Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets
The paper presents an alternative approach to measuring systemic illiquidity applicable to countries with frontier and emerging financial markets, where other existing methods are not applicable. We develop a novel Systemic Illiquidity Noise (SIN)-based measure, using the Nelson–Siegel–Svensson meth...
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doaj-b5a2b15d12454363b4d477d4be4a9d8d2021-07-23T14:04:56ZengMDPI AGRisks2227-90912021-07-01912412410.3390/risks9070124Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging MarketsEwa Dziwok0Marta A. Karaś1Department of Applied Mathematics, University of Economics in Katowice, 40-287 Katowice, PolandDepartment of Financial Investments and Risk Management, Wroclaw University of Economics and Business, 53-345 Wrocław, PolandThe paper presents an alternative approach to measuring systemic illiquidity applicable to countries with frontier and emerging financial markets, where other existing methods are not applicable. We develop a novel Systemic Illiquidity Noise (SIN)-based measure, using the Nelson–Siegel–Svensson methodology in which we utilize the curve-fitting error as an indicator of financial system illiquidity. We empirically apply our method to a set of 10 divergent Central and Eastern Europe countries—Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia—in the period of 2006–2020. The results show three periods of increased risk in the sample period: the global financial crisis, the European public debt crisis, and the COVID-19 pandemic. They also allow us to identify three divergent sets of countries with different systemic liquidity risk characteristics. The analysis also illustrates the impact of the introduction of the euro on systemic illiquidity risk. The proposed methodology may be of consequence for financial system regulators and macroprudential bodies: it allows for contemporaneous monitoring of discussed risk at a minimal cost using well-known models and easily accessible data.https://www.mdpi.com/2227-9091/9/7/124systemic risksystemic illiquidityliquidity crisisparametric modelsquantitative methodsemerging markets |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ewa Dziwok Marta A. Karaś |
spellingShingle |
Ewa Dziwok Marta A. Karaś Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets Risks systemic risk systemic illiquidity liquidity crisis parametric models quantitative methods emerging markets |
author_facet |
Ewa Dziwok Marta A. Karaś |
author_sort |
Ewa Dziwok |
title |
Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets |
title_short |
Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets |
title_full |
Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets |
title_fullStr |
Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets |
title_full_unstemmed |
Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets |
title_sort |
systemic illiquidity noise-based measure—a solution for systemic liquidity monitoring in frontier and emerging markets |
publisher |
MDPI AG |
series |
Risks |
issn |
2227-9091 |
publishDate |
2021-07-01 |
description |
The paper presents an alternative approach to measuring systemic illiquidity applicable to countries with frontier and emerging financial markets, where other existing methods are not applicable. We develop a novel Systemic Illiquidity Noise (SIN)-based measure, using the Nelson–Siegel–Svensson methodology in which we utilize the curve-fitting error as an indicator of financial system illiquidity. We empirically apply our method to a set of 10 divergent Central and Eastern Europe countries—Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia—in the period of 2006–2020. The results show three periods of increased risk in the sample period: the global financial crisis, the European public debt crisis, and the COVID-19 pandemic. They also allow us to identify three divergent sets of countries with different systemic liquidity risk characteristics. The analysis also illustrates the impact of the introduction of the euro on systemic illiquidity risk. The proposed methodology may be of consequence for financial system regulators and macroprudential bodies: it allows for contemporaneous monitoring of discussed risk at a minimal cost using well-known models and easily accessible data. |
topic |
systemic risk systemic illiquidity liquidity crisis parametric models quantitative methods emerging markets |
url |
https://www.mdpi.com/2227-9091/9/7/124 |
work_keys_str_mv |
AT ewadziwok systemicilliquiditynoisebasedmeasureasolutionforsystemicliquiditymonitoringinfrontierandemergingmarkets AT martaakaras systemicilliquiditynoisebasedmeasureasolutionforsystemicliquiditymonitoringinfrontierandemergingmarkets |
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