:243-267

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This is a preprint of an article accepted for publication in

This is a preprint of an article accepted for publication in Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://www-m4.mathematik.tu-muenchen.de/m4/Papers/Haug/paper440.pdf

http://www-m4.mathematik.tu-muenchen.de/m4/Papers/Haug/paper440.pdf Minimize

Document Type:

text

Language:

en

Subjects:

ultra high frequency ; CARMA ; mixed effect model ; state space ; Kalman filter

ultra high frequency ; CARMA ; mixed effect model ; state space ; Kalman filter Minimize

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First published in J. Appl. Probab. 44 (4) c○2007 by The Applied Probability Trust AN EXPONENTIAL CONTINUOUS TIME GARCH PROCESS

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In this paper we introduce an exponential continuous time GARCH(p,q) process. It is defined in such a way that it is a continuous time extension of the discrete time EGARCH(p,q) process. We investigate stationarity, mixing and moment properties of the new model. An instantaneous leverage effect can be shown for the exponential continuous time GA...

In this paper we introduce an exponential continuous time GARCH(p,q) process. It is defined in such a way that it is a continuous time extension of the discrete time EGARCH(p,q) process. We investigate stationarity, mixing and moment properties of the new model. An instantaneous leverage effect can be shown for the exponential continuous time GARCH(p,p) model. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-11-19

Source:

http://www-m4.ma.tum.de/pers/haug/ecogarch.pdf

http://www-m4.ma.tum.de/pers/haug/ecogarch.pdf Minimize

Document Type:

text

Language:

en

Subjects:

exponential continuous time GARCH process ; EGARCH ; Lévy process ; stationarity ; stochastic volatility

exponential continuous time GARCH process ; EGARCH ; Lévy process ; stationarity ; stochastic volatility Minimize

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Title:

Mixed effect model for absolute log returns of ultra high frequency data

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this paper given the past information G t i-1 = #(S t j , d t j ; j 1) and current duration d t i = t i t i-1 . Since the duration process is a stochastic process itself one also needs a model for this regularly spaced (measured in tick time) time series. A popular model for the durations given the past information, called Autoregressive Conditi...

this paper given the past information G t i-1 = #(S t j , d t j ; j 1) and current duration d t i = t i t i-1 . Since the duration process is a stochastic process itself one also needs a model for this regularly spaced (measured in tick time) time series. A popular model for the durations given the past information, called Autoregressive Conditional Duration (ACD) model, has been proposed by Engle and Russell (1997). There are a number of modifications of the ACD model, which are described for example in Bauwens et al. (2004) Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-19

Source:

http://www-m4.ma.tum.de/m4/Papers/Haug/paper440.ps

http://www-m4.ma.tum.de/m4/Papers/Haug/paper440.ps Minimize

Document Type:

text

Language:

en

Subjects:

ultra high frequency ; CARMA ; mixed effect model ; state space ; Kalman filter

ultra high frequency ; CARMA ; mixed effect model ; state space ; Kalman filter Minimize

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Title:

An Exponential Continuous Time Garch Process

Description:

In this paper we introduce an exponential continuous time GARCH(p, q) process. It is defined in such a way that it is a continuous time extension of the discrete time EGARCH(p, q) process. We investigate stationarity and moment properties of the new model. An instantaneous leverage e#ect can be shown for the exponential continuous time GARCH(p, ...

In this paper we introduce an exponential continuous time GARCH(p, q) process. It is defined in such a way that it is a continuous time extension of the discrete time EGARCH(p, q) process. We investigate stationarity and moment properties of the new model. An instantaneous leverage e#ect can be shown for the exponential continuous time GARCH(p, p) model. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-19

Source:

http://www-m4.ma.tum.de/m4/Papers/Haug/ecogarch.pdf

http://www-m4.ma.tum.de/m4/Papers/Haug/ecogarch.pdf Minimize

Document Type:

text

Language:

en

Subjects:

exponential continuous time GARCH process ; EGARCH ; Lévy process ; stationarity ; stochastic volatility

exponential continuous time GARCH process ; EGARCH ; Lévy process ; stationarity ; stochastic volatility Minimize

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Title:

A Fractionally Integrated Ecogarch Process

Description:

In this paper we introduce a fractionally integrated exponential continuous time GARCH(p, d, q) process. It is defined in such a way that it is a continuous time extension of the discrete time FIEGARCH(p, d, q) process. We investigate stationarity and moment properties of the new model. It is also shown that the long memory e#ect introduced in t...

In this paper we introduce a fractionally integrated exponential continuous time GARCH(p, d, q) process. It is defined in such a way that it is a continuous time extension of the discrete time FIEGARCH(p, d, q) process. We investigate stationarity and moment properties of the new model. It is also shown that the long memory e#ect introduced in the log-volatility propagates to the volatility process. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-19

Source:

http://www-m4.ma.tum.de/m4/Papers/Haug/fiecogarch.pdf

http://www-m4.ma.tum.de/m4/Papers/Haug/fiecogarch.pdf Minimize

Document Type:

text

Language:

en

Subjects:

fractionally integrated ECOGARCH process ; long memory ; Lévy process ; stationarity ; stochastic volatility

fractionally integrated ECOGARCH process ; long memory ; Lévy process ; stationarity ; stochastic volatility Minimize

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Title:

Dimension reduction based on extreme dependence

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We introduce a dimension reduction technique based on extreme observations. The classical assumption of a linear model for the distribution of a random vector is replaced by the weaker assumption of a fairly general model for the copula. We assume an elliptical copula to describe the extreme dependence structure, which preserves a ’correlation-l...

We introduce a dimension reduction technique based on extreme observations. The classical assumption of a linear model for the distribution of a random vector is replaced by the weaker assumption of a fairly general model for the copula. We assume an elliptical copula to describe the extreme dependence structure, which preserves a ’correlation-like’ structure in the extremes. Based on the tail dependence function we estimate the copula correlation matrix, which is then analysed through classical dimension reduction techniques. After introducing the new concepts and deriving some theoretical results we observe in a simulation study the performance of the estimator. Finally, we test our method on real financial data and explain differences between our copula based approach and the classical approach. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2011-02-18

Source:

http://www-m4.ma.tum.de/Papers/Haug/excopstruc100514.pdf

http://www-m4.ma.tum.de/Papers/Haug/excopstruc100514.pdf Minimize

Document Type:

text

Language:

en

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