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Empirical Economics

Erschienen in:

25.07.2020

Financial distress and real economic activity in Lithuania: a Granger causality test based on mixed-frequency VAR

verfasst von: Andrea Cipollini, Ieva Mikaliunaite

Erschienen in: Empirical Economics | Ausgabe 2/2021

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Abstract

In this paper, we extend the monthly financial stress index for Lithuania, computed by the European Central Bank, to a daily frequency and we also include banking sector stress among its constituents, beyond bond, equity and foreign exchange markets. We investigate the causal relationship between the daily financial stress index and monthly industrial production growth, using a Granger causality test applied to a mixed-frequency VAR. Our results suggest evidence of Granger causality from financial stress to industrial production growth once the index is enriched by daily observations from the financial markets. Our findings, based on impulse response analysis, confirm the negative effect of financial stress on real economy found in the empirical literature through common frequency analysis. Finally, the comparison between common and mixed frequency analysis suggests that ignoring the information content of daily data would lead to a mild temporal aggregation bias that could affect the evaluation of financial stress shocks on industrial production.

Vorheriger Artikel Bank stability and economic growth: trade-offs or opportunities?

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Financial stress indexes were introduced for the US (Hakkio and Keeton 2009; Kliesen et al. 2010; Brave and Butters 2011; Oet et al. 2011), Canada (Illing and Liu 2006), major advanced and emerging counties (Cardarelli et al. 2011; Balakrishnan et al. 2011, respectively) and the euro area as a whole (Hollo et al. 2012), among others.

Also, a paper from the Bank of England by Chatterjee et al. (2017) introduces a FSI for the United Kingdom by extending the CLIFS index by Duprey et al. (2017). The authors incorporate three additional sub-indexes that represent stress in corporate bond, money and housing markets.

While we emphasize the dependence of the Lithuanian financial system on foreign banks, a recent study by Rubio and Comunale (2018) emphasizes the vulnerability of the Lithuanian housing markets to euro area common shocks, given that Lithuania has variable-rate mortgages and a higher LTV cap than its European partners.

Since 2017, the Baltic operations of DnB and Nordea banks were merged into a new bank—Luminor.

Source: Statistics Lithuania: https://www.stat.gov.lt/en.

By using a variance-equal weighting method, each component is computed as a deviation from its mean and weighted by the inverse of its variance (Balakrishnan et al. 2011).

Chen et al. (2014) examine the link between Kansas City FSI and oil prices.

CLIFS indexes are available at: https://sdw.ecb.europa.eu/browse.do?node=9693347.

As argued by Duprey et al. (2017), other sectors are not considered because the availability of data capturing stress in 27 countries is limited both in the time and cross-sectional dimension.

The daily data on Lithuania bond yields are obtained as the difference of the daily spread with German 10-year government bond yield available from Ycharts:

https://ycharts.com/indicators/lithuaniagermany_10_year_bond_spread, and the daily 10-year German government bond yields available from Bundesbank database. Then, we use the monthly CPI for Lithuania and for Germany, available from the OECD to convert the nominal yields into real term.

Note that CPI is available only on monthly frequency, therefore, we simply interpolate it to a daily frequency.

Estonia and Latvia joined the euro area in 2011 and 2014, respectively.

The bilateral exchange rates for litas and its major trading partners are collected from the Bank of Lithuania; the CPI data are taken from the OECD and the trading weights from BIS.

We follow the methodology by Duprey et al. (2017) for the components’ construction. However, note that the authors do not include the banking sector in the CLIFS for Lithuania.

The OMXS30 is a stock market index for the Stockholm Stock Exchange that consists of the 30 most traded stock classes (including SEB bank and Swedbank stocks). The OBX Index is a stock market index which lists twenty-five of the most liquid companies (including DnB bank) of the Oslo Stock Exchange in Norway. The data on Swedbank and SEB bank stock prices as well as OMXS30 index are from the NASDAQ database. The data on DnB bank stock prices are collected from the DnB database and the OBX index—from the Oslo Bors database. CPI is available only on a monthly frequency, therefore, we simply interpolate it to a daily frequency.

If the same value of x occurs more than once, the rank number assigned to each of the observations is given as the average of rankings involved.

Hollo et al. (2012) estimate the weights of the sub-indexes in the CISS index by using a bivariate linear VAR and, then, by computing the cumulated impulse response of industrial production growth to a one standard deviation shock to a sub-sector index. However, the authors find that the differences between the CISS computed with impulse response-based weights and the one with equal weights are not large.

For instance, when financial markets suffer from high distress, increased uncertainty about asset value decreases the value of collateral. As a consequence, shocks affecting the creditworthiness lead to increased swings in output. At the same time, economic activity is affected by the fact that bank capital is eroded, which forces banks to deleverage and decrease lending to businesses.

Montecarlo simulations in Götz et al. (2016) show that bootstrap variants of high-to-low and low-to-high causality tests improve the empirical size.

The monthly data for industrial production (seasonally and working day adjusted) are obtained from Statistics Lithuania. Data on the consumer price index and short-term interest rates are from the OECD database. The short-term interest rate is not a component of the financial stress index and it is not included in the CLIFS index of the European Central Bank.

In line with Götz et al. (2016), we prefer as a (balanced) strategy to drop the last \(m-18\) observations (some of them do exhibit large values) rather than taking the maximum number of days in a particular month (e.g., 23) and creating additional values for non-existing days in other months whenever necessary. Moreover, thanks to the anonymous referee suggestion, we show that our results are robust to the use of 21 daily observations (\(m=21\)) within a month. The results are available upon request.

