VaR Risk Measures versus Traditional Risk Measures: An Analysis and Survey

Kaplanski, Guy; Kroll, Yoram

doi:10.2139/ssrn.271992

Cited by 8 publications

(2 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4. For further discussion on the individual properties of VaR and CVaR, see for instance, Kaplanski and Kroll (2002), Garcia et al (2007), Artzner et al (1999), Agarwal and Naik (2004), Rockafellar and Uryasev (2002), Acerbi and Tasche (2002), Rockafellar and Uryasev (2000, 2002). …”

Section: Notesmentioning

confidence: 99%

Multivariate portfolio optimization under illiquid market prospects: a review of theoretical algorithms and practical techniques for liquidity risk management

Janabi

2020

JM2

View full text Add to dashboard Cite

Purpose This study aims to examine the theoretical foundations for multivariate portfolio optimization algorithms under illiquid market conditions. In this study, special emphasis is devoted to the application of a risk-engine, which is based on the contemporary concept of liquidity-adjusted value-at-risk (LVaR), to multivariate optimization of investment portfolios. Design/methodology/approach This paper examines the modeling parameters of LVaR technique under event market settings and discusses how to integrate asset liquidity risk into LVaR models. Finally, the authors discuss scenario optimization algorithms for the assessment of structured investment portfolios and present a detailed operational methodology for computer programming purposes and prospective research design with the backing of a graphical flowchart. Findings To that end, the portfolio/risk manager can specify different closeout horizons and dependence measures and calculate the necessary LVaR and resulting investable portfolios. In addition, portfolio managers can compare the return/risk ratio and asset allocation of obtained investable portfolios with different liquidation horizons in relation to the conventional Markowitz´s mean-variance approach. Practical implications The examined optimization algorithms and modeling techniques have important practical applications for portfolio management and risk assessment, and can have many uses within machine learning and artificial intelligence, expert systems and smart financial applications, financial technology (FinTech), and within big data environments. In addition, it provide key real-world implications for portfolio/risk managers, treasury directors, risk management executives, policymakers and financial regulators to comply with the requirements of Basel III best practices on liquidly risk. Originality/value The proposed optimization algorithms can aid in advancing portfolios selection and management in financial markets by assessing investable portfolios subject to meaningful operational and financial constraints. Furthermore, the robust risk-algorithms and portfolio optimization techniques can aid in solving some real-world dilemmas under stressed and adverse market conditions, such as the effect of liquidity when it dries up in financial and commodity markets, the impact of correlations factors when there is a switching in their signs and the integration of the influence of the nonlinear and non-normal distribution of assets’ returns in portfolio optimization and management.

show abstract

Section: Notesmentioning

confidence: 99%

Multivariate portfolio optimization under illiquid market prospects: a review of theoretical algorithms and practical techniques for liquidity risk management

Janabi

2020

JM2

View full text Add to dashboard Cite

show abstract

“…As usual, the roots of any good concept like VaR can by found much early than it is noted by RiskMetrics "official mythology" and Holton (2002) takes the VaR back to 1922. We are not able to refer all those who contributed to VaR development as one of most effective and useful risk measures and mention here only few (Malkiel, 1981;Linsmeier and Pearson 1996;Marshall and Siegel, 1996;Simons, 1996;Duffie and Pan, 1997;Berkowitz and O'Brien, 2001;Manganelli and Engle, 2001;Kaplanski and Kroll, 2002;Holton, 2003;Jorion, 2006;Aramonte, Rodriguez and Wu 2011). Since RiskMetrics publications the VaR concept occupied permanent position in risk management monographs (Choudhry, 2013;Horcher, 2015).…”

Section: Introductionmentioning

confidence: 99%