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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. - PublicationAccès libreThe economic benefits of market timing the style allocation of characteristic-based portfolios(2016)
; ;Boudt, KrisWauters, MarjanMany exchange traded funds track simple characteristic-based equity portfolios such as the market capitalization, the fundamental value or the inverse volatility portfolio. This paper provides theoretical and empirical evidence for the economic benefits in exploiting the timing-gains that result from the time-varying relative performance of these characteristic-based portfolios. Under a factor model for expected returns, we show that this dynamic portfolio allocation can be efficient across the low-dimensional set of characteristic-based portfolios. We assess the out-of-sample performance on the S&P 100 universe over the period 1990–2013 and show gains in stability and significant positive risk-adjusted returns for the dynamic style portfolio. We conduct several robustness tests and extensions confirming the benefits of dynamic style allocation across characteristic-based portfolios.