Systemic liquidity risk and portfolio theory: An application to the Italian financial markets

Iachini, Eleonora; Nobili, Stefano

doi:10.1016/j.srfe.2015.12.001

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…Cabe remarcar que los inversionistas exigen mayores primas de liquidez para aquellos activos que presentan mayor riesgo de liquidez en el mercado. Este enfoque enfatiza que la liquidez se valora por los costes de negociación, así como por ser una fuente de riesgo, puesto que cambia de manera inesperada a lo largo del tiempo (Iachini y Nobili, 2016).…”

Section: Liquidezunclassified

Liquidez y rentabilidad de las Cajas Municipales de Ahorro y Crédito del Perú: Periodo 2020 – 2022

2024

RCS

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There are two financial indicators that are fundamental pillars for the sustainability and fulfillment of the objectives of the Municipal Savings and Credit Banks of Peru: Liquidity and profitability. In this sense, this research sought to determine how liquidity influences the profitability of these institutions, during the periods between 2020 and 2022. An investigation was carried out under a quantitative, nonexperimental longitudinal procedure with a correlational-causal scope. which had a population and census sample comprised of 11 Municipal Savings and Credit Banks authorized, regulated and supervised by the Superintendency of Banking, Insurance and Private Pension Fund Administrators. Likewise, the information was grouped and recorded through documentary analysis, designing a data sheet as an instrument. With a value of -0.627 and a significance of less than 5%, it was found that liquidity had a negative and significant influence on the profitability of the Municipal Savings and Credit Banks, during the years 2020 to 2022. Finally, it is inferred that the liquidity and profitability behaviors of these institutions showed that greater liquidity will not always determine greater profitability.

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Section: Liquidezunclassified

Liquidez y rentabilidad de las Cajas Municipales de Ahorro y Crédito del Perú: Periodo 2020 – 2022

2024

RCS

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“…Most of the CFSI components are spreads (i.e., the interbank liquidity spread, corporate bond spread and liquidity spread) and two of the remaining CFSI components are ratios, and one is a measure of stock market volatility. Iachini and Nobili (2016) introduced an indicator for measuring systemic risk in the Italian financial market using the portfolio aggregation theory method. This portfolio aggregation was used to capture the systemic dimension of liquidity stress.…”

Section: A Brief History Of Fsimentioning

confidence: 99%

“…Although several studies (Illing and Liu 2006;Hakkio and Keeton 2009;Holló et al 2012;Huotari 2015;Iachini and Nobili 2016;Ilesanmi and Tewari 2019a) have been conducted to develop a simple composite index to measure stress within the financial system, there is no consensus as most of the studies differs either in terms of the number of the market segment to be included, variables to be used in each market segment, data frequency or methodologies. The financial stress indicator (FSI) is a single aggregate indicator that is constructed to reflect the systemic nature of financial instability and as well to measure the vulnerability of the financial system to both internal and external shocks.…”

Section: Introductionmentioning

confidence: 99%

Financial Stress Index and Economic Activity in South Africa: New Evidence

Ilesanmi

Tewari

2020

Economies

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The importance of a sound and stable financial system and by extension economic stability was brought to the fore by the global financial crisis (GFC). The economic and social costs of the GFC have renewed the commitment of stakeholders in the financial sector including central banks to develop instruments and methodologies that will be useful in monitoring financial stress within the financial system and the real economy. This study contributes to the growing literature by developing a financial stress indicator for the South African financial market. The financial stress indicator (FSI) is a single aggregate indicator that is constructed to reflect the systemic nature of financial instability and also to measure the vulnerability of the financial system to both internal and external shocks. Using the principal component analysis (PCA), the results show that financial stress can be identified by the financial stress indicator. Furthermore, using a recursive Vector Autoregression (VAR) model to estimate the impact of financial stress on output and investment, the result shows that financial stress has a negative impact on economic growth and investment, though not immediately. FSI is very useful for gauging the effectiveness of government measures to mitigate the impact of financial stress. Concerted effort to stimulate investment and domestic production by relevant stakeholders is necessary to mitigate the impact of financial stress. This will go a long way to alleviating the impact of the financial stress on industrial production, employment and the economy at large.

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“…We use three different sources of data to our model. To take information on market liquidity, we use the data on bid-ask spread δ P available on Bloomberg and which provides information on two main aspects: the country riskiness and liquidity tightness (Iachini and Nobili, 2016). Secondly, we use the Bankscope dataset from which we compute the liquidity ratio Liq i and the liquidity resilience proxy ν.…”

Section: Datamentioning

confidence: 99%

“…6 using the 10 years maturity government bonds. In normal time, the bid-ask spreads mostly synthesize market structural features, while during turbulent periods it becomes a very responsive indicator of liquidity tightness (market liquidity risk) (Iachini and Nobili, 2016;Kyle, 1985).…”

Section: Bid-ask Spread Datamentioning

confidence: 99%

Systemic liquidity contagion in the European interbank market

Macchiati,

Brandi,

Cimini

et al. 2019

Preprint

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Systemic liquidity risk, defined by the IMF as "the risk of simultaneous liquidity difficulties at multiple financial institutions", is a key topic in macroprudential policy and financial stress analysis. Specialized models to simulate funding liquidity risk and contagion are available but they require not only banks' bilateral exposures data but also balance sheet data with sufficient granularity, which are hardly available. Alternatively, risk analyses on interbank networks have been done via centrality measures of the underlying graph capturing the most interconnected and hence more prone to risk spreading banks. In this paper, we propose a model which relies on an epidemic model which simulate a contagion on the interbank market using the funding liquidity shortage mechanism as contagion process. The model is enriched with country and bank risk features which take into account the heterogeneity of the interbank market. The proposed model is particularly useful when full set of data necessary to run specialized models is not available. Since the interbank network is not fully available, an economic driven reconstruction method is also proposed to retrieve the interbank network by constraining the standard reconstruction methodology to real financial indicators. We show that the contagion model is able to reproduce systemic liquidity risk across different years and countries. This result suggests that the proposed model can be successfully used as a valid alternative to more complex ones.

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Systemic liquidity risk and portfolio theory: An application to the Italian financial markets

Cited by 8 publications

References 35 publications

Liquidez y rentabilidad de las Cajas Municipales de Ahorro y Crédito del Perú: Periodo 2020 – 2022

Liquidez y rentabilidad de las Cajas Municipales de Ahorro y Crédito del Perú: Periodo 2020 – 2022

Financial Stress Index and Economic Activity in South Africa: New Evidence

Systemic liquidity contagion in the European interbank market

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