Smart beta and CPPI performance

Abstract

Les stratégies d’assurance CPPI sont des investissements d’horizon moyen à long terme, qui allouent dynamiquement entre un actif sans risque et un portefeuille d’actifs risqués. Leur objectif est de combiner un potentiel de gain tout en garantissant un capital à l’horizon. Notre article utilise une approche de simulation par bloc pour déterminer si la combinaison de stratégies « smart beta » et l’assurance de portefeuille est utile sous divers scénarios de marché. Nos résultats montrent une amélioration significative de la performance pour le CPPI combiné avec un portefeuille « low-risk ». Ceci est cohérent avec le véga négatif des stratégies CPPI ainsi qu’avec leur dépendance par rapport aux pertes passées.

Properties of the Margrabe Best-of-two strategy to tactical asset allocation

“…The drawback of such a long investment horizon is that the historical data provide only a small number of evaluation samples. To overcome this problem, we follow Perold and Sharpe (1995) and Ardia et al (2016) and backtest the MBo2 strategy by simulating M artificially generated price-paths of the high-risk asset for two investment horizons: one year and five years. Based on Jegadeesh and Titman (1993), the momentum strategy requires sampling windows of at least three months to preserve the positive autocorrelation in the return series.…”

“…We, therefore, apply a block-bootstrap with a block length of four and six months (for one-year and five-year investment horizons, respectively) which are sufficient to strike a balance between preserving the momentum in the returns, and allowing for heterogeneity in the simulated paths. Following Perold and Sharpe (1995) and Ardia et al (2016), we simulate M = 10, 000 price-paths.…”

Constant or Variable? A Performance Analysis among Portfolio Insurance Strategies

“…We then compare the performance of the three portfolio insurance strategies in three different market configurations, i.e., in low-, medium-, and high-volatility regimes, calculated according to the methodology proposed in Annaert et al (2009); Ardia et al (2016). More precisely, we split the simulated return paths into tercile groups, by using the realized volatility.…”

Estimating portfolio risk for tail risk protection strategies

“…The remainder is invested in the risk-free asset. If rebalancing were continuous and price movements sufficiently smooth, the CPPI allocation rule would ensure that the portfolio does not fall below the floor (Ardia, Boudt, & Wauters, 2016;Balder et al, 2009;Cont & Tankov, 2009;Hamidi, Hurlin, Kouontchou, & Maillet, 2015). However, with discrete rebalancing and jumps in prices, there is a non-negligible probability that the floor is breached.…”