A bootstrap-based comparison of portfolio insurance strategies

Dichtl, Hubert; Drobetz, Wolfgang; Wambach, Martin

doi:10.1080/1351847x.2015.1029590

Cited by 20 publications

(20 citation statements)

References 52 publications

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“…To avoid portfolio shifts triggered by rather small market movements, we also apply a trading filter of 2%, acting only on exposure changes in excess of 2% (cf. Dichtl et al, 2017).…”

Section: Hsmentioning

confidence: 99%

“…To be consistent with the DPPI strategy that is based on ES we only report the results for the ES targeting strategy.26 Note that we rely on the Omega ratio rather than the mean of the yearly Calmar ratios in the historical block-bootstrap analysis. While the Calmar ratio is based on daily returns (and thus needs to be transformed via the mean), the Omega ratio is usually calculated for longer-horizon returns such as yearly returns and is therefore more appropriate in the historical block-bootstrap analysis (seeBertrand & Prigent, 2011;Dichtl et al, 2017).…”

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confidence: 99%

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Estimating portfolio risk for tail risk protection strategies

Happersberger

Lohre

Nolte

2020

Euro Fin Management

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We forecast portfolio risk for managing dynamic tail risk protection strategies, based on extreme value theory, expectile regression, copula‐GARCH and dynamic generalized autoregressive score models. Utilizing a loss function that overcomes the lack of elicitability for expected shortfall, we propose a novel expected shortfall (and value‐at‐risk) forecast combination approach, which dominates simple and sophisticated standalone models as well as a simple average combination approach in modeling the tail of the portfolio return distribution. While the associated dynamic risk targeting or portfolio insurance strategies provide effective downside protection, the latter strategies suffer less from inferior risk forecasts, given the defensive portfolio insurance mechanics.

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“…To avoid portfolio shifts triggered by rather small market movements, we also apply a trading filter of 2%, acting only on exposure changes in excess of 2% (cf. Dichtl et al, 2017).…”

Section: Hsmentioning

confidence: 99%

mentioning

confidence: 99%

Estimating portfolio risk for tail risk protection strategies

Happersberger

Lohre

Nolte

2020

Euro Fin Management

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“…To avoid portfolio shifts triggered by rather small market movements, we also apply a trading filter of 2%, acting only on exposure changes in excess of 2% (cf. Dichtl, Drobetz, and Wambach, 2017). Figure 3 illustrates the performance of the ES targeting strategy for the historical path and the historical block-bootstrap, 25 based on the 1%-ES of the FZ loss combination approach.…”

Section: The Economic Relevance Of Risk Forecasting For Tail Risk Promentioning

confidence: 99%

“…Note that we rely on the Omega ratio rather than the mean of the yearly Calmar ratios in the historical blockbootstrap analysis. While the Calmar ratio is based on daily returns (and thus needs to be transformed via the mean), the Omega ratio is usually calculated for longer-horizon returns such as yearly returns and is therefore more appropriate for the historical block-bootstrap analysis (seeBertrand and Prigent, 2011;Dichtl, Drobetz, and Wambach, 2017).27 We choose the following portfolio-specific ES target levels: 1.5% for the multi-asset portfolio, 4% for the pure equity portfolio, 1.5% for the pure bond portfolio, 1.5% for the 30/70 equity/bond portfolio and 2.5% for the 60/40 equity/bond portfolio.…”

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confidence: 99%

Estimating Portfolio Risk for Tail Risk Protection Strategies

2018

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We forecast portfolio risk for managing dynamic tail risk protection strategies, based on extreme value theory, expectile regression, Copula-GARCH and dynamic GAS models. Utilizing a loss function that overcomes the lack of elicitability for Expected Shortfall, we propose a novel Expected Shortfall (and Value-at-Risk) forecast combination approach, which dominates simple and sophisticated standalone models as well as a simple average combination approach in modelling the tail of the portfolio return distribution. While the associated dynamic risk targeting or portfolio insurance strategies provide effective downside protection, the latter strategies suffer less from inferior risk forecasts given the defensive portfolio insurance mechanics.

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“…In the case of comparison of the CPPI strategy with other portfolio insurance strategies, Jiang, Ma, and An (2009) proposed a VaR-based portfolio insurance strategy which is called VBPI strategy, they compared the effectiveness of this model with the other strategies such as CPPI and the buy and hold strategy (Dichtl, Drobetz, & Wambach, 2017).…”

Section: Introductionmentioning

confidence: 99%

Dynamic portfolio insurance strategy: a robust machine learning approach

Dehghanpour

Esfahanipour

2018

Journal of Information and Telecommunication

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In this paper, we propose a robust genetic programming (RGP) model for a dynamic strategy of stock portfolio insurance. With portfolio insurance strategy, we divide the money in a risky asset and a risk-free asset. Our applied strategy is based on a constant proportion portfolio insurance strategy. For determining the amount for investing in the risky asset, a critical parameter is a constant risk multiplier that is calculated in our proposed model using RGP to reflect market dynamics. Our model includes four main steps: (1) Selecting the best stocks for constructing a portfolio using a density-based clustering strategy. (2) Enhancing the robustness of our proposed model with an application of the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for forecasting the future prices of the selected stocks. The findings show that using ANFIS, instead of a regular multi-layer artificial neural network improves the prediction accuracy and our model's robustness. (3) Implementing the RGP model for calculating the risk multiplier. Risk variables are used to generate equation trees for calculating the risk multiplier. (4) Determining the optimal portfolio weights of the assets using the well-known Markowitz portfolio optimization model. Experimental results show that our proposed strategy outperforms our previous model.

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A bootstrap-based comparison of portfolio insurance strategies

Cited by 20 publications

References 52 publications

Estimating portfolio risk for tail risk protection strategies

Estimating portfolio risk for tail risk protection strategies

Estimating Portfolio Risk for Tail Risk Protection Strategies

Dynamic portfolio insurance strategy: a robust machine learning approach

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