Journal ArticleDOI
Modeling the dependence of conditional correlations on volatility
Luc Bauwens,Edoardo Otranto +1 more
TLDR
In this paper, the authors investigated the determinants of the correlation dynamics between time series of financial returns and found that the market volatility is a major determinant of the correlations, which can be transmitted through linear or nonlinear, and direct or indirect effects, and applied to different data sets to verify the presence and possible regularity of the volatility impact on correlations.Abstract:
Several models have been developed to capture the dynamics of the conditional correlations between time series of financial returns, but few studies have investigated the determinants of the correlation dynamics. A common opinion is that the market volatility is a major determinant of the correlations. We extend some models to capture explicitly the dependence of the correlations on the volatility of the market of interest. The models differ in the way by which the volatility influences the correlations, which can be transmitted through linear or nonlinear, and direct or indirect effects. They are applied to different data sets to verify the presence and possible regularity of the volatility impact on correlations.read more
Citations
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Journal ArticleDOI
Structural breaks and volatility forecasting in the copper futures market
Xu Gong,Boqiang Lin +1 more
TL;DR: In this article, the authors examined whether structural breaks contain incremental information for forecasting the volatility of copper futures and developed four heterogeneous autoregressive (HAR) models based on classical or latest HAR-type models.
Journal ArticleDOI
Forecasting realized volatility of crude oil futures with equity market uncertainty
TL;DR: In this article, the authors examined whether the EMU index contains incremental information for forecasting the realized volatility of crude oil futures, using a 5-min high-frequency tran...
Journal ArticleDOI
MGARCH models: Trade-off between feasibility and flexibility
TL;DR: The authors analyzes the limitations of some of the popular restricted parametric MGARCH models that are often used to represent the dynamics observed in real systems of financial returns and shows that the restrictions imposed by the BEKK model are very unrealistic, generating potentially misleading forecasts of conditional correlations.
Journal ArticleDOI
Estimation and empirical performance of non-scalar dynamic conditional correlation models
TL;DR: Empirical results show that the use of richly parametrized models adds value with respect to the conventional scalar case.
Posted Content
A Multivariate GARCH Model with Time-Varying Correlations
Yiu Kuen Tse,Albert K. Tsui +1 more
TL;DR: In this article, a multivariate GARCH model with time-varying correlations is proposed, where each conditional-variance term is assumed to follow an autoregressive moving average type of analogue.
References
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Journal ArticleDOI
On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks
TL;DR: In this article, a modified GARCH-M model was used to find a negative relation between conditional expected monthly return and conditional variance of monthly return, using seasonal patterns in volatility and nominal interest rates to predict conditional variance.
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Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models
TL;DR: In this article, a new class of multivariate models called dynamic conditional correlation models is proposed, which have the flexibility of univariate generalized autoregressive conditional heteroskedasticity (GARCH) models coupled with parsimonious parametric models for the correlations.
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Maximum likelihood estimation of misspecified models
TL;DR: In this article, the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference are examined, and the properties of the quasi-maximum likelihood estimator and the information matrix are exploited to yield several useful tests.
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Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model.
TL;DR: In this article, a multivariate time series model with time varying conditional variances and covariances but with constant conditional correlations is proposed, which is readily interpreted as an extension of the seemingly unrelated regression (SUR) model allowing for heteroskedasticity.
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No Contagion, Only Interdependence: Measuring Stock Market Comovements
TL;DR: The authors showed that correlation coefficients are conditional on market volatility, and that there was virtually no increase in unconditional correlation coefficients (i.e., no contagion) during the 1997 Asian crisis, 1994 Mexican devaluation, and 1987 U.S. market crash.