Portfolio Insurance Strategies Research Articles

Dynamic portfolio insurance strategies: risk management under Johnson distributions

The purpose of this paper is to analyze the gap risk of dynamic portfolio insurance strategies which generalize the “Constant Proportion Portfolio Insurance” (CPPI) method by allowing the multiple to vary. We illustrate our theoretical results for conditional CPPI strategies indexed on hedge funds. For this purpose, we provide accurate estimations of hedge funds returns by means of Johnson distributions. We introduce also an EGARCH type model with Johnson innovations to describe dynamics of risky logreturns. We use both VaR and Expected Shortfall as downside risk measures to control gap risk. We provide accurate upper bounds on the multiple in order to limit this gap risk. We illustrate our theoretical results on Credit Suisse Hedge Fund Index. The time period of the analysis lies between December 1994 and December 2013.

Investors' Perpective on Portfolio Insurance - Expected Utility vs Prospect Theories

This study supports the use of behavioural finance to explain the popularity of most portfolio insurance investments. Portfolio insurance strategies are important financial solutions sold to institutional and individual investors, that protect against downside risk while maintaining some upside valuation potential. The way these strategies are engineered has been criticized, and some analysts point them as one of the causes for increasing market volatility in depressed markets. In spite of the negative opinion, and the difficulties to explain their solid market share, investors keep on buying portfolio insurance. This study contributes to understand the phenomenon. We compare investors’ decision using two distinct frameworks: the expected utility theory and the behavioural approach. Based upon Monte Carlo simulation techniques we compare popular portfolio insurance investment strategies against uninsured basic benchmark strategies. We conclude that cumulative prospect theory may be a viable framework to explain the popularity of most portfolio insurance investments (the exception being constant portfolio insurance strategies). In line with the recent literature, our results point the best choices to be naive portfolio insurance strategies, instead of complex products.

On the Stochastic Dominance of Portfolio Insurance Strategies

This paper compares the performance of the two main portfolio insurance strategies, namely the Option-Based Portfolio Insurance (OBPI) and the Constant Proportion Portfolio Insurance (CPPI). For this purpose, we use the stochastic dominance approach. We provide several explicit sufficient conditions to get stochastic dominance results. When taking account of specific constraints, we use the consistent statistical test proposed by Barret and Donald [1]. It is similar to the Kolmogrov-Smirnov test with a complete set of restrictions related to the various forms of stochastic dominance. We find that the CPPI method can perform better than the OBPI one at the third order stochastic dominance.

A portfolio insurance strategy for volatility index (VIX) futures

Abstract This paper proposes a methodology using VIX futures as an investment asset while controlling downside risk. For this purpose, three portfolio insurance ( PI) strategies are built by using option-based portfolio insurance ( OBPI) and constant proportion ( CPPI) for VIX futures. The effectiveness of the strategy is tested by historical return simulation of eight subsamples and a full sample for the period of Feb. 2007–Jan. 2015. We evaluate the performance of each strategy first as a pure investment tool and then as a diversification tool for S&P500 index. In the subsample simulation, all PI strategies perfectly protect its floor. Protective Put and CPPI appropriately catch up with strong and trendy bull markets of VIX futures. Resetting achieves a considerable return for the periods of return-reversal. In the full-sample simulation, the daily mean returns of the PI strategy are all greater than the benchmark's. The PI strategy is also a good diversification tool for S&P500 index.

Optimized Proportional Portfolio Insurance Strategy Indexes

In this paper, we derive an improved methodology for proportional portfolio insurance strategies to create an investment proposal that offers both continuous drawdown protection on the downside as well as long-run participation on the upside. The key improvement lies in implementing mechanisms that ensure the portfolio insurance strategy does not lose its upside participation potential by getting stuck on the fixed-income buffer after a serious market downturn has occurred. Further, we will show the effectiveness and robustness of the methodology across the business cycle by long-term performance backtests.

Optimized Proportional Portfolio Insurance Strategy Indexes

Optimized Proportional Portfolio Insurance Strategy Indexes

Optimal rebalance rules for the constant proportion portfolio insurance strategy – Evidence from China

The constant proportion portfolio insurance (CPPI) strategy is one of the most popular asset allocation strategies employed by guaranteed-return financial products investors. Rebalance disciplines play an important role in determining the CPPI performance in practice. This paper examines whether the selection of rebalance rules affects CPPI strategy performance in the context of Chinese equity markets and, if so, in what pattern, and whether an optimal parameter of rebalance exists. We find that, (1) the three alternative rebalance disciplines – time discipline, market move discipline and lag discipline – are indifferent in affecting the performance of CPPI strategy; (2) in terms of optimal parameters of each rebalance rule, the optimal rebalancing period for the time discipline is 3 trading days, the optimal trading threshold of the market move discipline 4%, and the optimal lag factor of the lag discipline 6%. These optimal parameters are not influenced by the length of investment.

