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Estimating a time-varying financial conditions index for South Africa?

Author

Listed:
  • Alain Kabundi
  • Asi Mbelu

Abstract

This paper uses 39 monthly time series of the financial market observed from January 2000 to April 2017 to estimate a financial conditions index (FCI) for South Africa. The empirical technique used is a dynamic factor model with time-varying factor loadings proposed by Koop and Korobilis (2014) based on the principal component analysis and the Kalman smoother. In addition, we estimate a time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model, which includes, in addition to the FCI, two observed macroeconomic variables. The results show the ability of the estimated FCI to predict risks in the financial market emanating from both the domestic market and the global market. Furthermore, the TVP-FAVAR model outperforms the constant-loadings factor-augmented vector autoregressive (FAVAR) model and the traditional vector autoregressive (VAR) model in the out-of-sample forecasting of the inflation rate and the real gross domestic product (GDP) growth rate. Finally, tighter financial conditions contract the real economy and are deflationary at the same time. Importantly, the responses of macroeconomic variables vary over time.
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Suggested Citation

  • Alain Kabundi & Asi Mbelu, 2017. "Estimating a time-varying financial conditions index for South Africa?," ERSA Working Paper Series 720, Economic Research Southern Africa.
    • Handle: RePEc:rza:ersawp:720
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    Cited by:

    1. is not listed on IDEAS
    2. Theshne Kisten, 2020. "A Financial Stress Index for South Africa: A Time-Varying Correlation Approach," Working Papers 202011, University of Pretoria, Department of Economics.
    3. Kehinde Damilola Ilesanmi & Devi Datt Tewari, 2020. "Financial Stress Index and Economic Activity in South Africa: New Evidence," Economies, MDPI, vol. 8(4), pages 1-19, December.
    4. Ferriani, Fabrizio & Gazzani, Andrea, 2022. "Financial condition indices for emerging market economies: Can Google help?," Economics Letters, Elsevier, vol. 216(C).
    5. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "On the efficient synthesis of short financial time series: A Dynamic Factor Model approach," Finance Research Letters, Elsevier, vol. 53(C).
    6. 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.
    7. Lin Zhu & Jian He, 2024. "China financial stability and asymmetric implications for economic stability," Economic Change and Restructuring, Springer, vol. 57(1), pages 1-29, February.

    More about this item

    Keywords

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    JEL classification:

    • B26 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Financial Economics
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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