Multivariate High-Frequency-Based Volatility (HEAVY) Models

By Diaa Noureldin, N. Shephard, Kevin Sheppard. 2012

222 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/e07662a02d79247f2d7aebac75cd66bc7bfe4976

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Citations in the corpus

Multivariate Rotated ARCH Models (2014, unknown): “Noureldin et al. (2011) propose a margins-copula predictive likelihood decomposition to assess the t of multivariate volatility models by separating the gains/losses due to the forecast variances and correla- tions.”
Pricing under rough volatility (2015, unknown): “Integrated variance forecasts must therefore be rescaled to obtain close-to-close integrated variance forecasts as explained for example in Corsi et al. (2013) or alternatively using an econometric model such as the HEAVY model of Noureldin et al. (2012).”
A New Parametrization of Correlation Matrices (2020, unknown): “Such as those in Liu (2009), Chiriac and Voev (2011), Golosnoy et al. (2012), Bauwens et al. (2012), Noureldin et al. (2012), Hansen et al. (2014), and Gorgi et al. (2019).”
The Impact of Volatility Targeting (2018, unknown): “In addition, advances in statistical modeling may help us to use the intraday data to get more timely estimates; see, for example, Noureldin, Shephard, and Sheppard (2012) for a discussion on multivariate highfrequency-based volatility models.”
Modeling Realized Covariances and Returns (2013, methodology): “The second model is a dynamic conditional correlation (DCC-t) model of Engle (2002) with Student-t innovations, rt|r1:t−1 ∼ t(0, Ht, ζ) (34) Ht = DtRtDt (35) Dt = diag(σi,t) (36) σ2i,t = ωi + κir 2 i,t−1 + λiσ 2 i,t−1, i = 1, . . . , k (37) t = ( ζ − 2 ζ )1/2 D−1t rt (38) Qt = Q(1− α− β) + α t−1 ′t−”
Exploiting the errors: A simple approach for improved volatility forecasting (2016, methodology): “The multivariate HEAVY model of Noureldin et al. (2012) and the Realized Beta GARCH model of Hansen et al. (2014) may similarly be extended to allow the parameters of the models to vary with the degree of the measurement errors in the realized covariance matrix.”
Bayesian semiparametric modeling of realized covariance matrices (2016, background): “Examples of this approach include the multivariate high-frequency-based volatility (HEAVY) model of Noureldin et al. (2012) which exploits ex post volatility measures in a GARCH-like setting.”
Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice (2016, background): “In contrast, Noureldin, Shephard, and Sheppard (2012) and Hansen, Lunde, and Voev (2014) developed frameworks for forecasting the covariance of daily returns which also involves building a model to forecasts future realized measures.”
High-Dimensional Copula-Based Distributions with Mixed Frequency Data (2016, unknown): “Equation (31) provides a denition of joint symmetry for general CDFs.”
Forecasting Value-at-Risk under Different Distributional Assumptions (2016, background): “[33]) 5 comprises daily open-to-close returns of 10 stocks from the Dow Jones Industrial Average: Bank of America (BAC), JP Morgan (JPM), International Business Machines (IBM), Microsoft (MSFT), Exxon Mobil (XOM), Alcoa (AA), American Express (AXP), Du Pont (DD), General Electric (GE) and Coca Cola ”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “Second, we follow Noureldin, Shephard, and Sheppard (2012) and construct realized covariance matrices using 5 min returns with subsampling.”