Systematic Tail Risk

Oordt, Maarten van; Zhou, Chen

doi:10.2139/ssrn.2367225

Cited by 21 publications

(25 citation statements)

References 44 publications

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“…The concept of tail risk proposed in the literature is based on different metrics. Most research on tail risk in asset pricing relies on univariate extreme value theory (Chapman, Gallmeyer, & Martin, ; Cholette, and Lu, ; Kelly & Jiang, ), or multivariate extreme value theory (Chabi‐Yo et al., ; Van Oordt & Zhou, ; Weigert, ). To the best of our knowledge, the only paper that proposes an alternative to the extreme value theory approach to the implications of tail risk in asset pricing is that by Bali et al.…”

Section: Literature Reviewmentioning

confidence: 99%

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Can tail risk explain size, book‐to‐market, momentum, and idiosyncratic volatility anomalies?

Aboura

Arısoy

2019

Business Fin & Account

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We examine the impact of tail risk on the return dynamics of size, book-to-market ratio, momentum and idiosyncratic volatility sorted portfolios. Our time-series analyses document significant portfolio return exposures to aggregate tail risk. In particular, portfolios that contain small, value, high idiosyncratic volatility and low momentum stocks exhibit negative and statistically significant tail risk betas. Our cross-sectional analyses at the individual stock level suggest that tail risk helps in explaining the four pricing anomalies, particularly size and idiosyncratic volatility anomalies. K E Y W O R D Sanomalies, idiosyncratic volatility, momentum, size, tail risk, value

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Section: Literature Reviewmentioning

confidence: 99%

“…Van Oordt and Zhou () further report that stocks with historically high tail betas suffer losses during market crashes. However, they conclude that the ability of systematic tail risk to explain the cross‐section of expected returns seems limited.…”

mentioning

confidence: 99%

Can tail risk explain size, book‐to‐market, momentum, and idiosyncratic volatility anomalies?

Aboura

Arısoy

2019

Business Fin & Account

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“…The first is the network econometrics literature that applies network models to summarize contagion channels among financial institutions and markets using stock market data (Ahelegbey et al, 2016a;Billio et al, 2012;Diebold and Yilmaz, 2014). The second contribution relates to research on the impact of tail risk on asset returns Almeida et al, 2017;Chabi-Yo et al, 2018;Harris et al, 2019;Mojtahedi et al, 2020;Van Oordt and Zhou, 2016). Van Oordt and Zhou (2016) studied a systematic tail risk measure that captures the sensitivity of asset returns to market returns conditional on market tail events.…”

Section: Introductionmentioning

confidence: 99%

Statistical Modelling of Downside Risk Spillovers

2020

SSRN Journal

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We extend the extreme downside hedge methodology to model sensitivity interconnectedness of market returns to the tail risk of other markets under turbulent conditions. We derive the interconnectedness via Bayesian graph structural learning. The empirical application examines the dynamic interconnectedness among 15 major markets, including G10 economies, during turbulent times. We investigate whether downside risk connections among these major markets are merely anecdotal or provide evidence of contagion and the most central market for spillover propagation. The result shows that the Covid-19 induced downside risk connections record the highest density, suggesting stronger evidence of contagion in the coronavirus pandemic than during the financial and eurozone crisis. Central to the spillover propagation is the finding that most of the transmitters and recipients of downside risk are EU markets.

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“…Our contribution is, firstly, the evaluation of extreme negative vs extreme positive market shifts separately and, secondly, its impact in a portfolio setting. Some research has been done on the asymmetric tail risk (Ang et al, 2006;van Oordt and Zhou, 2016) 2 , including a companion paper by Alexeev et al (2016) which only contrasts continuous and discontinuous systematic risks. In this paper, we use the inferential procedure of Li et al (2017) to extend the single jump beta to the positive and negative jump betas.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric jump beta estimation with implications for portfolio risk management

Alexeev

Urga

Yao

2019

International Review of Economics & Finance

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We evaluate the impact of extreme market shifts on equity portfolios and study the difference in negative and positive reactions to market jumps with implications for portfolio risk management. Employing high-frequency data for the constituents of the S&P500 index over the period 2 January 2003 to 30 December 2017, we investigate to what extent the portfolio exposure to the downside and upside jumps can be mitigated. We contrast the risk exposure of individual stocks with those of the portfolios as the number of holdings increases. Varying the jump identification threshold, we show that the number of holdings required to stabilise portfolios' sensitivities to negative jumps is higher than when positive jumps are considered and that the asymmetry is more prominent for more extreme events. Ignoring this asymmetry results in under-diversification of portfolios and increases exposure to sudden extreme negative market shifts.

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Systematic Tail Risk

Cited by 21 publications

References 44 publications

Can tail risk explain size, book‐to‐market, momentum, and idiosyncratic volatility anomalies?

Can tail risk explain size, book‐to‐market, momentum, and idiosyncratic volatility anomalies?

Statistical Modelling of Downside Risk Spillovers

Asymmetric jump beta estimation with implications for portfolio risk management

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