This study presents a systematic comparison of portfolio insurance strategies. In order to test for statistical significance of the differences in downside performance risk measures between pairs of portfolio insurance strategies, we use a bootstrap-based hypothesis test. Our comparison of different strategies considers the following distinguishing characteristics: static versus dynamic; initial wealth versus cumulated wealth protection; model-based versus model-free; and strong floor compliance versus probabilistic floor compliance. Our results show that the classical portfolio insurance strategies synthetic put and CPPI provide superior downside protection compared to a simple stop-loss trading rule, also resulting in significantly higher Omega ratios. Analyzing more recently developed strategies, neither the TIPP strategy (as an 'improved' CPPI strategy) nor the dynamic VaR-strategy provide significant improvements over the more traditional portfolio insurance strategies. The attractiveness of the dynamic VaR-strategy strongly depends on the quality of the estimates for the required input parameters, in particular, the equity risk premium. However, if an investor possesses superior forecasting skills, other active (market timing) strategies may exist which generate higher (risk-adjusted) returns compared to a protected passive stock market investment.

Scientific Essay from the year 2021 in the subject Business economics - Investment and Finance, , language: English, abstract: Investments in money and capital markets involve different loss potentials that market participants should be able to manage. Below follows an overview and comparison of selected strategies to manage these risks. Portfolio insurance (PI) strategies were developed in the 1980s. They are used to hedge portfolios or individual investments against price losses. The volume of assets hedged with these strategies is significant. Different forms of individual strategies have developed over the years. Risk quantification and Value at Risk (VAR) strategies emerged around the same time. Risks of individual investments or portfolios were measured and different strategies were developed to take them into account in Value at Risk optimised portfolios (VaRoP). VaRoP is a strategy that calculates an optimal portfolio taking into account a given or permissible maximum VAR. Both strategies are intended to protect portfolios from losses in value. Their similarities and differences as well as their successes are presented and summarised in this paper. Their applicability in practice is also examined.

We compare the performances of the two standard portfolio insurance methods: the Option Based Portfolio Insurance (OBPI) and the Constant Proportion Portfolio Insurance (CPPI), when the volatility of the stock index is stochastic. In this framework, we provide a quite general formula for the CPPI portfolio value. We use criteria such as comparison of payoffs functions at maturity and various quantiles. We emphasize in particular the role of the insured percentage of the initial investment.

This study makes a comparison between the most popular strategies of Portfolio Insurance based on Monte Carlo simulation. This work aims to define the best strategy at comparing different strategies and provide a contribution to solving some divergences in literature. Most of the previous comparisons do not take into consideration all the strategies discussed in this study and this analysis intends to add some relevant findings.The OBPI, CPPI and SLPI strategies are evaluated in terms of moments of the distribution, performance ratios (Sharpe ratio, Sortino ratio, Omega ratio and Upside Potential ratio) and stochastic dominance in different market conditions represented by an underlying asset that follows a geometric Brownian motion. In order to have a perception of a real situation in financial markets, the strategies are later also applied to three major stock indices (S&P 500, DJ EuroStoxx 50 and Nikkei 225).We find that CPPI 1 and SLPI strategies should be preferred in all scenarios according to the higher performance ratios, the higher expected returns and other measures. The choice between them is based on the preferences of the investor or manager, but we also find that the CPPI 1 strategy stochastically dominates, on second and third order, the others strategies in bear market scenarios. From our results we can state that a value of 100% for the floor should be preferred in terms of performance ratios, expected returns and other measures. This comparison allows improving the efficiency of decision making of an investor or manager in a Portfolio Insurance investment.

This book offers a timely review of cutting-edge applications of computational intelligence to business management and financial analysis. It covers a wide range of intelligent and optimization techniques, reporting in detail on their application to real-world problems relating to portfolio management and demand forecasting, decision making, knowledge acquisition, and supply chain scheduling and management.

In this article we use stochastic simulation methods to study the performance of a number of different dynamic portfolio insurance strategies, including option replicating portfolio insurance (ORPI), constant proportion portfolio insurance (CPPI) and a modified stop-loss (MSLI) strategy. We assume the underlying portfolio to be the Samp;P 500 tracking portfolio with all dividends reinvested upon receipt. The initial time to maturity is one year. Although the differences are mostly small, our results show that ORPI typically offers more attractive results than CPPI or MSLI. Adjusting the floor rule to lock in intermediate profits or adding a constant horizon feature does not lead to superior results.

The continuing creation of portfolio insurance applications as well as the mixed research evidence suggests that so far no consensus has been reached about the effectiveness of portfolio insurance. Therefore, this paper provides a performance evaluation of the stop-loss, synthetic put and constant proportion portfolio insurance techniques based on a block-bootstrap simulation. Apart from more traditional performance measures, we consider the Value-at-risk and Expected Shortfall of the strategies, which are more appropriate in an insurance context. An additional performance evaluation is given by means of the stochastic dominance framework where we account for sampling error. A sensitivity analysis is performed in order to examine the impact on performance of a change in a specific decision variable (ceteris paribus). The results indicate that a buy-and-hold strategy does not dominate the portfolio insurance strategies at any stochastic dominance order. Moreover, both for the stop-loss and synthetic put strategy a 100% floor value outperforms lower floor values. For the CPPI strategy we find that a higher CPPI multiple enhances the upward potential of the CPPI strategies, but harms the protection level in return. As regards the optimal rebalancing frequency, daily rebalancing should be preferred for the synthetic put and CPPI strategy, despite the higher transaction costs.

The purpose of this paper is to compare the performance of three portfolio insurance strategies which have different theoretical foundations. Although theoretically all of the strategies can protect the portfolio value without losing the chance of enjoying the gains from up movements of the market, the discrepancy between the theory and reality makes this goal hard to achieve. Under realistic assumptions, we conduct simulations to exam the strategy effectiveness with costs involved.

We compare performances of the two standard portfolio insurance methods: the Option Based Portfolio Insurance (OBPI) and the Constant Proportion Portfolio Insurance (CPPI). First we examine basic properties of these two strategies and compare them by means of various criteria: comparison of their payoffs, possible property of stochastic dominance, expectations, variances, skewness and kurtosis of their returns, and some of the quantiles of their returns. We prove that the OBPI method can be analyzed as a kind of CPPI where the multiple is allowed to vary. We then study the properties of this varying multiple.In a second section, we analyze more deeply both method's dynamic properties. We turn our attention to the dynamics management involved by these two strategies. Although the pure OBPI do not require any management by the buyer (if the put or call option is available on the market), we can calculate the quot;greeksquot; of its call part. We derive the quot;greeksquot; of the CPPI and show the very different nature of the dynamic properties of the two strategies.

This thesis is set out with the objective of evaluating and comparing the performance of portfolio insurance strategies. We try to figure out when and why one portfolio insurance strategy should be preferred by investors in practice. To meet this objective, main portfolio insurance strategies (OBPI, CPPI, Synthetic put and Stop-loss) are compared relatively to each other and to some benchmark strategies. Portfolio insurance strategies are applied within different implementation scenarios and compared according to various criteria that include:1. The payoff functions, stochastic dominance, the level of protection and the cost of insurance under bull and bear market conditions. 2. Various risk adjusted performance measures that reflect different investors' preferences toward risk and return. 3. The preferences of investors who act according to cumulative prospect theory (CPT). Our results reveal a dominant role of CPPI strategy at the majority of cases and according to the majority of comparison criteria.