The 2007-2009 global financial crisis emphasized the need for rigorous integration of asset liquidity trading risk into value at risk (VaR) modeling algorithms. In this chapter, the author examines measures of certain kinds of liquidity risk that is useful for completing the definition of market risk and for forecasting liquidity-adjusted VaR (L-VaR) under illiquid and intricate market outlooks. This chapter proposes robust modeling algorithms for the quantification of liquidity risk for portfolios that consist of multiple-assets. The empirical testing is performed using data of emerging and Islamic Gulf Cooperation Council stock markets. To that end, the author simulates diverse portfolios and determines the risk-capital and risk-budgeting constraints. The optimization algorithms are interesting in terms of theory as well as practical applications, particularly in light of the 2007-2009 global financial meltdown. The optimization algorithms can have important uses and applications in expert systems, machine learning, and financial technology (FinTech) in big data environments.
TopIntroduction And Overview
The traditional Value at Risk (VaR) method for the computation of market risk for multiple-assets portfolios does not explicitly consider asset liquidity risk. Conventional VaR models assess the worst change in the mark-to-market multiple-assets portfolios value over a given time horizon but do not account for the actual trading risk of liquidation. Customary fine-tunings are made on an ad-hoc basis. At most, the holding period (it is routinely also known as the closeout, unwinding or liquidation period) over which the VaR number is calculated is adjusted to ensure the inclusion of asset liquidity risk (Al Janabi, 2010). As a result, asset liquidity risk can be imprecisely factored into VaR assessments by assuring that the liquidation horizon is as a minimum larger than an orderly liquidation interval. Moreover, the same liquidation horizon is employed to all trading asset classes, albeit some assets may be more liquid than others. Neglecting asset liquidity risk can lead to an underestimation of the overall market risk and misapplication of capital cushion for the safety and soundness of financial institutions. In emerging and Islamic financial markets, which are relatively well considered as illiquid, ignoring asset liquidity risk can result in significant underestimation of the VaR assessment, and especially under severe market conditions (Al Janabi, 2008 and 2009).
In its most general definitions, asset liquidity risk is depicted as the risk that the financial institution will not have sufficient funding at any period (i.e., has not enough cash at hand to meet its short-term obligations) or the risks associated with the market liquidity of financial assets—which can be considered as part of market risk and thus it is a function of the price impact of trades and the size of trading holdings. Comprehension of liquidity risk demands knowledge of several diverse fields, such as market microstructures. In general terms, market liquidity risk may arise due to the following circumstances (Al Janabi, 2011):
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When there is a mismatch between the contractual maturities (or more precisely: cash flows) of assets and liabilities; a large cash flow mismatch could for instance arise when treasury unit takes large interest rate trading positions.
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The inability of the financial entity to fund its assets, which is a function of its creditworthiness.
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Because of unexpected non-performance or late payments of debtors.
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When the market is illiquid and the financial entity unexpectedly cannot dispose assets (unwind or closeout trading positions) or attract new funds—due to credit crunch or event risk. This is related to the necessary time to closeout a position. Furthermore, the time to unwind a position may increase in event situations.
This chapter provides advanced quantitative risk management modeling techniques and simulation strategies that can be applied to equity trading portfolios in emerging and Islamic markets. The intent of this chapter is to examine a robust modeling technique for including of asset liquidation trading risk in typical VaR analysis. This chapter expands earlier techniques by explicitly modeling the liquidation of trading portfolios, over the holding period, with the aid of an appropriate scaling of the multiple-assets’ liquidity-adjusted VaR (L-VaR) modeling algorithms. The key methodological contribution is a different and less conservative asset liquidity-scaling factor than the conventional root-t multiplier. The proposed add-on is a function of a predetermined liquidity threshold defined as the maximum position that can be unwound without disturbing market prices during one trading day.