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Measuring Financial Systemic Stress for Turkey: A Search for the Best Composite Indicator

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  • Meltem Gulenay Chadwick
  • Huseyin Ozturk

Abstract

In this study, we aim to construct a single financial stress indicator (FSI) for Turkey adopting weekly data between April 2005 and December 2016. To do so, we compose 15 different FSIs using 14 variables that will represent five different markets, i.e. money market, bond market, foreign exchange market, equity market and banking sector. We aggregate these five different markets using variety of techniques, including principal component analysis (PCA), basic portfolio theory, variance equal weights and Bayesian dynamic factor model. We compare 15 different FSIs on the basis of their relation to and forecasting power of different variables such as the growth rate of industrial production, OECD business condition index and OECD composite leading indicator for Turkey. Our results suggest that there does not exist a simple best indicator for Turkey that will measure the financial systemic stress. Some indicators offer a good forecasting power for economic growth while others have a stronger correlation with the systemic risk. Therefore, we offer a final FSI for Turkey conducting a model averaging method via a rolling-correlation based weighting scheme to benefit from the information content of all the FSIs and observe that the final FSI successfully indicate the tension periods.

Suggested Citation

  • Meltem Gulenay Chadwick & Huseyin Ozturk, 2018. "Measuring Financial Systemic Stress for Turkey: A Search for the Best Composite Indicator," Working Papers 1816, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    • Handle: RePEc:tcb:wpaper:1816
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    3. Kaelo Ntwaepelo & Grivas Chiyaba, 2022. "Financial Stability Surveillance Tools: Evaluating the Performance of Stress Indices," Economics Discussion Papers em-dp2022-06, Department of Economics, University of Reading.
    4. Ozcelebi, Oguzhan, 2020. "Assessing the impacts of financial stress index of developed countries on the exchange market pressure index of emerging countries," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 288-302.
    5. Ghosh, Indranil & Jana, Rabin K. & David, Roubaud & Grebinevych, Oksana & Wanke, Peter & Tan, Yong, 2024. "Modelling financial stress during the COVID-19 pandemic: Prediction and deeper insights," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 680-698.
    6. Xuan Lv & Menggang Li & Yingjie Zhang, 2022. "Financial Stability and Economic Activity in China: Based on Mixed-Frequency Spillover Method," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
    7. Bu, Ya & Du, Xin & Li, Hui & Yu, Xinghui & Wang, Yuting, 2023. "Research on the FinTech risk early warning based on the MS-VAR model: An empirical analysis in China," Global Finance Journal, Elsevier, vol. 58(C).

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    More about this item

    Keywords

    Financial stress indicators; Composite indicator of systemic stress; Principal component analysis; Bayesian dynamic factor model; Portfolio theory; Aggregation methods;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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