Dependencies and systemic risk in the European insurance sector: New evidence based on Copula-DCC-GARCH model and selected clustering methods

Denkowska, Anna; Wanat, Stanisław

doi:10.15678/eber.2020.080401

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…They find that despite the fact that the banking sector is the most systemically risky financial sector in the U.S., the insurance sector is becoming a systemically risky sector through interdependence with the financial sector and the real economy. Similar conclusions are drawn by Denkowska and Wanat (2020), who employ a copula-DCC-GARCH model and CoVaR to analyse systemic risk in the European insurance sector between 2005-2018. They find that the European insurance sector contributes more to systemic risk during financial market distress because of the stronger interdependence between insurance companies.…”

Section: Literature Reviewsupporting

confidence: 70%

Systemic risk and real economic activity: A South African insurance stress index of systemic risk

Mwamba¹,

Angaman²

2022

AAMJAF

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This study investigates the link between systemic risk in the South African insurance sector real economic activity in South Africa. To this end, we use six systemic risk measures, the Conditional Value at Risk (CoVaR), the Marginal Conditional Value at Risk (ΔCoVaR), the Comovement and Interconnectedness of the South African insurance sector (Eigen), the Dynamic Mixture Copula Marginal Expected Shortfall (DMC-MES), the Average Conditional Volatility (Ave-vol), and the South African Volatility Index (SAVI). We first evaluate the significance of each measure by assessing its ability to forecast future economic downturns in South Africa. We find that only two systemic risk measures possess the ability to predict future economic downturns in South Africa. We then use principal component quantile regression analysis to aggregate these measures into a composite stress index of systemic risk for the South African insurance sector and assess the ability of the proposed index to predict future economic downturns in South Africa. Our results reveal that the proposed index is a good predictor of future economic downturns in South Africa. Thus, our results suggest that regulators and risk managers must develop an analysis of systemic risk in the insurance sector with particular attention to its effects on real economic activity. In addition, our index can potentially be used as an instrument to monitor and mitigate systemic risk in the insurance sector in order to ensure the stability of the financial system and the economy in South Africa.

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Section: Literature Reviewsupporting

confidence: 70%

Systemic risk and real economic activity: A South African insurance stress index of systemic risk

Mwamba¹,

Angaman²

2022

AAMJAF

View full text Add to dashboard Cite

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“…Statistically, this ACF value should be located at −1 and 1. Furthermore, data are said to be stationary when the ACF value at each lag is equal to 0, and non-stationary when the ACF value at each lag is not equal to 0 or is relatively high (Eliyawati, 2012;Denkowska & Wanat, 2020). The augmented Dickey-Fuller (ADF) test is also used to ensure the data series is stationary.…”

Section: Data Stationarity Testmentioning

confidence: 99%

Decision-making model to predict auto-rejection: An implementation of ARIMA for accurate forecasting of stock price volatility during the Covid-19

Suripto¹

2023

10.5267/j.dsl

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This study aims to determine an accurate forecasting model, especially an error rate of around 0, and to examine how the automatic rejection system reacts to stock price as a result of the pandemic. The statistical clustering method is used for the dataset in form of daily observations, while the sample covers the period of cases before and after COVID-19 pandemic from 02 January 2019 to 20 June 2020 at the Trinitan Minerals and Metal Company. Furthermore, the data used in the estimation are the opening and closing price of returns, which are later processed using SAS analysis tools. It is shown that the most appropriate decision-making processes are those proven to be most effective. Therefore, predicting future events based on a suitable time series model will help policymakers and strategists make decisions and develop appropriate strategic plans regarding the stock market. Meanwhile, 98% of the ARIMA (1,1,1) is a forecasting model which can be applied to predict stock prices. The new approach of this study is an integrated autoregressive moving average used as an attempt to accurately predict stock prices during a pandemic.

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“…Shocks from specific financial events allow us to clearly understand the time-varying process of the financial markets' connectedness, but cyclical elements will inevitably produce heterogeneous shocks resulting in various sources of connectedness and thus short-and long-term systemic risk (Denkowska & Wanat, 2020). It is necessary to understand whether shocks originate in the short-or long-term, which is also a way to understand the propagation cycle of shocks (see Engle & Granger, 1987;Dew-Becker & Giglio, 2016).…”

Section: Total Connectedness Of Financial Cyclesmentioning

confidence: 99%

The mean reversion/persistence of financial cycles: Empirical evidence for 24 countries worldwide

Zhang²,

Fan³

et al. 2023

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Research background: The globalization trend has inevitably enhanced the connectivity of global financial markets, making the cyclicality of financial activities and the spread of market imbalances have received widespread attention, especially after the global financial crisis. Purpose of the article: To reduce the negative effects of the contagiousness of the financial cycles, it is necessary to study the persistence of financial cycles and carve out the total connectedness, spillover paths, and sources of risks on a global scale. In addition, understanding the relationship between the financial cycle and economic development is an important way to prevent financial crises. Methods: This paper adopts the nonlinear smoothing transition autoregressive (STAR) model to extract cyclical and phase characteristics of financial cycles based on 24 countries during 1971Q1?2015Q4, covering developed and developing countries, the Americas, Europe, and Asia regions. In addition, the frequency connectedness approach is used to measure the connectedness of financial cycles and the relationship between the global financial cycle and the global economy. Findings & value added: The analysis reveals that aggregate financial cycles persist for 13.3 years for smoothed and 8.7 years for unsmoothed on average. The national financial cycles are asynchronous and exhibit more prolonged expansions and faster contractions. The connectedness of financial cycles is highly correlated with systemic crises and contributes to the persistence and harmfulness of shocks. It is mainly driven by short-term components and exhibits more pronounced interconnectedness within regions than across regions. During the financial crisis, the global financial cycle movements precede and are longer than the business fluctuations. Based on the study, some policy implications are presented. This paper emphasizes the impact of systemic crises on the persistence of financial cycles and their connectedness, which contributes to refining research related to the coping mechanisms of financial crises.

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Dependencies and systemic risk in the European insurance sector: New evidence based on Copula-DCC-GARCH model and selected clustering methods

Cited by 8 publications

References 32 publications

Systemic risk and real economic activity: A South African insurance stress index of systemic risk

Systemic risk and real economic activity: A South African insurance stress index of systemic risk

Decision-making model to predict auto-rejection: An implementation of ARIMA for accurate forecasting of stock price volatility during the Covid-19

The mean reversion/persistence of financial cycles: Empirical evidence for 24 countries worldwide

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