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Financial stability indicator for non-banking markets

Author

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  • Marius Cristian Acatrinei

    (Financial Supervisory Authority, Bucharest, Romania)

Abstract

A mixed frequency indicator is designed to incorporate and extract information from time-series data that are available at different frequencies: daily, monthly, quarterly, etc. Currently, the non-banking financial markets in Romania are supervised by the Financial Supervisory Authority and are composed of three distinct markets: the capital market, insurance, and private pension funds. Due to the mutual exposure between them, facilitated by the financial instruments held in their investment portfolios, there are common risk factors that influence their dynamics. Although a financial shock can affect all three sectors at the same time, the impact can be measured at a different frequency and with a different lag. Surveillance data for capital markets and pension funds are available every month, with a gap of one month, while for insurance the data are available quarterly, but with a gap of two months, similar to GDP data. If a sudden financial event disrupts financial markets or a change in the macroeconomic environment changes the medium-term outlook, what is the impact on non-bank financial intermediation? The stability indicator for non-banking financial markets is a monthly indicator estimated from mixed frequency data. The indicator is designed to provide a signal of financial instability in non-banking financial markets, to the extent that all three markets are disrupted at once.

Suggested Citation

  • Marius Cristian Acatrinei, 2020. "Financial stability indicator for non-banking markets," Journal of Financial Studies, Institute of Financial Studies, vol. 9(5), pages 3-9, November.
    • Handle: RePEc:fst:rfsisf:v:5:y:2020:i:9:p:3-9
    DOI: 10.6084/m9.figshare.13621271
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    References listed on IDEAS

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    More about this item

    Keywords

    financial stability indicator; non-bank financial markets; state-space model;
    All these keywords.

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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