Results 211 to 220 of about 3,197 (255)

Random coefficient GARCH models

Mathematical and Computer Modelling, 2005
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A Thavaneswaran, S S Appadoo, M Samanta
exaly   +3 more sources

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ON MIXTURE MEMORY GARCH MODELS

Journal of Time Series Analysis, 2013
We propose a new volatility model, which is called the mixture memory generalized autoregressive conditional heteroskedasticity (MM‐GARCH) model. The MM‐GARCH model has two mixture components, of which one is a short‐memory GARCH and the other is the long‐memory fractionally integrated GARCH.
Li, M, Li, WK, Li, G
openaire   +4 more sources

A multivariate skew-garch model

2005
Empirical research on European stock markets has shown that they behave differently according to the performance of the leading financial market identified as the US market. A positive sign is viewed as good news in the international financial markets, a negative sign means, conversely, bad news.
DE LUCA, GIOVANNI, LOPERFIDO N, GENTON M.   +2 more
openaire   +3 more sources

A tobit model with garch errors [PDF]

Econometric Reviews, 1998
In the context of time series regression, we extend the standard Tobit model to allow for the possibility of conditional heteroskedastic error processes of the GARCH type. We discuss the likelihood function of the Tobit model in the presence of conditionally heteroskedastic errors.
CALZOLARI, GIORGIO, FIORENTINI, GABRIELE
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GARCH-type factor model

Journal of Multivariate Analysis, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yuanbo Li, Chi Tim Ng, Chun Yip Yau
openaire   +1 more source

THE GARCH OPTION PRICING MODEL

Mathematical Finance, 1995
This article develops an option pricing model and its corresponding delta formula in the context of the generalized autoregressive conditional heteroskedastic (GARCH) asset return process. the development utilizes the locally risk‐neutral valuation relationship (LRNVR).
Jin-Chuan Duan
exaly   +3 more sources

EMPIRICAL LIKELIHOOD FOR GARCH MODELS

Econometric Theory, 2006
Summary: This paper develops an empirical likelihood approach for regular generalized autoregressive conditional heteroskedasticity (GARCH) models and GARCH models with unit roots. For regular GARCH models, it is shown that the log empirical likelihood ratio statistic asymptotically follows a \(\chi^2\) distribution.
Chan, NH, Ling, SQ
openaire   +3 more sources

Bayesian Clustering of Many GARCH Models [PDF]

Econometric Reviews, 2005
We consider the estimation of a large number of GARCH models, of the order of several hundreds. To achieve parsimony, we classify the series in a small number of groups. Within a cluster, the series share the same model and the same parameters. Each cluster contains therefore similar series. We do not know a priori which series belongs to which cluster.
BAUWENS, Luc, ROMBOUTS, Jeroen
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GO‐GARCH: a multivariate generalized orthogonal GARCH model

Journal of Applied Econometrics, 2002
AbstractMultivariate GARCH specifications are typically determined by means of practical considerations such as the ease of estimation, which often results in a serious loss of generality. A new type of multivariate GARCH model is proposed, in which potentially large covariance matrices can be parameterized with a fairly large degree of freedom while ...
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Adaptive Filtering for GARCH Models

2002
The volatility of a speculative asset is a fundamental ingredient of many financial pricing algorithms, therefore, accurate forecasts of volatility are essential to financial practioners. Autoregressive Conditional Heteroscekdastic models and their generalisations (GARCH) have been shown to provide reasonable forecasts of volatility with relatively few
Paul E. Lynch, Nigel M. Allinson
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