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Exchange rate spillovers in the CIS

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Abstract

This paper estimates macroeconomic connectedness in the CIS (the Commonwealth of Independent States) through risk spillovers via the exchange rates. We collect high frequency daily data on exchange rates from January 2006 to July 2020 and use the Diebold-Yilmaz method of variance decomposition, as well as the Barunik-Krehlik method of frequency variance decomposition, for the analysis. We find that macroeconomic risk in the region increases significantly during macroeconomic shocks and that it has maintained a higher average level since 2015, a difficult year full of regional and global challenges. Our findings also show that currencies managed by more flexible exchange rate regimes on average transmit macroeconomic risk in the region. Frequency variance decomposition demonstrates that while the majority of risk transmission is smaller-scale and short-lived, spillovers from main regional and global crises are bigger and more persistent. Although short-term connectedness dominates the overall variance of the system, more severe macroeconomic shocks resonate greatly on all time horizons, i.e. they impact the system for a longer period of time and more deeply.

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Fig. 1

(Data adapted from Investing.com)

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Data availability

Data is available upon request.

Notes

  1. A notable example of application of this method is the April 2016 IMF Global Financial Stability Report, which uses it to apprehend financial spillovers across advanced and emerging market economies. A big advantage of this methodology is its ability to appraise directional specifics of the spillovers, a property explored by the April 2018 IMF Global Financial Stability Report in their evaluation of term premium spillovers among G4 countries. We turn to this advantage to perceive which countries on average drive the spillovers, or lead the contagion, in the region.

  2. The Group of Seven, or G7, is an informal grouping of the advanced economies: Canada, France, Germany, Italy, Japan, the UK, and the US.

  3. OE is the author-defined informal grouping of low- and middle-income emerging economies: Malaysia, Phillipins, South Africa, Thailand, Turkey.

  4. The standard BIC criterion gave the 1 lag specification.

  5. We are employing VAR(1) specification in our analysis.

  6. Note that we drop (H) going forward for convenience but it is always implied that measures are attributed to a given forecast horizon H.

  7. These values are normalized for easier interpretation as described in 3.2.

  8. BK call these measures within spectral band measures.

  9. Additionally, Kiani (2010) argues that given the SOE (small open economy) status of the CIS countries, interest rates may not be the most viable monetary policy tool available to them whilst exchange rates may be playing a more poignant role in their economies and policy toolkits.

  10. Please see the table in the Appendix for detailed information.

  11. Azerbaijan has mostly pegged against the USD, and Belarus - USD and euro. Other two countries who have leaned on pegging is Kazakhstan (peg to USD and the rouble) and Kyrgyzstan (mostly unofficial peg to USD).

  12. Countries often switch around the arrangements during a year, IMF captures the most accurate information to the date.

  13. Data on these three variables was taken from the Fred website.

  14. It increases largerly by the virtue of having more variables and variance being non-negative.

  15. We measure volatility by 20-day rolling standard deviations.

  16. Values are as of 2016.

  17. Data is taken from the oec.world website.

Abbreviations

BK:

Barunik-Krehlik

CEE:

Central and Eastern Europe

CIS:

Commonwealth of Independent States

DE:

Developed economies

DY:

Diebold-Yilmaz

ECU:

Eurasian Customs Union

EEC:

European economic community

EEU:

Eurasian Economic Union

EU:

European Union

FEVD:

Forecast error variance decomposition

GVD:

General variance decomposition

IMF:

Internaitonal Monetary Fund

LA:

Latin America

RGDP:

Real Gross Domestic Output

USSR:

Union of Soviet Socialist Republics

VAR:

Vector Autoregression

WTI:

West Texas Intermediate

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  1. Economics Department, University of Wisconsin-Milwaukee, Miilwaukee, WI, USA

    Salome Giorgadze

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  1. Salome Giorgadze
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Appendix

Appendix

See Table 9.

Table 9 Country info
Full size table

KAOPEN is a popular index invented by Chinn and Hiro (2006) used to quantify a country’s capital account openness; the larger the value, the more financially open a country is.Footnote 16 As a reference point, the US has a KAOPEN value of 2.33. Income group indicates which of the four income group classifications (low, lower middle, upper middle, high) as per the World Bank Atlast method the countries belonged to in 2016. Top Exports column designates the products our countries exported the most in 2019.Footnote 17

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Giorgadze, S. Exchange rate spillovers in the CIS. Eurasian Econ Rev (2024). https://doi.org/10.1007/s40822-024-00268-w

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