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Abstract

A realized covariance model specifies a dynamic process for a conditional covariance matrix of daily asset returns as a function of past realized variances and covariances. We propose parsimonious parameterizations enabling a spillover effect in the conditional variance equations, and a specific nonlinear, time-varying, effect of the lagged realized covariance between each asset pair on the corresponding conditional covariance. We introduce these parameterizations in four classes of realized covariance models. In an application to the components of the Dow Jones index, we find that the extended models improve the fit of their less flexible scalar versions and show a good out-of-sample forecast performance, in particular for short forecast horizons.

Keywords

attenuation effect forecasting realized volatility spillover effect time-varying effect

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References

Andersen

, Bollerslev

and Diebold

(2007) Roughing it up: including jump components in the measurement, modeling and forecasting of return volatility. Review of Economics and Statistics , 89, 701–20.

Bauwens

and Otranto

(2016) Modeling the dependence of conditional correlations on market volatility. Journal of Business & Economic Statistics, 34, 254–68.

Bauwens

and Otranto

(2020) Nonlinearities and regimes in conditional correlations with different dynamics. Journal of Econometrics , 217, 496–522.

Bauwens

and Otranto

(2023) Modeling realized covariance matrices: a class of Hadamard exponential models. Journal of Financial Econometrics , 21, 1376–1401. https://doi.org/10.1093/jjfinec/nbac007.

Bauwens

, Laurent

and Rombouts

(2006) Multivariate GARCH models: a survey. Journal of Applied Econometrics , 21, 79–109.

Bauwens

, Storti

and Violante

(2012) Dynamic conditional correlation models for realized covariance matrices. CORE Discussion Paper 2012-60, Université catholique de Louvain, Louvain La Neuve.

Bauwens

and Xu

(2023) DCC- and DECO-HEAVY: multivariate GARCH models based on realized variances and correlations. International Journal of Forecasting , 39, 938–55.

Bollerslev

, Patton

and Quaedvlieg

(2018) Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions. Journal of Econometrics , 207, 71–91.

Cipollini

, Gallo

and Otranto

(2021) Realized volatility forecasting: robustness to measurement errors. International Journal of Forecasting , 37, 44–57.

10.

Corsi

(2009) A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics , 7(2), 174–96.

11.

Engle

(2009) Anticipating correlations . Princeton University Press.

12.

Engle

and Kroner

(1995) Multivariate simultaneous generalized ARCH. Econometric Theory , 11, 122–50.

13.

Engle

(2002) Dynamic conditional correlation: a simple class of multivariate GARCH models. Journal of Business & Economic Statistics , 20, 339–50.

14.

Forbes

and Rigobon

(2002) No contagion, only interdependence: measuring stock market comovement. Journal of Finance , 57, 2223–61.

15.

Gallo

and Otranto

(2015) Forecasting realized volatility with changing average levels. International Journal of Forecasting , 31, 620–34.

16.

Gallo

and Otranto

(2018) Combining sharp and smooth transitions in volatility dynamics: a fuzzy regime approach. Journal of the Royal Statistical Society-Series C-Applied Statistics , 67, 549–73.

17.

Glosten

, Jagannathan

and Runkle

(1993) On the relation between expected value and the volatility of the nominal excess return on stocks. Journal of Finance , 48, 17791801.

18.

Golosnoy

, Gribisch

and Liesenfeld

(2012) The conditional autoregressive Wishart model for multivariate stock market volatility. Journal of Econometrics , 167(1), 211–23.

19.

Hansen

, Lunde

and Nason

(2003) Choosing the best volatility models: the model confidence set approach. Oxford Bulletin of Economic and Statistics , 65, 839–61.

20.

Hansen

and Timmermann

(2015) Comment on F.X. Diebold: comparing predictive accuracy: twenty years later. A personal perspective on the use and abuse of DieboldMariano Tests. Journal of Business and Economic Statistics , 33(1), 17–21.

21.

Hansen

, Lunde

and Nason

(2011) The model confidence set. Econometrica , 79(2), 453–97.

22.

Hansen

and Lunde

(2006) Consistent ranking of volatility models. Journal of Econometrics , 131(1), 97–121.

23.

Hansen

(2010) A winner's curse for econometric models: on the joint distribution of in-sample fit and out-of-sample fit and its implications for model selection. Mimeo, Stanford University.

24.

Laurent

, Rombouts

and Violante

(2013) On loss functions and ranking forecasting performances of multivariate volatility models. Journal of Econometrics , 173(1), 1–10.

25.

and Patton

(2016) High-dimensional copula-based distributions with mixed frequency data. Journal of Econometrics , 193, 349–66.

26.

Pelletier

(2006) Regime switching for dynamic correlations. Journal of Econometrics , 131, 445–73.

27.

Reams

(1999) Hadamard inverses, square roots and products of almost semidefinite matrices. Linear Algebra and its Applications , 288, 35–43.

28.

Scaffidi Domianello

and Otranto

(2023) Long and short run dynamics in realized covariance matrices: a robust MIDAS approach. In Kitsos

, Oliveira

, Pierri

and Restaino

, editors, Statistical modelling and risk analysis , pages 169–86. Springer.

29.

Takeuchi

(1976) Distribution of information statistics and validity criteria of models. Mathematical Science , 153, 12–8.

30.

Tsay

(2014) Multivariate time series analysis: with R and financial applications . Wiley.

31.

Xie

and Yu

(2020) Realized GARCH models: simpler is better. Finance Research Letters , 33, 101221.

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Realized covariance models with time-varying parameters and spillover effects

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