Portfolio selection: An extreme value approach

DiTraglia, Francis J.; Gerlach, Jeffrey R.

doi:10.1016/j.jbankfin.2012.08.022

Cited by 24 publications

(7 citation statements)

References 63 publications

(70 reference statements)

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“…One of these challenges is the need for diversification strategies that account for strong drawdowns and increasing dependence of asset returns in crisis periods. This has raised the relevance of non-Gaussian models, tail dependence, and quantile based risk measures in portfolio optimization [5,8,9,10,11,12,17,21,24,26,35,38,39,44].…”

Section: Introductionmentioning

confidence: 99%

Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz

Mainik

Mitov²,

Rüschendorf

2015

Journal of Empirical Finance

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Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the extreme risk index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, our study seems to be the first application of extreme value techniques in portfolio management on a large scale. The primary aim of our investigation is the potential of ERI in practice. The performance of this strategy is benchmarked against the minimum variance portfolio and the equally weighted portfolio. These fundamental strategies are important benchmarks for large-scale applications. Our comparison includes annualized portfolio returns, maximal drawdowns, transaction costs, portfolio concentration, and asset diversity in the portfolio. In addition to that we study the impact of an alternative tail index estimator. Our results show that the ERI strategy significantly outperforms both the minimum-variance portfolio and the equally weighted portfolio on assets with heavy tails. 1 c 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http: // creativecommons. org/ licenses/ by-nc-nd/ 4. 0/ arXiv:1505.04045v1 [q-fin.PM] 15 May 2015 2 c 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http: // creativecommons. org/ licenses/ by-nc-nd/ 4. 0/ In our paper we follow the basic line of developments on the optimization problem that the investment strategy is derived from. Our reformulation of 3 c 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http: // creativecommons. org/ licenses/ by-nc-nd/ 4. 0/ 5 c 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http: // creativecommons. org/ licenses/ by-nc-nd/ 4. 0/ 7 c 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http: // creativecommons. org/ licenses/ by-nc-nd/ 4. 0/

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Section: Introductionmentioning

confidence: 99%

Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz

Mainik

Mitov²,

Rüschendorf

2015

Journal of Empirical Finance

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“…2) 이 외에도 왼쪽꼬리위험이 주식수익률과 유의한 양(+)의 관계를 가짐을 보여준 Iqbal et al(2013), Atilgan and Demirtas(2013), Chen et al(2014), Bollerslev et al(2015) 등을 참조할 수 있다. 그러나 일부 연구들은 꼬리위험과 기대수익률간에 음(-)의 관계를 보고한다 (DiTraglia and Gerlach, 2013;Baltussen et al, 2018;Long et al, 2019).…”

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Left-Tail Momentum of Korean Stock Markets

Eom¹,

Eom²,

Park³

2022

Korean J Financ Stud

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This study examines the relationship between left-tail risk and stock return in the Korean stock markets and ascertains whether there is a left-tail momentum phenomenon in which stocks with high left-tail risk have lower investment performance than those with low left-tail risk (Atilgan et al., 2020). The results reveal that left-tail risk has a significant negative relationship with stock returns for future holding periods and that the left-tail momentum is strongly observed in the Korean stock markets. The excess return of the Fama-French 3-factor model of the decile portfolio-based (H-L) zero-cost investment strategy using left-tail risk has a significant value of -0.67% to -0.73% per month on average. These results are robust even after controlling for changes in the left-tail risk measures or key related variables that could affect the left-tail momentum. In addition, the left-tail momentum has explanatory power regarding the fluctuations of stock returns independent of Fama-French 3-factors.

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“…To assess the vegetation changes in the DPR, we reproduced a 250-m-resolution FVC dataset (CD FVC) using the improved pixel bipartite model 53 , 54 based on a 250-m-resolution constructed normalized difference vegetation index (NDVI) dataset (CD NDVI) for 1982-2018 (for more details about the CD NDVI dataset, see Supplementary Note 3 ): where (or ) and (or ) correspond to the NDVI values representing surfaces with a fully covered dense vegetation and bare soils, respectively. To reduce the uncertainty and randomness involved in determining extreme NDVI values 55 , we first selected the NDVI values (NDVI > 0) at the 5% and 95% percentiles from the surface grid values as the annual and for each year, respectively, and then calculated the multiyear average and values as the bare soil pixel ( ) and the fully vegetated pixel ( ) in the study area for the period from 1982 to 2018.…”

Section: Methodsmentioning

confidence: 99%

Unintended consequences of combating desertification in China

Wang

Geng

et al. 2023

Nat Commun

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Since the early 2000s, China has carried out extensive “grain-for-green” and grazing exclusion practices to combat desertification in the desertification-prone region (DPR). However, the environmental and socioeconomic impacts of these practices remain unclear. We quantify and compare the changes in fractional vegetation cover (FVC) with economic and population data in the DPR before and after the implementation of these environmental programmes. Here we show that climatic change and CO2 fertilization are relatively strong drivers of vegetation rehabilitation from 2001-2020 in the DPR, and the declines in the direct incomes of farmers and herders caused by ecological practices exceed the subsidies provided by governments. To minimize economic hardship, enhance food security, and improve the returns on policy investments in the DPR, China needs to adapt its environmental programmes to address the potential impacts of future climate change and create positive synergies to combat desertification and improve the economy in this region.

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Portfolio selection: An extreme value approach

Cited by 24 publications

References 63 publications

Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz

Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz

Left-Tail Momentum of Korean Stock Markets

Unintended consequences of combating desertification in China

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