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Ardia, David
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Ardia, David
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Voici les éléments 1 - 10 sur 17
- PublicationAccès libre
- PublicationAccès libreProperties of the Margrabe Best-of-Two strategy to tactical asset allocation(2019)
; ;Boudt, Kris ;Hartmann, StefanNguyen, Giang - PublicationAccès libreMarkov-switching GARCH models in R: The MSGARCH package(2019)
; ; ;Boudt, Kris ;Catania, LeopoldoTrottier, Denis-Alexandre - PublicationAccès libreGeneralized autoregressive score models in R: The GAS package(2019-1-1)
; ;Boudt, KrisCatania, Leopoldo - PublicationAccès libreDownside risk evaluation with the R package GAS(2018)
; ;Boudt, KrisCatania, Leopoldo - PublicationAccès libreForecasting risk with Markov-switching GARCH models: A large-scale performance study(2018)
; ; ;Boudt, KrisCatania, Leopoldo - PublicationAccès libreBeyond risk-based portfolios: Balancing performance and risk contributions in asset allocation(2018)
; ;Boudt, KrisNguyen, Giang - PublicationAccès libreThe impact of covariance misspecification in risk-based portfolios(2017-3)
; ;Boudt, Kris; Gagnon-Fleury, PhilippeThe equal-risk-contribution, inverse-volatility weighted, maximum-diversification and minimum-variance portfolio weights are all direct functions of the estimated covariance matrix. We perform a Monte Carlo study to assess the impact of covariance matrix misspecification to these risk-based portfolios at the daily, weekly and monthly forecasting horizon. Our results show that the equal-risk-contribution and inverse-volatility weighted portfolio weights are relatively robust to covariance misspecification. In contrast, the minimum-variance portfolio weights are highly sensitive to errors in both the estimated variances and correlations, while errors in the estimated correlations can have a large effect on the weights of the maximum-diversification portfolio. - PublicationAccès libreRiskPortfolios: Computation of risk-based portfolios in R(2017-2)
; ;Boudt, KrisGagnon-Fleury, PhilippeRiskPortfolios is an R package for constructing risk-based portfolios. It provides a set of functionalities to build mean-variance, minimum variance, inverse-volatility weighted (Leote De Carvalho, Lu, and Moulin (2012)), equal-risk-contribution (Maillard, Roncalli, and Teïletche (2010)), maximum diversification (Choueifaty and Coignard (2008)), and risk-efficient (Amenc et al. (2011)) portfolios. Optimization is achieved with the R packages quadprog (Weingessel (2013)) and nloptr (Ypma (2014)). Long or gross constraints can be added to the optimizer. As risk-based portfolios are mainly based on covariances, the package also provides a large set of covariance matrix estimators.