A bootstrap-based comparison of portfolio insurance strategies

This study presents a systematic comparison of portfolio insurance strategies. We implement a bootstrap-based hypothesis test to assess statistical significance of the differences in a variety of downside-oriented risk and performance measures for pairs of portfolio insurance strategies. Our comparison of different strategies considers the following distinguishing characteristics: static versus dynamic protection; initial wealth versus cumulated wealth protection; model-based versus model-free protection; and strong floor compliance versus probabilistic floor compliance. Our results indicate that the classical portfolio insurance strategies synthetic put and constant proportion portfolio insurance (CPPI) provide superior downside protection compared to a simple stop-loss trading rule and also exhibit a higher risk-adjusted performance in many cases (dependent on the applied performance measure). Analyzing recently developed strategies, neither the TIPP strategy (as an ‘improved’ CPPI strategy) nor the dynamic VaR-strategy provides significant improvements over the more traditional portfolio insurance strategies.

Appendix to Managing Sponsor Risk in Pension Plans: Derivation and Implementation of First-Best Strategies

Defined-benefit (DB) pension funds, often underfunded, rely on the legal obligation of their sponsor to secure pension rights for individuals. The sponsor guarantee being risky, its riskiness must be hedged to secure the pension promises. This appendix details the implementation of the extended class of portfolio insurance strategies of Sender (2014), relying on a dynamic trading strategy based on the sponsor’s shares.The paper Managing Sponsor Risk in Pension Plans: Dynamic Strategies vs. Pension Assurance to which this Appendix applies is available at the following URL: http://ssrn.com/abstract=1939056

Dynamic preferences for popular investment strategies in pension funds

In this paper, we characterize dynamic investment strategies that are consistent with the expected utility setting and more generally with the forward utility setting. Two popular dynamic strategies in the pension funds industry are used to illustrate our results: a constant proportion portfolio insurance (CPPI) strategy and a life-cycle strategy. For the CPPI strategy, we are able to infer preferences of the pension fund’s manager from her investment strategy, and to exhibit the specific expected utility maximization that makes this strategy optimal at any given time horizon. In the Black–Scholes market with deterministic parameters, we are able to show that traditional life-cycle funds are not optimal to any expected utility maximizers. We also prove that a CPPI strategy is optimal for a fund manager with HARA utility function, while an investor with a SAHARA utility function will choose a time-decreasing allocation to risky assets in the same spirit as the life-cycle funds strategy. Finally, we suggest how to modify these strategies if the financial market follows a more general diffusion process than in the Black–Scholes market.

A dynamic autoregressive expectile for time-invariant portfolio protection strategies

“Constant proportion portfolio insurance” is a popular technique among portfolio insurance strategies: the risky part of a portfolio is reallocated with respect to market conditions, via a fixed parameter (the multiple), guaranteeing a predetermined floor. We propose here to use a conditional time-varying multiple as an alternative. We provide the main properties of the conditional multiples for some mainstream cases, including discrete-time rebalancing and an underlying risk asset driven by the Lévy process, while evaluating conditional and unconditional gap risks. Finally, we evaluate the use of a dynamic autoregressive expectile model for estimating the conditional multiple in such a context.

Forecasting market turbulence using regime-switching models

We propose an early warning system to timely forecast turbulence in the US stock market. In a first step, a Markov-switching model with two regimes (a calm market and a turbulent market) is developed. Based on the time series of the monthly returns of the S&P 500 price index, the corresponding filtered probabilities are successively estimated. In a second step, the turbulent phase of the model is further specified to distinguish between bullish and bearish trends. For comparison only, a Markov-switching model with three states (a calm market, a turbulent bullish market, and a turbulent bearish market) is examined as well. In a third step, logistic regression models are employed to forecast the filtered probabilities provided by the Markov-switching models. A major advantage of the presented modeling framework is the timely identification of the factors driving the different phases of the capital market. In a fourth step, the early warning system is applied to an asset management case study. The results show that explicit consideration of the models’ signals yields better portfolio performance and lower portfolio risk compared to standard buy-and-hold and constant proportion portfolio insurance strategies.

Performance evaluation of optimized portfolio insurance strategies

We use S&P 500 index return data for the time period 1985–2013 to evaluate the performance of portfolio insurance strategies. We shed light on the question if the performance of a constant proportion portfolio insurance (CPPI) strategy can be improved by means of a time-varying multiplier which depends on the estimated future volatility. Neglecting any inter-temporal hedging demand, the theoretical foundation of the strategies is given by maximizing the expected utility of a HARA investor in a diffusion model setup. If the risk premium is assumed to be proportional to the variance, the optimal strategy is a CPPI strategy. Otherwise, the multiple is time-varying (PPI). It turns out that even time-varying multiple strategies based on a rolling window of historical volatility estimates give a significant improvement of CPPI strategies. The out-performance is robust w.r.t. alternative performance measures and is also true for proportional transaction costs and adequate trigger trading.

