The impact of pre-announced day-to-day interventions on the Colombian exchange rate

Abstract

There is a general lack of consensus within the literature on the question of how to intervene in the foreign exchange market. Are secret (dirty) interventions more powerful than pre-announced interventions? This paper compares the effects of pre-announced day-to-day intervention with respect to discretionary intervention, by combining a Tobit-GARCH policy function with an asymmetric power PGARCH impact function. Using Colombia as a case study, we show that the impact of pre-announced daily interventions, adopted in 2008, is larger than the impact of dirty interventions adopted during 2004–2007. In terms of the former, we find that the impact of a change in daily interventions (from US$ 20 million to US$ 40 million) raises the exchange rate by approximately COP $2, implying that actual interventions of US$ 1000 million depreciate domestic currency by 5.5%. Additionally, we find that capital controls had a positive effect on the exchange rate.

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Notes

  1. 1.

    Hausmann et al. (2004) find that sustained and rapid growth accelerations are related to real exchange rate depreciations. Also, Rodrik (2008) shows that higher growth in emerging economies occurs, on average, 10 years after strong depreciations.

  2. 2.

    For Frankel (2012), the corner hypothesis did not have a good theoretical foundation, arguing that a target zone is entirely compatible with the uncovered interest parity condition.

  3. 3.

    Fratzscher (2012, pp. 722–723 and 737).

  4. 4.

    A notable difference between our investigation and Dominguez et al. (2013) is that we study the effects of intervention explicitly aimed at influencing the exchange rate, while Dominguez et al. (2013) study the effects of changes in international reserves, without an explicit intention to alter the value of the exchange rate, i.e., “non-interventions.”

  5. 5.

    Daily average transactions in the market corresponded to US$ 320 millions in 2001–2004. The stock of international reserves was close to US$ 10,611 in 2001–2004.

  6. 6.

    For an in-depth description of the evolution of Colombian interventions, see the following official reports: Junta Directiva al Congreso de la República (2007, pp. 68–85) and Junta Directiva al Congreso de la República (2011, pp. 111–114).

  7. 7.

    In 2008, Chile purchased US$ 50 million in a market with daily transactions of US$ 2,036 million, whereas Israel purchased US$ 25 million in a market with daily transactions of US$ 3,543 million.

  8. 8.

    We refer readers to Kuersteiner et al. (2016) for a study on the effects of rule-based foreign exchange intervention.

  9. 9.

    The first measure, \(\mathrm{tax}^1\), uses the methodology proposed by Tovar et al. (2003) and complemented by Rincón (2000). The second measure, \(\mathrm{tax}^2\), uses the methodology proposed by Cardenas and Barrera (1997).

  10. 10.

    The first (debt) imposed by the Board of the central bank, and the second (portfolio), some weeks later, by the government. The Board also imposed a limit of 500% for the relation between purchases and sales of foreign exchange derivatives (mainly forwards) and capital. See Junta Directiva al Congreso de la República (2007, p. 75); (2008), March, 2008, pp. 40–43 and (2011, p. 113).

  11. 11.

    The other measure of capital controls, \(\mathrm{tax}^{2}_{t}\), was also considered yielding similar results. The positive association between the exchange rate in Colombia and Brazil is motivated in Loaiza et al. (2012). See “Data Appendix” for a detailed description of each variable.

  12. 12.

    Given the lack of a discretionary component within pre-announced interventions, we include the lagged level of purchases, \(I^{\mathrm{pre{\text {-}}ann}}_{t-1}\), as described in Eq. 1. This is usual in the econometric literature as presented in Barro (2001). Formally, we note that: (i) the Spearman correlation of pre-announced interventions and its 1-day lag is 0.53, suggesting a high enough correlation with the endogenous variable, and (ii) the regression model that we examine is exactly identified, and hence, lagged intervention is uncorrelated with future error disturbances.

  13. 13.

    This variable is used by Kamil (2007) and by Echavarría et al. (2010). Other depreciation maturities were considered with similar results.

