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

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.

Performance Analysis of Fundamentally-Weighted Indices in the Croatian Capital Market

The work of Arnott et al. (2005) presented an interesting fact that the fundamentally-weighted indices generally outperform the market capitalisation-weighted counterparts in the US stock market. The research results prompted the introduction of fundamentally-weighted indices in the US market. Since research dealing with Croatian capital market also points out the inefficiency of the risk return trade-off of the cap-weighted (CROBEX) index this paper examines more closely the risk return characteristics of the potential fundamentally-weighted alternative and analyses the source of higher returns in the case of fundamentally-weighted indices. We use the original and propose a modified Fama French three factor model in order to try to capture specific sources of risk in the small and illiquid market. We find evidence in support of the view that better risk return trade-off of the fundamentally-weighted indices is driven by additional exposure to risk factors in comparison to CROBEX index.

A Drivers of Islamic Bond Liquidity in Malaysia: Latent Liquidity Approach

A steady liquidity level is an importance characteristic of a financial market, especially after the 2008 financial crisis. The Islamic financial market was virtually isolated from the crisis. It is interesting to explore the underlying determinants that stabilise a market’s liquidity level. This paper studies the determinants of a Sukuk’s liquidity level in the Malaysian bond market using a new liquidity measure known as latent liquidity. The measure does not require transaction data, which makes it applicable to an illiquid market such as the Malaysian bond market. Utilising data from the Malaysian bond market, the paper involves two steps of data analyses, namely an insight into the trend and the liquidity level of the Sukuk market. It then continues to investigate the driver of Sukuk’s liquidity using the latent liquidity as a proxy against five Sukuk characteristics in a random effect regression model. Four variables issuance amount, maturity, coupon rate, and age are found to be significant drivers of Sukuk’s liquidity level. Conclusions drawn from the regression results indicate Sukuk’s investors’ preference in matching long term Sukuk with their long term liabilities, in addition to their fondness for keeping their Sukuk to amortise the return.

An illiquid market in the desert: estimating the cost of water trade restrictions in northern Chile

This paper estimates the cost of a policy to restrict water trades to mining firms in northern Chile in order to protect riparian ecosystems and indigenous agriculture. In response to the policy, mining firms have developed high-cost desalination and pumping facilities to secure adequate water supplies. We develop a methodology and estimate the cost of market transactions that fail to occur due to the policy. Lost trade surplus is estimated at US$52 million per year. Without trade restrictions, around 86 per cent of the remaining agricultural water in the region would be transferred to mining.