Discrete-time mean-CVaR portfolio selection and time-consistency induced term structure of the CVaR

Strub, Moris S.; Li, Duan; Cui, Xiangyu; Gao, Jianjun

doi:10.1016/j.jedc.2019.103751

Cited by 24 publications

(5 citation statements)

References 43 publications

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“…If we fix , then this strategy is the induced time-consistent strategy (Strub et al. , 2019; Forsyth, 2020a; Cui et al. , 2022) and hence is implementable.…”

Section: Risk and Rewardmentioning

confidence: 99%

“…However, in the following, we will consider the pre-commitment strategy merely as a device to determine an appropriate level of W * in Equation (6.3). If we fix W * ∀t > 0, then this strategy is the induced time-consistent strategy (Strub et al, 2019;Forsyth, 2020a;Cui et al, 2022) and hence is implementable. We delay further discussion of this subtle point to Appendix A.…”

Section: Risk: Definition Of Expected Shortfall (Es)mentioning

confidence: 99%

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Optimal performance of a tontine overlay subject to withdrawal constraints

Forsyth,

Vetzal,

Westmacott

2023

ASTIN Bull.

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We consider the holder of an individual tontine retirement account, with maximum and minimum withdrawal amounts (per year) specified. The tontine account holder initiates the account at age 65 and earns mortality credits while alive, but forfeits all wealth in the account upon death. The holder wants to maximize total withdrawals and minimize expected shortfall at the end of the retirement horizon of 30 years (i.e., it is assumed that the holder survives to age 95). The holder controls the amount withdrawn each year and the fraction of the retirement portfolio invested in stocks and bonds. The optimal controls are determined based on a parametric model fitted to almost a century of market data. The optimal control algorithm is based on dynamic programming and the solution of a partial integro differential equation (PIDE) using Fourier methods. The optimal strategy (based on the parametric model) is tested out of sample using stationary block bootstrap resampling of the historical data. In terms of an expected total withdrawal, expected shortfall (EW-ES) efficient frontier, the tontine overlay dramatically outperforms an optimal strategy (without the tontine overlay), which in turn outperforms a constant weight strategy with withdrawals based on the ubiquitous four per cent rule.

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“…If we fix , then this strategy is the induced time-consistent strategy (Strub et al. , 2019; Forsyth, 2020a; Cui et al. , 2022) and hence is implementable.…”

Section: Risk and Rewardmentioning

confidence: 99%

Section: Risk: Definition Of Expected Shortfall (Es)mentioning

confidence: 99%

Optimal performance of a tontine overlay subject to withdrawal constraints

Forsyth,

Vetzal,

Westmacott

2023

ASTIN Bull.

View full text Add to dashboard Cite

show abstract

“…Furthermore, Alexander and Baptista [8], assuming that efficiency follows a multivariate normal distribution, introduced a new model, whereas Huang et al [9], Zhu and Fukushima [10] and Zhu et al [11] suggested that robust optimization of the portfolio can be achieved through the use of the worst-case CVaR.Recently, continuous time balance policies were derived for optimization by means of the Mean-CVaR approach in the work of He and Jiang [12], and Cui et al [13] also examined the discrete-time state. The Mean-CVaR portfolio optimization problem was solved based on Lagrangian relaxation of the problem in a discrete time and an imperfect state in a study by Strub et al [14]. They solved one of the old puzzles in financial economics, the premium puzzle, using a Mean-CVaR model.…”

Section: Literature Reviewmentioning

confidence: 99%

Portfolio Selection Problem Using CVaR Risk Measures Equipped with DEA, PSO, and ICA Algorithms

et al. 2022

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Investors always pay attention to the two factors of return and risk in portfolio optimization. There are different metrics for the calculation of the risk factor, among which the most important one is the Conditional Value at Risk (CVaR). On the other hand, Data Envelopment Analysis (DEA) can be used to form the optimal portfolio and evaluate its efficiency. In these models, the optimal portfolio is created by stocks or companies with high efficiency. Since the search space is vast in actual markets and there are limitations such as the number of assets and their weight, the optimization problem becomes difficult. Evolutionary algorithms are a powerful tool to deal with these difficulties. The automotive industry in Iran involves international automotive manufacturers. Hence, it is essential to investigate the market related to this industry and invest in it. Therefore, in this study we examined this market based on the price index of the automotive group, then optimized a portfolio of automotive companies using two methods. In the first method, the CVaR measurement was modeled by means of DEA, then Particle Swarm Optimization (PSO) and the Imperial Competitive Algorithm (ICA) were used to solve the proposed model. In the second method, PSO and ICA were applied to solve the CVaR model, and the efficiency of the portfolios of the automotive companies was analyzed. Then, these methods were compared with the classic Mean-CVaR model. The results showed that the automotive price index was skewed to the right, and there was a possibility of an increase in return. Most companies showed favorable efficiency. This was displayed the return of the portfolio produced using the DEA-Mean-CVaR model increased because the investment proposal was basedon the stock with the highest expected return and was effective at three risk levels. It was found that when solving the Mean-CVaR model with evolutionary algorithms, the risk decreased. The efficient boundary of the PSO algorithm was higher than that of the ICA algorithm, and it displayed more efficient portfolios.Therefore, this algorithm was more successful in optimizing the portfolio.

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“…After analysing portfolio optimization literature, it can be seen that scientists often conclude that the portfolio selection problem should include additional parameters besides return and risk (Meghwani & Thakur, 2017;Sanchez-Roger et al, 2020;Siddique et al, 2020;Steuer et al, 2008). Examples of such criteria are liquidity (Al Janabi et al, 2019;Jana et al, 2009;Li & Zhang, 2021), skewness (Kerstens et al, 2008;Konno & Yamamoto, 2005;Pahade & Jha, 2021;Saborido et al, 2016), conditional value at risk (CVaR) (Aboulaich et al, 2010;Najafi & Mushakhian, 2015;Strub et al, 2019). Other scientists also broadly analysed various aspects of stock market and investment (Ogiugo et al, 2020, Dvorsky et al, 2020Masood et al, 2020;Giacomella, 2021;Becheikh, 2021;Zumente & Bistrova, 2021;Sl avik et al, 2021;Kasperovica & Lace, 2021;Nassar & Tvaronavi cien_ e 2021;Mura & Hajduov a, 2021), thus, these topics get proper attention.…”

Section: Literature Reviewmentioning

confidence: 99%