Conditional Value-at-Risk, spectral risk measures and (non-)diversification in portfolio selection problems – A comparison with mean–variance analysis

Brandtner, Mario

doi:10.1016/j.jbankfin.2013.02.009

Cited by 45 publications

(13 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Criteria for portfolio selection based on the tail properties of the asset returns distribution often choose a corner solution, meaning that most weight goes to the asset with the thinnest tail. This has been observed by, for instance, Jansen et al (2000), Hartmann et al (2004), Poon et al (2003), Adam et al (2008), and Brandtner (2013). The theoretical explanation for this is linked with a result from Geluk and de Haan (1987).…”

Section: Introductionmentioning

confidence: 67%

“…This requires the estimation of possibly many high-order crossmoments; see Martellini and Ziemann (2010). Another stream focuses on constraining the portfolio downside risk via Value-at-Risk (VaR), conditional Value-at-Risk (CVaR) or spectral risk measures; see Uryasev (2000, 2002), Alexander and Baptista (2002), Adam et al (2008), Brandtner (2013). This literature focuses mostly on probabilistic properties and estimation methods rather than on the economic significance of considering the possibility of large losses in the criteria for portfolio selection.…”

Section: Introductionmentioning

confidence: 99%

“…Jansen et al (2000) concentrate on estimating the portfolio fat-tail distribution using the safety-first principle combined with extreme value theory to limit downside risk. More recently Jondeau and Rockinger (2006) introduce higher moments in portfolio selection and Adam et al (2008), and Brandtner (2013) concentrate on using spectral measures of risk. Through time portfolio selection has departed from the assumption of normality.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Economic Value of Controlling for Large Losses in Portfolio Selection

Dias

2011

SSRN Journal

View full text Add to dashboard Cite

Research on asset pricing has shown that investor preferences include asymmetry and tail heaviness which affects the composition of optimal portfolios. This article investigates the out-of-sample economic value of introducing the risk of very large losses in portfolio selection. We combine mean-variance analysis with conditional Value-at-Risk using the subadditivity property of conditional Value-at-Risk, and we introduce a two stage method that preserves diversification while controlling for large losses. We find that strategies that account both for variance and the probability of large losses outperform efficient meanvariance portfolios, during and after the global financial crisis. JEL classification: G11; G14

show abstract

Section: Introductionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Economic Value of Controlling for Large Losses in Portfolio Selection

Dias

2011

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…In our model, the risk reference of the capital‐constrained retailer is reflected by a spectral risk measure, thus our work is also relevant to the literature on operational decisions under spectral risk measures. The concept of spectral risk measure was first introduced by Acerbi (), and has been widely used in the finance literature (e.g., Adam et al., ; Brandtner, ). Fichtinger () appears to be the first to consider spectral risk measures in the inventory ordering and pricing problem.…”

Section: Literature Reviewmentioning

confidence: 99%

The supplier's optimal guarantee policy in newsvendor finance

Jiang

2019

Int Trans Operational Res

View full text Add to dashboard Cite

The retailer is a capital-constrained newsvendor and can borrow money from the bank if necessary. To help the retailer get a bank loan at a lower interest rate, the supplier provides guarantee for the retailer's loan up to a prespecified amount. In a Stackelberg game, the supplier decides the wholesale price and the guarantee amount as a leader, and then the retailer determines the order quantity and the amount of the loan as a follower. The supplier is risk-neutral while the retailer's risk preference is reflected by a spectral risk measure (risk-neutral, risk-averse, or risk-seeking). For a given wholesale price and guarantee amount, the retailer's objective function is quasi-concave in the order quantity. The optimal solutions for the supplier and the retailer are derived. The supplier's expected profit with optimized wholesale price increases with the guarantee amount, and thus the supplier's optimal policy is to provide a full guarantee for the retailer's loan. When the supplier can limit his guarantee responsibility by a proportion of the outstanding loan obligation, the supplier's optimal policy is also to provide a full guarantee. Even if the retailer incurs bankruptcy costs in the event of repayment default, the supplier's optimal guarantee policy remains the same in these two different forms of limited guarantee. However, when the wholesale price is exogenous, that is, not a decision variable, the full guarantee is not necessarily optimal for supplier.

show abstract

“…There has been several studies on the need and benefits of portfolio diversification (Gaudecker, 2015;Levy and Levy, 2015;Zhou and Nicholson, 2015;Brandtner, 2013;Hung, Liu, and Tsai, 2012;Amenc and Martellini, 2011;Goetzmann and Kumar, 2008;De Santis and Gerard, 1997;Meric and Meric, 1989). Zhou and Nicholson (2015) constructed a diversified portfolio across 3 asset classes in the US economy.…”

Section: Literature Reviewmentioning

confidence: 99%