Measuring financial systemic stress for Turkey: A search for the best composite indicator

Chadwick, Meltem; Öztürk, Hüseyin

doi:10.1016/j.ecosys.2018.09.004

Cited by 16 publications

(8 citation statements)

References 16 publications

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“…As a result, they find the main determinants of systemic risk: the market share of bank pairs, the amount of non-performing loans, heard of behavior of banks, and the volatilities of macrovariables. Chadwick and Ozturk (2018) establish 15 different single financial stress indicators for Turkey using principal component analysis, basic portfolio theory, variance equal weights and Bayesian dynamic factor model. They use 14 variables representing five different markets and attempted to find a best indicator for Turkey and compare the results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They use XBANK as a financial indicator for Turkey in their study. Chadwick and Ozturk (2018) use XBANK as the most common variable that represent banking stress in order to construct a single financial stress indicator for Turkey. Sener et al (2019) use XBANK in order to examine the Turkish financial market in their study.…”

Section: Datamentioning

confidence: 99%

“…Aydin and Cavdar (2015) investigate the relationship between the exchange rate of US Dollar-Turkish Lira (USD/TRY), gold prices and the Borsa Istanbul (BIST) 100 index. In this study, BIST100 index is used as an indicator for the Turkish financial market as in the other studies such as Hatipoglu and Umut (2019), Chadwick and Ozturk (2018) and Sengul and Yilmaz (2019). In the literature, it is seen that the equity volatility is chosen as a common variable in many researches on systemic risk.…”

Section: Datamentioning

confidence: 99%

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Identifying the systemically important banks of Turkey with the CoVaR method

Civan¹,

Şimşek

Akay

2020

Heliyon

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The purpose of this paper is to measure the systemic risk contributions of Turkish banks and to identify the systemically important banks of Turkey during the period from 2005 to 2016. We apply the conditional value-at-risk (CoVaR) method proposed by Adrian and Brunnermeier (2009) using quantile regression. The study includes thirteen major banks of Turkey, including both public and private banks, out of a total of 52 banks. The banks are ranked in terms of their systemic risk contribution to the Turkish financial system based on their asset returns, macroeconomic variables and individual bank variables. The study reveals that Akbank, Garanti, Yapi Kredi and Isbank have the highest systemic risk contribution to the financial system when adding macroeconomic variables to the model. This ranking is changed to Yapi Kredi, Garanti, TEB, Sekerbank and Akbank when taking into account bank-specific variables. One surprising result is that risk in isolation and the spillover risks of public banks are smaller than in large private banks. Furthermore, the marginal systemic risk contributions of public banks are smaller than those of private banks. In conclusion, authorities improve the regulatory framework according to the context of CoVaR in addition to monitor the idiosyncratic risks of banks.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

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Identifying the systemically important banks of Turkey with the CoVaR method

Civan¹,

Şimşek

Akay

2020

Heliyon

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“…It should be noted that process of conceptualization and defining the financial stress index has itself seen a serious attempt in literature. Chadwick and Ozturk [ 5 ] utilized principal component analysis (PCA) to construct a financial system stress indicator for Turkey on the basis of money, bond, forex, equity, and bank markets. Ishrakieh et al [ 25 ] developed a financial stress indicator for the Lebanese market comprising separate market sectors: the banking sector, equity, and forex market, and other sectors.…”

Section: Cognate Literaturementioning

confidence: 99%

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies

Ghosh

Pamučar

2022

Complex Intell. Syst.

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Global financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision. The current research resorts to two granular hybrid predictive frameworks to discover the inherent pattern of financial stress across several critical variables and geography. The predictive structure utilizes the Ensemble Empirical Mode Decomposition (EEMD) for granular time series decomposition. The Long Short-Term Memory Network (LSTM) and Facebook’s Prophet algorithms are invoked on top of the decomposed components to scrupulously investigate the predictability of final stress variables regulated by the Office of Financial Research (OFR). A rigorous feature screening using the Boruta methodology has been utilized too. The findings of predictive exercises reveal that financial stress across assets and continents can be predicted accurately in short and long-run horizons even at the time of steep financial distress during the COVID-19 pandemic. The frameworks appear to be statistically significant at the expense of model interpretation. To resolve the issue, dedicated Explainable Artificial Intelligence (XAI) methods have been used to interpret the same. The immediate past information of financial stress indicators largely explains patterns in the long run, while short-run fluctuations can be tracked by closely monitoring several technical indicators.

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“…They propose a composite indicator of systemic stress (CISS) for the Euro area using data from different market segments: equity, bond, money, forex markets and financial intermediaries. Along this line, Louzis and Vouldis (2012), Cerqueira and Murcia (2016) and Chadwick and Ozturk (2019) studied the FSI indices for Greece, Spain and Turkey, respectively.…”

Section: Evidence Frommentioning

confidence: 99%

Evaluating financial stress indicators: evidence from Indian data

Mundra

Bicchal

2020

JFEP

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Purpose The purpose of this study is to assess alternative financial stress indicators for India in terms of tracing crisis events, mapping with the business cycle and the macroeconomic effect of stress indices. Design/methodology/approach The study constructs the composite indicator of systemic stress of Hollo, Kremer and Lo Duca (2012) for India using two different methods for computing time-varying cross-correlation matrix, namely, exponentially weighted moving average (EWMA) and dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH). The derived indices are evaluated with widely used, equal variance and principal component weighting indices in terms of tracing stress events, mapping with the business cycles and the macroeconomic effect. For this purpose, the study identifies various episodes of financial stress and uses the business cycle dates in the sample covering from January 2001 to October 2018. Findings The results suggest that stress indices based on EWMA and DCC-GARCH accurately identify the well-known stress periods and capture the recession dates and show an adverse effect on economic activity. Primarily, the DCC-GARCH-based stress index emerges as a better indicator of stress because it efficiently locates all the major-minor events, traces the build-up of stress and reverts to the normal level during stable times. Practical implications The DCC-GARCH-based stress index is a very useful indicator for policymakers in regularly monitoring India’s financial conditions and providing timely identification of systemic stress to avoid adverse repercussion effects of the financial crisis. Originality/value The 2007–2008 financial crisis and subsequent recurrent instability in the financial markets highlighted the requirement for an appropriate financial stress indicator for a timely assessment of the system-wide financial stress. To the authors’ knowledge, this is the first study that incorporates the systemic nature of financial stress in the construction of stress indices for India and provides a holistic evaluation of the financial stress from an emerging country’s perspective.

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Measuring financial systemic stress for Turkey: A search for the best composite indicator

Cited by 16 publications

References 16 publications

Identifying the systemically important banks of Turkey with the CoVaR method

Identifying the systemically important banks of Turkey with the CoVaR method

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies

Evaluating financial stress indicators: evidence from Indian data

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