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The simpler, the better: Measuring financial conditions for monetary policy and financial stability

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  • Arrigoni, Simone
  • Bobasu, Alina
  • Venditti, Fabrizio

Abstract

In this paper we assess the merits of financial condition indices constructed using simple averages versus a more sophisticated alternative that uses factor models with time varying parameters. Our analysis is based on data for 18 advanced and emerging economies at a monthly frequency covering about 70% of the world's GDP.We assess the performance of these indicators based on their ability to capture tail risk for economic activity and to predict banking and currency crises. We find that averaging across the indicators of interest, using judgmental but intuitive weights, produces financial condition indices that are not inferior to, and actually perform better than, those constructed with more sophisticated statistical methods. An indicator that gives more weight to measures of financial stress, which we term WA-FSI, emerges as the best indicator for anticipating banking crisis, and is therefore better suited for financial stability.

Suggested Citation

  • Arrigoni, Simone & Bobasu, Alina & Venditti, Fabrizio, 2021. "The simpler, the better: Measuring financial conditions for monetary policy and financial stability," EIB Working Papers 2021/10, European Investment Bank (EIB).
  • Handle: RePEc:zbw:eibwps:202110
    DOI: 10.2867/149278
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    2. Lodge, David & Manu, Ana-Simona, 2022. "EME financial conditions: Which global shocks matter?," Journal of International Money and Finance, Elsevier, vol. 120(C).
    3. Fernando Eguren-Martin & Andrej Sokol, 2022. "Attention to the Tail(s): Global Financial Conditions and Exchange Rate Risks," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(3), pages 487-519, September.
    4. Ferriani, Fabrizio & Gazzani, Andrea, 2022. "Financial condition indices for emerging market economies: Can Google help?," Economics Letters, Elsevier, vol. 216(C).
    5. Frederic Boissay & Fabrice Collard & Cristina Manea & Adam Shapiro, 2023. "Monetary tightening, inflation drivers and financial stress," BIS Working Papers 1155, Bank for International Settlements.
    6. Yang, Jin Young & Suh, Hyunduk, 2023. "Heterogeneous effects of macroprudential policies on firm leverage and value," International Review of Financial Analysis, Elsevier, vol. 86(C).
    7. Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2021. ""Vulnerable Funding in the Global Economy"," IREA Working Papers 202106, University of Barcelona, Research Institute of Applied Economics, revised Mar 2021.
    8. 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.
    9. Al-Haschimi, Alexander & Apostolou, Apostolos & Azqueta-Gavaldon, Andres & Ricci, Martino, 2023. "Using machine learning to measure financial risk in China," Working Paper Series 2767, European Central Bank.

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    More about this item

    Keywords

    financial conditions; quantile regressions; banking crises; SVARs; spillovers;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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