证券投资策略相关研究综述

证券投资策略对于投资者来说非常重要，也是国内外学者重点关注的问题之一。自从Markowitz提出证券投资组合理论以来，国内外学者对证券投资策略进行了广泛而深入地探讨。本文主要从证券投资策略的兴起与发展、基于行为金融学的证券投资策略、投资组合保险策略以及基于金融市场微观结构理论的证券投资策略等几个方面，系统梳理和综述了国内外相关文献，并指出了今后需要研究的一些方向。Securities investment strategy is very important for investors, and it’s one of the issues that do-mestic and foreign scholars focus on. Since Markowitz proposed securities portfolio theory, domestic and foreign scholars have carried out extensive and deep discussion on securities investment strategy. From the rise and development of the securities investment strategy, securities in-vestment strategy based on behavioral finance, portfolio insurance strategies and securities in-vestment strategies based on financial market microstructure theory, this paper systematically combines and reviews the domestic and foreign relevant literature, and points out some directions for future research.

A Bootstrap-Based Comparison of Portfolio Insurance Strategies

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.

The market of dynamic hybrid products in Germany: concept, risk-return profiles, and market overview

This article analyzes dynamic hybrid products along with their diverse characteristics and contract variations that are currently available in the German market. Dynamic hybrid products are innovative life insurance contracts combining features of traditional participating life insurance with those of unit-linked policies. This approach is thereby implemented by a mathematical algorithm based on a constant proportion portfolio insurance strategy that periodically reallocates funds (e.g. once per month or day) between the policy reserve stock (with an interest rate guarantee), a guarantee fund and/or equity fund. In this paper, we contribute to the literature by examining the concepts and key features of available dynamic hybrid products with particular focus on the embedded options, which allows the identification of key contract characteristics associated with them. In addition, risk-return profiles are studied and compared, which is of high relevance for regulators and policyholders. Our results show that these strongly vary, depending on the individual rebalancing mechanism and the type and amount of embedded options.

Are Leveraged and Inverse ETFs the New Portfolio Insurers?

This paper studies Leveraged and Inverse Exchange Traded Funds (LETFs) from a financial stability perspective. Mechanical positive-feedback rebalancing of LETFs resembles the portfolio insurance strategies, which contributed to the stock market crash of October 19, 1987 (Brady Report, 1988). I show that a 1% increase in broad stock-market indexes induces LETFs to originate rebalancing flows equivalent to $1.04 billion worth of stock. Price-insensitive and concentrated trading of LETFs results in price reaction and extra volatility in underlying stocks. Implied price impact calculations and empirical results suggest that they contributed to the stock market volatility in the 2008-2009 financial crisis and in the second half of 2011 when the European sovereign debt crisis came to the forefront. Although LETFs are not as large as portfolio insurers of the 1980s and have not been proven to disrupt stock market activity, their large and concentrated trading could be destabilizing during periods of high volatility.

Best portfolio insurance for long-term investment strategies in realistic conditions

Constant proportion portfolio insurance (CPPI) strategies implemented in continuous time on asset prices following geometric Brownian processes are expected utility maximising for investors with HARA utilities. But, in reality, these strategies are implemented in discrete time and asset prices might jump. We show that under these more realistic circumstances, optimal CPPI strategies are still superior to optimal option based portfolio insurance (OBPI) strategies. The effects of discrete replication and jumps on optimal strategy parameters and certainty equivalent returns (CER) are examined by simulation and turn out to be minor in typical circumstances. Hence the much discussed gap risks are unimportant for investors in both portfolio insurance strategies and comparable for insurers of the gap risks.

A portfolio insurance strategy for commodity futures

We propose a generalised constant proportion portfolio insurance (CPPI) strategy for a commodity futures fund, which promises at least a partial principal guarantee at the end of the investment horizon. We present the generalised rebalancing rules to allocate capital between a risk-free asset and a futures margin account. Our formula guarantees that the fund satisfies both the margin requirement and the maximum exposure to the risky asset permitted by the CPPI at any time during the investment horizon. We find that although investment in futures is risky because of the use of significant leverage, generally, the proposed futures-based CPPI strategy performs better than the traditional CPPI-spot strategies. The satisfactory results in this paper will not only draw attention from academia but also from commodity trading advisors fund managers.

Value-at-Risk-Based Risk Management in a Jump-Diffusion Model

This paper investigates optimal portfolio and wealth strategy of an institutional investor with a Value-at-Risk (VaR) constraint in an economy under jump diffusion. The problem is solved in closed form. We show that overlooking or underestimating the jump risk factor results in failures to satisfy the VaR constraint. We also find that the presence of the jump risk drives the institutional investor to move towards the portfolio insurance strategy, alleviating the problem with VaR identified by Basak and Shapiro (2001) that the VaR risk manager incurs larger losses than non risk manager in worst scenarios. Moreover, put options play an important role in hedging jump risks under the VaR framework.