Liquidity-adjusted value-at-risk optimization of a multi-asset portfolio using a vine copula approach

Janabi, Mazin A. M. Al; Ferrer, Román; Shahzad, Syed Jawad Hussain

doi:10.1016/j.physa.2019.122579

Cited by 35 publications

(49 citation statements)

References 35 publications

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“…Later researchers have employed a number of different and advanced GARCH methodologies in order to better capture interlinkages between Bitcoin and traditional assets. Al Janabi et al (2019) perform liquidity-adjusted Value-at-Risk (LVaR) optimization based on vine copulas and LVaR models and GARCH, EGARCH, GJR-GARCH and APARCH specifications concerning Bitcoin, stock markets of the G7 countries, gold and commodities. Empirical outcomes provide evidence that Bitcoin and gold are useful in improving the risk-return trade-off of the G7 stock portfolio.…”

Section: Bitcoin and Hedging And/or Diversifying Abilitiesmentioning

confidence: 99%

A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets

Kyriazis

2021

JRFM

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This study is an integrated survey of GARCH methodologies applications on 67 empirical papers that focus on cryptocurrencies. More sophisticated GARCH models are found to better explain the fluctuations in the volatility of cryptocurrencies. The main characteristics and the optimal approaches for modeling returns and volatility of cryptocurrencies are under scrutiny. Moreover, emphasis is placed on interconnectedness and hedging and/or diversifying abilities, measurement of profit-making and risk, efficiency and herding behavior. This leads to fruitful results and sheds light on a broad spectrum of aspects. In-depth analysis is provided of the speculative character of digital currencies and the possibility of improvement of the risk–return trade-off in investors’ portfolios. Overall, it is found that the inclusion of Bitcoin in portfolios with conventional assets could significantly improve the risk–return trade-off of investors’ decisions. Results on whether Bitcoin resembles gold are split. The same is true about whether Bitcoins volatility presents larger reactions to positive or negative shocks. Cryptocurrency markets are found not to be efficient. This study provides a roadmap for researchers and investors as well as authorities.

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Section: Bitcoin and Hedging And/or Diversifying Abilitiesmentioning

confidence: 99%

A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets

Kyriazis

2021

JRFM

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“…After analysing portfolio optimization literature, it can be seen that scientists often conclude that the portfolio selection problem should include additional parameters besides return and risk (Meghwani & Thakur, 2017;Sanchez-Roger et al, 2020;Siddique et al, 2020;Steuer et al, 2008). Examples of such criteria are liquidity (Al Janabi et al, 2019;Jana et al, 2009;Li & Zhang, 2021), skewness (Kerstens et al, 2008;Konno & Yamamoto, 2005;Pahade & Jha, 2021;Saborido et al, 2016), conditional value at risk (CVaR) (Aboulaich et al, 2010;Najafi & Mushakhian, 2015;Strub et al, 2019). Other scientists also broadly analysed various aspects of stock market and investment (Ogiugo et al, 2020, Dvorsky et al, 2020Masood et al, 2020;Giacomella, 2021;Becheikh, 2021;Zumente & Bistrova, 2021;Sl avik et al, 2021;Kasperovica & Lace, 2021;Nassar & Tvaronavi cien_ e 2021;Mura & Hajduov a, 2021), thus, these topics get proper attention.…”

Section: Literature Reviewmentioning

confidence: 99%

The possibilities and consequences of investment decisions by stepwise optimization

Neverauskienė

Tvaronavičienė

Rutkauskas

et al. 2021

Economic Research-Ekonomska Istraživanja

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“…In their paper, Madoroba and Kruger (2014) review and compare 10 liquidity risk VaR models, including Al Janabi model. The liquidity risk model is largely drawn from Al Janabi model (Al Janabi, 2012, 2013, 2014), Al Janabi et al (2017, 2019).…”

Section: Notesmentioning

confidence: 99%

“…In fact, some relevant studies have addressed the issues of liquidity risk but not necessarily within portfolio optimization context. Their main focus, in fact, was on modeling merely transaction costs (that is, the widening of the bid-ask spread), however, the effects of adverse market price impact have not been studies rigorously, though Al Janabi (2013, 2014), Al Janabi et al (2017, 2019) are an exception. For the sake of brevity, we discuss below a concise description of some of the suggested modeling techniques for liquidity risk, detailed as follows: Within the VaR technique, Jarrow and Subramanian (1997) present a market impact model of liquidity by taking into consideration the optimal liquidation of a portfolio over a fixed horizon.…”

Section: Introductionmentioning

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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Liquidity-adjusted value-at-risk optimization of a multi-asset portfolio using a vine copula approach

Cited by 35 publications

References 35 publications

A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets

A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets

The possibilities and consequences of investment decisions by stepwise optimization

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

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