Robust Forecasting of Dynamic Conditional Correlation GARCH Models
By Kris Boudt, Jón Dańıelsson, Sébastien-Yves Laurent. 2013
90 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/0c40e7ffb4a68e82927c0127dc0235080c11bbf2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Konstantinos Nikolopoulos
- Evangelos Spiliotis
- Vassilios Assimakopoulos
- Amélie Charles
- Olivier Darné
- Esther Ruiz
- Luiz Koodi Hotta
- D. Almeida
In Finance
- Amélie Charles
- Olivier Darné
- Esther Ruiz
- Evangelos Spiliotis
- Konstantinos Nikolopoulos
- Luiz Koodi Hotta
- D. Almeida
- Vassilios Assimakopoulos
Citations in the corpus
- Volatility estimation for Bitcoin: Replication and robustness (2019, background): “…as: where ξ i are the coefficients from the AR(1) polynomial defined in equation (1), ) is the weight function, and c δ a factor ensuring that the conditional expectation of the weighted square of unexpected shocks equals the conditional variance of r t in the absence of jumps (Boudt et al., 2013).”
- Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: a vine copula–based approach (2019, unknown): “Bouri et al. (2017a), (2017b) argued that the popularity of cryptocurrencies stems from investors’ view that digital currency can be considered a shelter against economic and financial turmoil. Luther and Salter (2017) provided evidence that the demand for Bitcoin increased rapidly after the Cyprus ”
- Tales from tails: On the empirical distributions of forecasting errors and their implication to risk (2019, methodology): “Boudt et al. (2013) discuss robust approaches for applying GARCH models, Cheng & Yang (2015) propose a technique for combining forecasts with outlier protection, while Castle et al. (2015) claim improvements in accuracy using a class of robust devices exploiting equilibrium-correction models.”
- MGARCH models: Trade-off between feasibility and flexibility (2018, methodology): “Finally, Boudt et al. (2013) propose a robust extension of the model, known as BIP-cDCC model, for forecasting correlations in the presence of one-off events which cause large changes in prices whilst not affecting the volatility dynamics.”