The Importance of Measuring Liquidity Risk with Smart Financial Applications

Abstract

Over the past few days, alarm bells have been ringing for the risk of recession in the world’s leading economies (Germany, United Kingdom, Italy, Brazil and Mexico). Deceleration is affecting several regions in the world and might even become more widespread, exacerbating investor mistrust and financial market instability produced by the U.S.-China trade war, among other uncertainty factors. Investors have transferred part of their investment portfolios to safe haven assets, such as sovereign bonds, with investor mistrust at record lows in recent years. Managing your investment portfolio when an economic cycle is drawing to an end can be tricky. Even though some experts recommend rebalancing asset allocation, buying treasury bonds, focusing on raw materials, commodities or real estate investment, the most important thing is to learn how to manage risk. In the current context of uncertainty and adverse market conditions, assessing investment portfolio performance optimization, as well as liquidity risk, is crucial, as explained in the chapter “Theoretical and practical foundations of liquidity-adjusted value-at-risk (LVaR): optimization algorithms for portfolio selection and management” of the recently published book Expert Systems in Finance. Smart Financial Applications in Big Data Environments (Routledge, Taylor & Francis Group, 2019).

Keywords: Emerging Markets, Liquidity-Adjusted Value at Risk, Liquidity Risk, Machine Learning, Portfolio Management, Risk Management

JEL Classification: C10, C13, G20, and G28