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Modelling multivariate autoregressive conditional heteroskedasticity with double smooth transition conditional correlation GARCH model

Silvennoinen, Annastiina (contributor); … - 2007

In this paper we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new Double Smooth Transition Conditional Correlation GARCH model extends the Smooth Transition Conditional Correlation GARCH model of Silvennoinen and Teräsvirta (2005) by including...

Persistent link: https://www.econbiz.de/10003411196

Tail-dependence in stock-return pairs

Fortin, Ines; Kuzmics, Christoph - 2002

The empirical joint distribution of return-pairs on stock indices displays high tail-dependence in the lower tail and low tail-dependence in the upper tail. The presence of tail-dependence is not compatible with the assumption of (conditional) joint normality. The presence of asymmetric-tail...

Persistent link: https://www.econbiz.de/10009725481

Testing for vector autoregressive dynamics under heteroskedasticity

Hafner, Christian M.; Herwartz, Helmut - 2002

In this paper we introduce a bootstrap procedure to test parameter restrictions in vector autoregressive models which … is robust in cases of conditionally heteroskedastic error terms. The adopted wild bootstrap method does not require any … Monte Carlo investigation empirical size and power properties of the new method are illustrated. We compare the bootstrap …

Persistent link: https://www.econbiz.de/10009663846

Testing for a common volatility process and information spillovers in bivariate financial time series models

Chen, Jinghui; Kobayashi, Masahito; McAleer, Michael - 2016 - Revised: February 2016

The paper considers the problem as to whether financial returns have a common volatility process in the framework of stochastic volatility models that were suggested by Harvey et al. (1994). We propose a stochastic volatility version of the ARCH test proposed by Engle and Susmel (1993), who...

Persistent link: https://www.econbiz.de/10011441709

Stable-GARCH models for financial returns : fast estimation and tests for stability

Paolella, Marc S. - In: Econometrics : open access journal 4 (2016) 2, pp. 1-28

A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration is proposed. Several powerful tests for the (asymmetric) stable Paretian distribution with tail index 1 α 2 are used for assessing the appropriateness of the stable assumption as the...

Persistent link: https://www.econbiz.de/10011506322

Stock returns and risk : evidence from quantile

Chiang, Thomas C.; Li, Jiandong - In: Journal of risk and financial management : JRFM 5 (2012) 1, pp. 20-58

This paper employs weighted least squares to examine the risk-return relation by applying high-frequency data from four major stock indexes in the US market and finds some evidence in favor of a positive relation between the mean of the excess returns and expected risk. However, by using...

Persistent link: https://www.econbiz.de/10011555867

Multivariate autoregressive conditional heteroskedasticity with smooth transitions in conditional correlations

Silvennoinen, Annastiina (contributor); … - 2005 - [Elektronische Ressource], Rev. October 2005

In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The approach adopted here is based on the decomposition of the covariances into correlations and standard deviations. The time-varying conditional correlations change smoothly between two...

Persistent link: https://www.econbiz.de/10002570445

Testing for volatility interactions in the constant conditional correlation GARCH model

Nakatani, Tomoaki (contributor); … - 2007 - Rev. May 4, 2008

In this paper we propose a Lagrange multiplier test for volatility interactions among markets or assets. The null hypothesis is the Constant Conditional Correlation GARCH model in which volatility of an asset is described only through lagged squared innovations and volatility of its own. The...

Persistent link: https://www.econbiz.de/10003400410

Specification and estimation of random effects models with serial correlation of general form

Skoglund, Jimmy (contributor); Karlsson, Sune (contributor) - 2001 - [Elektronische Ressource]

This paper is concerned with maximum likelihood based inference in random effects models with serial correlation. Allowing for individual effects we introduce serial correlation of general form in the time effects as well as the idiosyncratic errors. A straightforward maximum likelihood...

Persistent link: https://www.econbiz.de/10001600058

Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model

Silvennoinen, Annastiina - 2010

In this paper, we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new double smooth transition conditional correlation (DSTCC) GARCH model extends the smooth transition conditional correlation (STCC) GARCH model of Silvennoinen and Teräsvirta (2005)...

Persistent link: https://www.econbiz.de/10013150666