  14. 14.

    As a proxy for the long-run equilibrium exchange rate (\({\bar{s}}_{t}\)) we consider the mean of seven in-house “structural” models estimated at the Colombian central bank. Specifically, two models are based on the purchasing power parity, one model uses a Hodrick–Prescott filter, two models are based on structural VEC methodologies and one model is based on the current account. This equilibrium exchange rate is presented monthly to the Board of Directors to feed into the discussion on a potential exchange rate misalignment.

  15. 15.

    We also considered using inflation expectations yielding similar results.

  16. 16.

    Results are available on request.

  17. 17.

    This last result contrasts with the surveys reported by Murcia and Rojas (2012), Graph 14, according to which the impact of discretionary interventions should be higher than the impact of other types of intervention, like \(I^{\mathrm{pre{\text {-}}ann}}_{t-1}\).

  18. 18.

    A similar positive impact of capital controls on the exchange rate is obtained by Edwards and Rigobon (2005) for the case of Chile, and by central bankers interviewed by Mihaljek (2005).

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Correspondence to Mauricio Villamizar-Villegas.

Data appendix

Data appendix

  1. 1.

    Discretionary Intervention \((I^{\mathrm{disc}})\): daily data from the Central Bank of Colombia consisting of purchases of foreign currency traded in the spot market during 2004–2007.

  2. 2.

    Pre-Announced Intervention \((I^{\mathrm{pre{\text {-}}ann}})\): daily data from the Central Bank of Colombia consisting of purchases of foreign currency auctioned daily and with overall constant amounts.

  3. 3.

    Interest Rate Differentials \((i-i^{*})\): daily data consisting of intraday interest rate differentials between Colombia (Repo rate) and the USA (Federal Funds rate).

  4. 4.

    Credit Default Swaps \((\rho _{\mathrm{CDS}})\): daily data consisting of five-year credit default swaps for the Colombian economy.

  5. 5.

    Nominal Exchange Rate (s): daily data from the Central Bank of Colombia consisting of COP-USD daily weighted averages of all transactions conducted in the Colombian stock market.

  6. 6.

    Long-run Equilibrium Exchange Rate \(({\bar{s}})\): daily data from the Central Bank of Colombia consisting of the average of seven in-house models. Specifically, two models are based on the purchasing power parity, one model uses a Hodrick–Prescott filter, two models are based on structural VEC methodologies and one model is based on the current account.

  7. 7.

    Credit/Debt Position \((D^{\mathrm{net}})\): daily data from the Central Bank of Colombia consisting of a dummy variable equal to unity when the central bank was a net debtor with respect to the financial sector.

  8. 8.

    Inflation minus target \((\pi -\pi ^{*})\): monthly data from the Central Bank of Colombia consisting of the difference between monthly inflation and its yearly target.

  9. 9.

    Capital Controls \((\mathrm{tax})\): daily data from the Central Bank of Colombia consisting of an index measuring the equivalent percentage tax imposed on inflows. Controls were implemented between May 7, 2007, and October 8, 2008.

  10. 10.

    Real Exchange Rate (q): daily data from the Central Bank of Colombia consisting of the COP-USD nominal exchange rate divided by the relative Consumer Price Index of both countries.

  11. 11.

    Brazilian exchange rate \((s^{brazil})\): Daily data from Bloomberg consisting of the BRL/USD exchange rate.

For additional information, visit the website of the Central Bank of Colombia: http://www.banrep.gov.co/es/-estadisticas.

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Echavarría, J.J., Melo-Velandia, L.F. & Villamizar-Villegas, M. The impact of pre-announced day-to-day interventions on the Colombian exchange rate. Empir Econ 55, 1319–1336 (2018). https://doi.org/10.1007/s00181-017-1299-1

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Keywords

  • Central bank intervention
  • Reaction function
  • Tobit-GARCH
  • Foreign exchange intervention
  • Capital controls

JEL Classification

  • E52
  • E58
  • F31