Multivariate Rotated ARCH Models
By Diaa Noureldin, N. Shephard, Kevin Sheppard. 2014
51 citations recorded by the source corpus.
This paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to fit them using a BEKK-type parameterization of the time-varying covariance whose long-run covariance is the identity matrix. This yields the rotated BEKK (RBEKK) model. The extension to DCC-type parameterizations is given, introducing the rotated DCC (RDCC) model. Inference for these models is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on the DJIA stocks.
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/446896ebf35963f51f7c65872d9df35ab0ec6b19
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Citations in the corpus
- Multivariate Variance Targeting in the BEKK-GARCH Model (2014, unknown): “…so that the unconditional covariance matrix of Xt appears explicitly in the equation for Ht. Substituting (2.3) into (2.2) yields Ht = Γ− AΓA′ −BΓB′ + AXt−1X ′t−1A′ +BHt−1B′, (2.4) and we say that Ht has the variance targeting BEKK representation, see also Noureldin, Shephard, and Sheppard (2012).”
- Forecasting Value-at-Risk under Different Distributional Assumptions (2016, methodology): “[15], as it can be estimated for relatively large cross sections even with reach dynamics.”
- MGARCH models: Trade-off between feasibility and flexibility (2018, methodology): “Noureldin et al. (2014) also study restricted versions of the RBEKK model denoted as diagonal RBEKK (D-RBEKK) and scalar RBEKK (S-RBEKK) models.”