A Simple Indicator of Systemic Risk

Patro, Dilip K.; Qi, Meiling; Sun, Xian

doi:10.2139/ssrn.1569805

Cited by 28 publications

(25 citation statements)

References 29 publications

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“…Bank is an institution that is vulnerable to the financial and macroeconomic condition due to its function as the fund collector and distributor in the financial system (Hadad et al, 2003). Therefore, the bank default will affect the financial system that can lead into domino and systemic effect (Acharya, 2010;Patro et al, 2013). Lo (2008) mentioned that systemic risk could not be eliminated, where systemic events would give the negative effect to the financial market and economy (Patro et al, 2013), and also would cause bank closures by the monetary authority (Arena, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the bank default will affect the financial system that can lead into domino and systemic effect (Acharya, 2010;Patro et al, 2013). Lo (2008) mentioned that systemic risk could not be eliminated, where systemic events would give the negative effect to the financial market and economy (Patro et al, 2013), and also would cause bank closures by the monetary authority (Arena, 2008). If the financial institutions experience the default altogether, systemic risk will appear as the impact of this situation (Rodrigues-Moreno et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of this research lies on the usage of two systemic risk measurements associated with the credit risk and liquidity risk in four ASEAN banks. Patro et al (2013) assumed systemic risk as likelihood from previous systemic events or financial system failures caused by systemic events that have negatively impacted financial markets and the economy. Separately, Lo (2008) explained that systemic risk is different from systemic failure.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Credit and Liquidity Risk Against Systemic Risk in Four Asean Banks

Pangestuti¹

2019

j. ilm. akunt.

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This study examines the effect of credit risk and liquidity risk on the potential increases in systemic risk of the banking sector in four ASEAN banks. Two systemic risk measurements, namely dCoVaR and MES, are used in order to evaluate the effect of credit risk and liquidity risk on systemic risk of individual bank (dCoVaR) and systemic risk when the market is in distress (MES). The result from the regressions shows that credit risk and liquidity risk significantly affect systemic risk at the market distress. Meanwhile, credit risk and liquidity risk do not affect systemic risk of individual bank. The crisis affects systemic risk is showed by two regressions which are conducted in four ASEAN banks. The result is interesting because when the regression is conducted for all the countries, there is a positive and significant effect of crisis on systemic risk in four ASEAN banks, but when it is conducted for each country (as an additional analysis), not all the countries are affected by the crisis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effect of Credit and Liquidity Risk Against Systemic Risk in Four Asean Banks

Pangestuti¹

2019

j. ilm. akunt.

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“…Most studies focus on stock return correlations within a sector of a particular country or across countries (for example, Chiang et al 2015;Phylaktis and Xia 2009) or the correlation of international stock market returns (for example, Chiang et al 2007). Patro et al (2013) found that the correlation between banks' individual risks could explain their stock return correlations. Moreover, De Nicolo and Kwast (2002) and Binici et al (2013) documented that return correlations among banks' stocks could be explained by bank-specific factors such as their market shares, the size of their total loan portfolios and the level of their non-performing loans.…”

Section: Review Of the Relevant Literaturementioning

confidence: 99%

Determinants of equity return correlations: a case study of the Amman Stock Exchange

Alomari

Power

Tantisantiwong

2017

Rev Quant Finan Acc

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This paper seeks to explain time-varying correlations among equity returns. The literature has shown that fundamental and economic factors can explain stock returns or the volatility of markets. Here, panel data analysis is employed to examine whether these factors can also explain the comovement of stock returns. Time-varying correlations among sectoral indexes are estimated using a restricted multivariate threshold GARCH model with dynamic conditional correlation (DCC-MTGARCH) controlling for the asymmetric effects of news and the influence of financial crises. The empirical results from this panel data analysis show that equity return correlations can be explained not only by macroeconomic variables but also by fundamentals within an industry. JEL classifications: C58; G12; G14; G17

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“…Financial risks appear a strong contagion characteristic, which may lead to systemic financial risk (Martínez-Jaramillo et al, 2010;De Bandt & Hartmann, 2000). However, without high correlation among financial markets and institutions, it is hard for a separate event to evolve into overall systemic risk in a short time (Patro et al, 2013). With the increase of financial innovation and economic globalization, the co-movement and interaction effects among the US and European countries before and after crises.…”

Section: Introductionmentioning

confidence: 99%

Co-Movement and Interaction Effects across the Monetary, Foreign Exchange and Stock Markets: Evidence from China

Zhao¹,

Zhang²

2017

JFRM

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This paper investigates the issue of co-movement and interaction among the monetary, foreign exchange and stock markets by employing the data from China's financial markets. Based on the ICA-EGARCH-M model, we explore the volatility spillover effects so as to illustrate the overall co-movements across financial markets. Furthermore, in order to observe the multi-market dynamic relationship variation process, we calculate the dynamic correlation coefficients with the AG-DCC-MGARCH model. Our findings provide both static and dynamic evidence on the co-movement and interaction effects of financial markets which may lead to the systemic financial risk.

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A Simple Indicator of Systemic Risk

Cited by 28 publications

References 29 publications

The Effect of Credit and Liquidity Risk Against Systemic Risk in Four Asean Banks

The Effect of Credit and Liquidity Risk Against Systemic Risk in Four Asean Banks

Determinants of equity return correlations: a case study of the Amman Stock Exchange

Co-Movement and Interaction Effects across the Monetary, Foreign Exchange and Stock Markets: Evidence from China

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