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Cited by 286

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

Is There an Intertemporal Relation between Downside Risk and Expected Returns?

Open access
Bali, Turan G., Demirtas, K. Ozgur, Levy, Haim·2009·Journal of Financial and Quantitative Analysis

Abstract

This paper examines the intertemporal relation between downside risk and expected stock returns. Value at Risk (VaR), expected shortfall, and tail risk are used as measures of downside risk to determine the existence and significance of a risk-return tradeoff. We find a positive and significant relation between downside risk and the portfolio returns on NYSE/AMEX/Nasdaq stocks. VaR remains a superior measure of risk when compared with the traditional risk measures. These results are robust across different stock market indices, different measures of downside risk, loss probability levels, and after controlling for macroeconomic variables and volatility over different holding periods as originally proposed by Harrison and Zhang (1999).

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Crossref · 10.1017/s0022109009990159

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Metadata

Authors
Bali, T. G., Demirtas, K. O., & Levy, H.
Venue
Journal of Financial and Quantitative Analysis
Published
2009-08
Volume
44
Issue
4
Pages
883-909
ISSN
  • 0022-1090 (ISSN-L)
  • 1756-6916
OpenAlex
W2097017827
Cited by
286
References
39
FWCI
22.97
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Is There an Intertemporal Relation between Downside Risk and Expected Returns? https://app.cochat.ai/papers/10.1017/s0022109009990159
The paper
Bali, T. G., Demirtas, K. O., & Levy, H. (2009). Is There an Intertemporal Relation between Downside Risk and Expected Returns? Journal of Financial and Quantitative Analysis. https://doi.org/10.1017/s0022109009990159

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.