While there is evidence of stationary for the financial stress index and the industrial production growth (using Phillips and Perron (1988) test), the short-term interest rate and inflation rate are stationary when allowing for a structural break (using the Zivot and Andrews (2002) testing methodology). In particular, January 2009 and October 2009 are the structural break dates for the short term interest and inflation rate, respectively.

Following Ghysels et al. (2018), p-values are computed based on the robust covariance matrix with 100,000 draws from an approximation to the limit distribution under non-causality.

We estimate 5 parameters in each \(i^{th}\) parsimonious model Eq. (23): (i) constant\(\left( \mu _{i}\right) \), (ii) three coefficients related to the lagged low-frequency variables \(\left( a_{1i},a_{2i},a_{3i}\right) \) and (iii) one coefficient related to the lagged FSI \(\left( \beta _{i}\right) \).

We simply average daily observations to weekly frequency.

Hubrich and Tetlow (2015) use a recursive identifying scheme by estimating a nonlinear VAR fitted to five endogenous variables ordered as follows: consumption, inflation, fed rate, monetary aggregate and FSI.

The results are robust to the ordering of the variables: IP growth, CPI inflation, financial stress index, short-term interest rate, and are available upon request.

Alessandri P, Mumtaz H (2019) Financial regimes and uncertainty shocks. J Monet Econ 101:31–46CrossRef

Balakrishnan R, Danninger S, Elekdag S, Tytell I (2011) The transmission of financial stress from advanced to emerging economies. Emerg Markets Finance Trade 47(sup2):40–68CrossRef

Bloom N (2009) The impact of uncertainty shocks. Econometrica 77(3):623–685CrossRef

Brave SA, Butters R (2011) Monitoring financial stability: a financial conditions index approach. Econ Perspect 35(1):22

Cardarelli R, Elekdag S, Lall S (2011) Financial stress and economic contractions. J Financial Stab 7(2):78–97CrossRef

Chatterjee S, Chiu J, Hacioglu-Hoke S, Duprey T (2017) A financial stress index for the United Kingdom. Bank of England working papers 697, Bank of England

Chau F, Deesomsak R (2014) Does linkage fuel the fire? The transmission of financial stress across the markets. Int Rev Financial Anal 36:57–70CrossRef

Chen W, Hamori S, Kinkyo T (2014) Macroeconomic impacts of oil prices and underlying financial shocks. J Int Financial Markets Inst Money 29:1–12CrossRef

Duprey T, Klaus B, Peltonen T (2017) Dating systemic financial stress episodes in the EU countries. J Financial Stab 32:30–56CrossRef

Ferrara L, Guérin P (2018) What are the macroeconomic effects of high-frequency uncertainty shocks? J Appl Econ 33(5):662–679CrossRef

Ghysels E, Hill JB, Motegi K (2016) Testing for Granger causality with mixed frequency data. J Econ 192(1):207–230CrossRef

Ghysels E, Hill JB, Motegi K (2018) Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality. Available at SSRN 2616736

Gilchrist S, Sim JW, Zakrajšek E (2014) Uncertainty, financial frictions, and investment dynamics. Technical report, National Bureau of Economic Research

Götz TB, Hecq A, Smeekes S (2016) Testing for Granger causality in large mixed-frequency VARs. J Econ 193(2):418–432CrossRef

Hakkio CS, Keeton WR (2009) Financial stress: what is it, how can it be measured, and why does it matter? Econ Rev 94(2):5–50

Henzel SR, Rengel M (2017) Dimensions of macroeconomic uncertainty: a common factor analysis. Econ Inquiry 55(2):843–877CrossRef

Hollo D, Kremer M, Lo Duca M (2012) CISS—a composite indicator of systemic stress in the financial system. Working Paper Series 1426, European Central Bank

Hubrich K, Tetlow RJ (2015) Financial stress and economic dynamics: the transmission of crises. J Monet Econ 70:100–115CrossRef

Illing M, Liu Y (2006) Measuring financial stress in a developed country: an application to Canada. J Financial Stab 2(3):243–265CrossRef

Johansson T, Bonthron F (2013) Further development of the index for financial stress for Sweden. Sveriges Riksbank Econ Rev 1:1–20

Kliesen KL, Smith DC, et al (2010) Measuring financial market stress. Economic Synopses

Kremer M (2016) Macroeconomic effects of financial stress and the role of monetary policy: a VAR analysis for the euro area. Int Econ Econ Policy 13(1):105–138CrossRef

Kuodis R, Ramanauskas T (2010) From boom to bust: lessons from Lithuania

Louzis DP, Vouldis AT (2013) A financial systemic stress index for Greece

Ludvigson SC, Ma S, Ng S (2015) Uncertainty and business cycles: exogenous impulse or endogenous response?. Technical report, National Bureau of Economic Research

Oet MV, Eiben R, Bianco T, Gramlich D, Ong SJ (2011) Financial stress index: Identification of systemic risk conditions. In: 24th Australasian Finance and Banking Conference

Phillips PC, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75(2):335–346CrossRef

Rubio M, Comunale M (2018) Macroeconomic and financial stability in a monetary union: the case of Lithuania. Econ Syst 42(1):75–90CrossRef

Schmitz M, De Clercq M, Fidora M, Lauro B, Pinheiro C (2013) Revisiting the effective exchange rates of the euro. J Econ Social Meas 38(2):127–158CrossRef

Zivot E, Andrews DWK (2002) Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. J Bus Econ Stat 20(1):25–44CrossRef

Titel: Financial distress and real economic activity in Lithuania: a Granger causality test based on mixed-frequency VAR
verfasst von: Andrea Cipollini
Ieva Mikaliunaite
Publikationsdatum: 25.07.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 2/2021
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-020-01888-2

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