A Risk Management Approach for Portfolio Insurance Strategies

Abstract

Controlling and managing potential losses is one of the main objectives of the Risk Management. Following Ben Ameur and Prigent (2007) and Chen et al. (2008), and extending the first results by Hamidi et al. (2009) when adopting a risk management approach for defining insurance portfolio strategies, we analyze and illustrate a specific dynamic portfolio insurance strategy depending on the Value-at-Risk level of the covered portfolio on the French stock market. This dynamic approach is derived from the traditional and popular portfolio insurance strategy (Cf. Black and Jones, 1987;Black and Perold, 1992): the so-called "Constant Proportion Portfolio Insurance" (CPPI). However, financial results produced by this strategy crucially depend upon the leverage -called the multiple -likely guaranteeing a predetermined floor value whatever the plausible market evolutions. In other words, the unconditional multiple is defined once and for all in the traditional setting. The aim of this article is to further examine an alternative to the standard CPPI method, based on the determination of a conditional multiple. In this time-varying framework, the multiple is conditionally determined in order to remain the risk exposure constant, even if it also depends upon market conditions. Furthermore, we propose to define the multiple as a function of an extended Dynamic AutoRegressive Quantile model of the Value-at-Risk (DARQ-VaR). Using a French daily stock database (CAC40 and individual stocks in the period 1998-2008), we present the main performance and risk results of the proposed Dynamic Proportion Portfolio Insurance strategy, first on real market data and secondly on artificial bootstrapped and surrogate data. Our main conclusion strengthens the previous ones: the conditional Dynamic Strategy with Constant-risk exposure dominates most of the time the traditional Constant-asset exposure unconditional strategies.

Hedge inflation risk of specific purpose guarantee funds

“…Previous literature has applied several different risk measures such as VaR and ES to determine the conditional dynamic multiplier, the criteria of which could ensure that the gap risk is well maintained (Ameur & Prigent, 2007; Balder et al, 2009; Hamidi et al, 2008, 2009; Jiang et al, 2009). For instance, Hamidi et al (2009) propose to define the multiple as a function of an extended Dynamic AutoRegressive Quantile model of VaR (DARQ‐VaR); Balder et al (2009) study the CPPI strategy in a discrete‐time setting with consideration of gap risk and discuss the criteria of under the ES measure. Thus, the common adopted (the upper bound of) dynamic multiples determined under the VaR and ES measures are and .…”

Estimating Portfolio Risk for Tail Risk Protection Strategies

“…The literature on dynamic proportion portfolio insurance puts forward various ways to model the conditional time-varying multiplier. While Ben Ameur andPrigent (2007, 2014) employ GARCH-type models, Hamidi, Jurczenko, and Maillet (2009) and Hamidi, Maillet, and Prigent (2009) define the multiplier as a function of a dynamic autoregressive quantile model of the Value-at-Risk according to Engle and Manganelli (2004). In contrast, Chen, Chang, Hou, and Lin (2008) propose a multiplier framework based on genetic programming.…”

Estimating portfolio risk for tail risk protection strategies

“…The literature on DPPI puts forward various ways to model the conditional time-varying multiplier. While Ben Prigent (2007, 2014) employ generalized autoregressive conditional heteroskedasticity (GARCH) type models, Hamidi, Jurczenko, and Maillet (2009) and Hamidi, Maillet, and Prigent (2009) define the multiplier as a function of a dynamic autoregressive quantile model of the VaR according to Engle and Manganelli (2004). In contrast, Chen, Chang, Hou, and Lin (2008) propose a multiplier framework based on genetic programming.…”