Estimating portfolio risk for tail risk protection strategies

Happersberger, David; Lohre, Harald; Nolte, Ingmar

doi:10.1111/eufm.12256

Cited by 18 publications

(9 citation statements)

References 95 publications

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“…It is intuitive that the advantage of dynamic strategies is to timely adjust their exposures to risky assets according to the market conditions. Happersberger et al (2020) provide a comprehensive analysis of literature on comparing different ways to dynamically determine the risky investment exposure of strategies. Commonly, DPPI sets the maximal value of dynamic multiplier by the following form, 24 Dichtl et al (2017) presents a systematic comparison of portfolio insurance strategies using Bootstrap-based approach.…”

Section: Discussion: Po-dppi Strategy With a Dynamic Multipliermentioning

confidence: 99%

“…Happersberger et al (2020) provide a comprehensive analysis of literature on comparing different ways to dynamically determine the risky investment exposure of strategies. Commonly, DPPI sets the maximal value of dynamic multiplier by the following form,

m_{t} \leq \frac{1}{ρ_{t} (r_{t + 1})}

to provide effective downside protection.…”

Section: Discussion: Po‐dppi Strategy With a Dynamic Multipliermentioning

confidence: 99%

“…Similarly, other works like Ameur and Prigent (2007), Balder et al (2009), Hamidi et al (2014), and Jiang et al (2009) have modelled the multipliers of traditional portfolio insurance strategies as time‐varying ones. Happersberger et al (2020) provide a comprehensive review and also propose a novel combination approach to estimate tail risk exposure of dynamic portfolio insurance strategies.…”

Section: Introductionmentioning

confidence: 99%

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Hedge inflation risk of specific purpose guarantee funds

Chen

et al. 2021

Euro Fin Management

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Specific purpose guarantee funds (SPGFs) such as pension guarantee funds are popular among investors with both specific investment purpose and guaranteed return requirement but receive little academic attention. We propose a practical purpose‐oriented constant proportion portfolio insurance (PO‐CPPI) strategy that optimally allocates its assets into a risk‐free fund (floor) and a purpose‐related portfolio (cushion) to maximize prospect theory investors' utility with consideration of their purpose‐related inflation risk. Our closed‐form solution of optimal PO‐CPPI allocation derived in the continuous time case and Monte‐Carlo simulations in the discrete‐time and dynamic cases prove the superiority of PO‐CPPI over general portfolio insurance strategies.

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Section: Discussion: Po-dppi Strategy With a Dynamic Multipliermentioning

confidence: 99%

m_{t} \leq \frac{1}{ρ_{t} (r_{t + 1})}

to provide effective downside protection.…”

Section: Discussion: Po‐dppi Strategy With a Dynamic Multipliermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hedge inflation risk of specific purpose guarantee funds

Chen

et al. 2021

Euro Fin Management

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“…Rickenberg (2019) compares different risk measure such as volatility, VaR and Conditional-Value-at-Risk (CVaR), also named Expected Shortfall (ES), in order to build dynamic trading strategies and find that downside risk measures outperform volatility in terms of a higher Sharpe Ratio, better drawdown protection and higher utility gains for mean-variance and loss-averse investors. Happersberger et al (2019) also focus ES and VaR forecasts in order to manage dynamic tail risk protection strategies.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…There are a few well documented tail risk protection strategies in the literature, such as the constant proportion portfolio insurance (CPPI) strategy (Black and Jones;1987), the Dynamic Proportion Portfolio Insurance (DPPI) or Time-varying Proportion Portfolio Insurance (TPPI) strategy (Hamidi et al;, (Happersberger et al;2019) or the protective put strategy using options. In this paper, we compared our results with more recent and machine learning oriented work, in particular the Varspread and target VaR strategies.…”

Section: Benchmarksmentioning

confidence: 99%

Tail-risk protection: Machine Learning meets modern Econometrics

Spilak¹,

Härdle²

2020

Preprint

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Tail risk protection is in the focus of the financial industry and requires solid mathematical and statistical tools, especially when a trading strategy is derived. Recent hype driven by machine learning (ML) mechanisms has raised the necessity to display and understand the functionality of ML tools. In this paper, we present a dynamic tail risk protection strategy that targets a maximum predefined level of risk measured by Value-At-Risk while controlling for participation in bull market regimes. We propose different weak classifiers, parametric and non-parametric, that estimate the exceedance probability of the risk level from which we derive trading signals in order to hedge tail events. We then compare the different approaches both with statistical and trading strategy performance, finally we propose an ensemble classifier that produces a meta tail risk protection strategy improving both generalization and trading performance.

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A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies

Trucíos

Taylor

2022

Journal of Forecasting

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Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long-memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, and quantile regression-based models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their value at risk and expected shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that include periods of turbulence due to the COVID-19 pandemic, the third halving of Bitcoin, and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for Bitcoin, there is no single procedure outperforming all other models, but for Ethereum, there is evidence showing that the GAS model is a suitable alternative for forecasting both risk measures. We found that the combining methods were not able to outperform the better of the individual models.

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Estimating portfolio risk for tail risk protection strategies

Cited by 18 publications

References 95 publications

Hedge inflation risk of specific purpose guarantee funds

Hedge inflation risk of specific purpose guarantee funds

Tail-risk protection: Machine Learning meets modern Econometrics

A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies

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