Tail Event Driven ASset allocation: evidence from equity and mutual funds’ markets

Härdle, Wolfgang Karl; Lee, David Kuo Chuen; Nasekin, Sergey; Petukhina, Alla

doi:10.1057/s41260-017-0060-9

Cited by 10 publications

(3 citation statements)

References 60 publications

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“…Bri ere et al (2015) found that cryptocurrencies have different statistical characteristics from oil, gold and other assets. They have also been found to be more volatile but often result in greater returns than traditional assets (Bri ere et al, 2015;H€ ardle et al, 2018a;Srilakshmi and Karpagam, 2017).…”

Section: Data and Samplementioning

confidence: 99%

Cryptocurrencies and portfolio diversification in an emerging market

Letho

Chelwa

Alhassan

2022

CFRI

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PurposeThis paper examines the effect of cryptocurrencies on the portfolio risk-adjusted returns of traditional and alternative investments within an emerging market economy.Design/methodology/approachThe paper employs daily arithmetic returns from August 2015 to October 2018 of traditional assets (stocks, bonds, currencies), alternative assets (commodities, real estate) and cryptocurrencies. Using the mean-variance analysis, the Sharpe ratio, the conditional value-at-risk and the mean-variance spanning tests.FindingsThe paper documents evidence to support the diversification benefits of cryptocurrencies by utilising the mean-variance tests, improving the efficient frontier and the risk-adjusted returns of the emerging market economy portfolio of investments.Practical implicationsThis paper firmly broadens the Modern Portfolio Theory by authenticating cryptocurrencies as assets with diversification benefits in an emerging market economy investment portfolio.Originality/valueAs far as the authors are concerned, this paper presents the first evidence of the effect of diversification benefits of cryptocurrencies on emerging market asset portfolios constructed using traditional and alternative assets.

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Section: Data and Samplementioning

confidence: 99%

Cryptocurrencies and portfolio diversification in an emerging market

Letho

Chelwa

Alhassan

2022

CFRI

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“…Example 3 (Portfolio allocation) A successful asset allocation takes the portfolio tail structure into account. Consider, for example, the Tail Event Driven ASset allocation (TEDAS) framework that was developed by Härdle et al (2015). Generally speaking, TEDAS enables us to broaden the classical portfolio allocation by using quantile regression between the index series and portfolio constituents at selected fixed tail levels (e.g., 5%, 15%, 25%, 35% and 50%).…”

Section: Tail Event Risk Examplesmentioning

confidence: 99%

“…For further details, we refer to Härdle et al (2015). As frequently encountered in risk management, both cases result in relatively short time series due to weekly or monthly rebalancing schemes.…”

Section: Tail Event Risk Examplesmentioning

confidence: 99%

TERES: Tail Event Risk Expectile Shortfall

Mihoci

Härdle

Chen

2020

Quantitative Finance

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We propose a generalized risk measure for expectile-based expected shortfall estimation. The generalization is designed with a mixture of Gaussian and Laplace densities. Our plug-in estimator is derived from an analytic relationship between expectiles and expected shortfall. We investigate the sensitivity and robustness of the expected shortfall to the underlying mixture parameter specification and the risk level. Empirical results from US, German and UK stock markets and for selected NASDAQ blue chip companies indicate that expected shortfall can be successfully estimated using the proposed method on a monthly, weekly, daily, and intra-daily basis using a one-year or one-day time horizon across different risk levels.

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