Assessing Performance of Liquidity Adjusted Value-at-Risk Models

Daka, Vandana Rao; Basu, Sankarshan

doi:10.5430/ijfr.v7n5p87

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…However, Ernst et al (2012) identified the non‐normality issue in the spread modeling of the Bangia et al (1998) approach. To relax the normality assumption, Daka and Basu (2016) designed an L‐VaR model using Cornish–Fisher expansion. However, one can also apply EVT, which deals with the tail distribution and accounts for non‐normality in return and spread distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

Liquidity‐adjusted value‐at‐risk using extreme value theory and copula approach

Kamal,

Paul

2024

Journal of Forecasting

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In this study, we propose the application of the GARCH‐EVT‐Copula model in estimating liquidity‐adjusted value‐at‐risk (L‐VaR) of energy stocks while modeling nonlinear dependence between return and bid‐ask spread. Using the L‐VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non‐zero skewness, excess kurtosis, and volatility clustering of both return and spread distributions of energy stocks. Moreover, to measure the nonlinear dependence between return and spread series, we use multiple copulas: Clayton, Gumbel, Frank, Normal, and Student‐t. Based on the statistical backtesting and economic loss functions, our results suggest that the GARCH‐EVT‐Clayton copula is superior and most consistent in forecasting L‐VaR compared with other competing models. This finding has several implications for investors, market makers, and daily traders who appreciate the importance of liquidity in market risk computation.

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Section: Literature Reviewmentioning

confidence: 99%

Liquidity‐adjusted value‐at‐risk using extreme value theory and copula approach

Kamal,

Paul

2024

Journal of Forecasting

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“…6. For other useful literature on asset pricing, liquidity risk and portfolio choice, see for instance, Grillini et al (2019), Arreola-Hernandez and Al Janabi (2020), Angelidis and Benos (2006), Berkowitz (2000), Hisata and Yamai (2000), Madhavan et al (1997), Le Saout (2002), Amihud et al (2005), Takahashi and Alexander (2002), Cochrane (2005), Meucci (2009), Arreola-Hernández et al (2017); Arreola-Hernández et al (2015), Daka and Basu (2016), etc. Furthermore, within the copula technique and particularly the vine copula approach, there were indeed very few studies in this respect and most of published research is still focused on the issue of transaction costs (i.e.…”

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

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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.

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“…Asimismo, un estudio reciente sobre la incorporación del riesgo liquidez en la estimación del valor en riesgo, es el realizado por Benito, López, & Arguedas, (2017) en donde incorporan el riesgo de liquidez utilizando modelos más sofisticados basados en la teoría del valor extremo (EVT). Es importante considerar que son variados los trabajos que en las últimas décadas han incorporado el riesgo de liquidez en la estimación del valor en riesgo (Giot & Grammig, 2006), (Angelidis & Benos, 2006), (Jin, 2017), (Madoroba & Kruger, 2014), (Weiß & Supper, 2013), (Daka & Basu, 2016), (Tran, T., & Nguyen, N, 2022). entre otros.…”

Section: Introductionunclassified

VeR Y RIESGO DE LIQUIDEZ EN CARTERAS DE ACCIONES EN TIEMPOS DE TURBULENCIAS FINANCIERAS

Tejos¹,

Larre

2022

COMPENDIUM

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Estudiamos si el riesgo de liquidez tiene una incidencia en la rentabilidad y riesgo de carteras de acciones en periodos de turbulencias financieras. En base a 30 acciones de empresas chilenas del período 2018-2020, se construyeron seis carteras compuestas por 5 acciones de empresas. La conformación se basó en acciones de diferentes sectores económicos. Se estimaron el valor en riesgo (VeR) con diferentes metodologías. Se verificó que las turbulencias del mercado afectaron a todas las carteras en forma similar; sin embargo, se observó que el riesgo de liquidez incrementa todas las potenciales pérdidas para todos los períodos y todas las carteras, reflejando la importancia de incorporar el riesgo de liquidez en la estimación del valor en riesgo.

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Assessing Performance of Liquidity Adjusted Value-at-Risk Models

Cited by 6 publications

References 19 publications

Liquidity‐adjusted value‐at‐risk using extreme value theory and copula approach

Liquidity‐adjusted value‐at‐risk using extreme value theory and copula approach

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

VeR Y RIESGO DE LIQUIDEZ EN CARTERAS DE ACCIONES EN TIEMPOS DE TURBULENCIAS FINANCIERAS

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