VOLATILITY AND CORRELATION FORECASTING

By Torben G. Andersen, Tim Bollerslev, Peter F. Christoffersen, Francis X. Diebold. 2006

437 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f02d3a231e43da1eec70677393c50f858face825

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Citations in the corpus

Non-Negativity Conditions for the Hyperbolic GARCH Model (2010, unknown): “ht = ω B(1) + Ψ(L)ε(2)t = ω B(1) + ∞ ∑”
Financial econometric analysis at ultra-high frequency: Data handling concerns (2006, unknown): “Hasbrouck et al. (1993) is often quoted as a reference for NYSE rules and procedures, but attention must be paid to the modifications adopted ever since. Some authors, such as Madhavan and Sofianos (1998) and Sofianos and Werner (2000), analyze various empirical aspects of the NYSE trading mechanism”
Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility (2015, methodology): “Andersen et al. (2001) introduced a natural estimator for the quadratic variation of a process as the sum of frequently sampled squared returns, commonly known as realized variance (RV ).”
Volatility Forecast Comparison Using Imperfect Volatility Proxies (2006, unknown): “MAE-LOG : L i ˆσ2t,ht ¢ = ¯ ¯log ˆσ2t − loght ¯ ¯ ( 11 )”
High-Dimensional Copula-Based Distributions with Mixed Frequency Data (2016, unknown): “Research on forecasting models for multivariate covariance matrices with low-frequency data is pervasive, see Andersen et al. (2006) for a review, and research on forecasting models using high frequency data is growing, e.g. Chiriac and Voev (2011), Noureldin et al. (2012) among others. There are tw”
Expected stock returns and variance risk premia (2007, unknown): “A number of studies, using the volatility signature plot first proposed by Andersen et al. (2000), suggest that for many liquid assets, including the S&P 500 index analyzed here, a five-minute sampling frequency provides a reasonable choice (see, e.”
Copula Methods for Forecasting Multivariate Time Series (2013, methodology): “5 Using the Bayesian Information Criterion (BIC) and considering ARMA models for the conditional mean up to order (5; 5) ; the optimal models were found to be an AR(2) for the S&P 100 and an AR(0) (i.”
Copula-Based Models for Financial Time Series (2009, background): “(1994), Longin and Solnik (2001) and Ang and Chen (2002), Ang and Bekaert (2002), Bae, et al.”
A review of copula models for economic time series (2012, unknown): “[5] for example.”
Demystifying Time‐Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations (2020, unknown): “11We note that volatility forecasting is not our main objective; see Andersen et al. (2006) for a detailed overview.”
Value–at–Risk Models (2009, unknown): “In order to capture the time-varying volatility found in the daily returns, we rely on the NGARCH( 1 ,1)”