Benchmarking, portfolio insurance and technical analysis: a Monte Carlo comparison of dynamic strategies of asset allocation

Cesari, Riccardo; Cremonini, David

doi:10.1016/s0165-1889(02)00052-0

Cited by 108 publications

(54 citation statements)

References 15 publications

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“…Numerous articles have been written investigating the efficiency of the CPPI investment strategy (e.g., Cesari and Cremonini 2003, Constantinou et al 2008, Dersch 2010, Dichtl and Drobetz 2010. Various types of models are used to simulate risky assets in a portfolio.…”

Section: Methodsmentioning

confidence: 99%

“…The lower the RaR, the better the portfolio performance, whereas in the case of the Sharpe and Sortino ratio higher values mean better portfolio performance (Cesari et al 2003) …”

Section: No-trend Marketsmentioning

confidence: 99%

“…However, the higher the multiplier, the faster the portfolio will reach the floor value in times of continuously falling markets (Perold et al 1995). Cesari and Cremonini (2003) test the performance of the CPPI strategy and conclude that CPPI strategies dominate in bear and no-trend markets, whereas constant-mix strategies dominate in bull markets according to risk-adjusted Risky asset = m X (invested assets−floor) performance measures. Pain and Rand (2008) investigate the latest developments in portfolio insurance and explore the link between portfolio insurance and its implications on financial stability.…”

Section: Buy-and-hold Constant MIX and Cppimentioning

confidence: 99%

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Constant Proportion Portfolio Insurance Strategy in Southeast European Markets

Agić-Šabeta¹

2016

Business Systems Research Journal

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Background: In today's highly volatile and unpredictable market conditions, there are very few investment strategies that may offer a certain form of capital protection. The concept of portfolio insurance strategies presents an attractive investment opportunity. Objectives: The main objective of this article is to test the use of portfolio insurance strategies in Southeast European (SEE) markets. A special attention is given to modelling non-risky assets of the portfolio. Methods/Approach: Monte Carlo simulations are used to test the buy-and-hold, the constant-mix, and the constant proportion portfolio insurance (CPPI) investment strategies. A covariance discretization method is used for parameter estimation of bond returns. Results: According to the risk-adjusted return, a conservative constant mix was the best, the buy-and-hold was the second-best, and the CPPI the worst strategy in bull markets. In bear markets, the CPPI was the best in a high-volatility scenario, whereas the buy-and-hold had the same results in low-and medium-volatility conditions. In no-trend markets, the buy-and-hold was the first, the constant mix the second, and the CPPI the worst strategy. Higher transaction costs in SEE influence the efficiency of the CPPI strategy. Conclusions: Implementing the CPPI strategy in SEE could be done by combining stock markets from the region with government bond markets from Germany due to a lack of liquidity of the government bond market in SEE.

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Section: Methodsmentioning

confidence: 99%

“…The lower the RaR, the better the portfolio performance, whereas in the case of the Sharpe and Sortino ratio higher values mean better portfolio performance (Cesari et al 2003) …”

Section: No-trend Marketsmentioning

confidence: 99%

Section: Buy-and-hold Constant MIX and Cppimentioning

confidence: 99%

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Constant Proportion Portfolio Insurance Strategy in Southeast European Markets

Agić-Šabeta¹

2016

Business Systems Research Journal

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“…In effect the PT allows the quantification of risks and expected returns and selections of optimal portfolios by investors in financial markets (Hibiki, 2006). Strategic asset allocation is mostly the optimal allocation of risky assets (bonds, lease financing, stocks) and risk free (treasury bills, government bonds) assets to an investor over his or her investment horizon (Cesari and Cremonini, 2003). The popularly known strategies are the static, also known as the tactical asset allocation and the dynamic strategies.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Aslanidis and Casas (2013), Chu (2011), Cesari andCremonini (2003) and Campbell, Huisman and Koedijk (2001) have all focused on developed economies. Even the few papers that conducted cross-country assessments used samples dominated by developed countries.…”

Section: Introductionmentioning

confidence: 99%

Asset Allocation Brewed Accross African Stock Markets

Mensah¹

2016

Proceedings of the 5th Economic &Amp; Finance Conference, Miami

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Comonotonicity

Dhaene¹,

Vanduffel²,

Goovaerts

2008

Encyclopedia of Quantitative Risk Analysis and Assessment

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In an actuarial or financial context it is often necessary to calculate the distribution function or risk measures for random variables of the type where the marginal distributions of the X i are known but their dependence structure is too difficult to work with. Comonotonicity, which is an extremal form of positive dependence, can be useful to determine easily computable and accurate upper and lower bounds for the distribution of S , and hence also for risk measures related to S .

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Benchmarking, portfolio insurance and technical analysis: a Monte Carlo comparison of dynamic strategies of asset allocation

Cited by 108 publications

References 15 publications

Constant Proportion Portfolio Insurance Strategy in Southeast European Markets

Constant Proportion Portfolio Insurance Strategy in Southeast European Markets

Asset Allocation Brewed Accross African Stock Markets

Comonotonicity

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