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Uncovering CIP Deviations in Emerging Markets: Distinctions, Determinants, and Disconnect

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Abstract

We provide a systematic empirical analysis of short-term covered interest parity (CIP) deviations for a large set of emerging market (EM) currencies. EM CIP deviations tend to be wider and more volatile compared to most G10 currencies, and may move in an opposite direction compared to G10 currencies during global risk-off episodes. Motivated by theories of financial determinants of exchange rate, we show that while offshore EM CIP deviations are sensitive to changes in FX dealers’ risk-bearing capacities and global risk aversion, onshore CIP deviations are largely unresponsive in segmented FX markets. Meanwhile, the sensitivity of offshore CIP deviations to global factors for currencies with segmented FX markets is stronger compared to their counterparts with integrated FX markets. After accounting for global factors, we find weak evidence of country default risk and FX intervention affecting EM CIP deviations.

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Notes

  1. A non-deliverable FX forward (NDF) is an outright forward FX contract in which counterparties settle the difference between the contracted NDF rate and the prevailing spot rate on an agreed notional amount, generally from OTC markets in international finance centers such as Singapore, Hong Kong, London, Dubai, and New York. Unlike a deliverable forward, no physical delivery of currencies is necessitated at settlement. Only the profit and loss are exchanged. Usually settled in U.S. dollar, NDF has been the dominant hedging instrument for a number of currencies with limited offshore convertibility.

  2. We follow the convention of the literature and define the short-term CIP deviation (also known as the cross-currency “basis”) as the relative difference between direct USD interest rate in the cash market and synthetic USD rate in the swap market from swapping local currency cash flow using FX forward and spot transactions.

  3. Global risk aversion is captured either with the first principal component of safe-haven currencies’ spot exchange rate (Cerutti et al. 2021) or the broad dollar index (Avdjiev et al. 2019).

  4. Other work in this area include Hertrich and Nathan (2023), who study the impact of Bank of Israel intervention on USD-ILS basis, and Zeev and Nathan (2023) investigate the impact of limits to arbitrage and inelastic supply for hedging services on the sensitivity of CIP deviations to hedging demand. An early related paper is Skinner and Mason (2011), who focus on a small subset of the currencies considered in this paper. Our findings highlighting that the forward FX markets are key in explaining the differences across EMs and AEs align with Kalemli-Özcan and Varela’s (2021) analysis of the differences across Uncovered Interest Parity premium in EMs and AEs.

  5. We also refer the readers to an earlier summary by Lipscomb (2005), with input from market participants on the factors affecting the pricing of NDF.

  6. For comparison, the ex-post unhedged amount to repay in local currency with a debt face value equal to 1 USD is given by \(i_{t,t+n}^{{{\$}}} + (s_{t,t+n} - s_{t})\).

  7. Bank for International Settlements (2022) and Jung and Jung (2022) provide systematic documentations of regulations on FX derivatives markets in Asia-Pacific EMEs.

  8. Major provider of CDS data, such as Markit, only report CDS spreads from the tenor of 6-month onwards.

  9. Except for the case of China (see Sect. 4), we make no distinction between offshore and onshore interest rates, instead using domestic interbank rates as the representative rates. Administrative filing suggests that while partial barriers to entry may exist, major international investors, such as PIMCO, actively tap into domestic money markets using interest rate swap agreements, receiving floating money market interest rates, or engage in cross-currency basis trade directly with the basis linked to domestic money market rates.

  10. On Refinitiv, we search short-term credit ratings of major banks headquartered in the EMs we consider with operations in the U.S (based on the latest foreign bank structure data: https://www.federalreserve.gov/releases/iba/202203/default.htm). Most banks in emerging markets are assigned a rating of B, A3/P3 or A2/P2. Examples of the A2/P2 category include Banco de Crédito e Inversiones (Chile), Banco de Crédito del Perú (Peru), and Bangkok Bank (Thailand).

  11. Currency-specific variations in default prospects remain unaccounted for.

  12. For Refinitiv, onshore forward quotes refer to quotes submitted by domestic data providers.

  13. As we use closing quotes and intraday prices are scarce for emerging markets, we do not account for time differences in daily data releases that potentially make the hypothetical CIP trade infeasible should markets be accessible. This is less of a concern, however, given that EMs’ interbank money market interest rates are usually slow-moving.

  14. We discuss the disconnect in more detail in the next two sections. The currencies are BRL, CNY, IDR, INR, MYR, PHP, THB, and TWD. Also see Fig. 9 for time-series charts of CIP deviations for individual currencies.

  15. Some of these limitations on non-residents were relaxed in January 2021 according to the 2022 AREAER.

  16. The requirement not to engage in the NDF market was already present before 2016 but it was not strictly enforced.

  17. The magnitude of ex-ante deviations from uncovered interest rate parity (UIP) (based on exchange rate expectations from survey data) are typically larger than CIP deviations in emerging markets. However, for most currencies, the correlations between CIP and UIP deviations are often very small (Kalemli-Özcan and Varela 2021).

  18. In this set of figures, we drop TWD as an outlier. TWD’s CIP deviations behave in a similar way to G10 currencies. See Fig. 12 for scatterplots where we put TWD back. We also provide a version of the scatterplots in Fig. 13 using countries with integrated offshore and onshore currency markets, to be defined in Sect. 4. The correlations are very similar to those generated from the full sample.

  19. The regression specification is

    $$\begin{aligned} x_{i,t} = \sum _{s=-10}^{s=10}D_{i,t+s}\beta _{s} + \alpha _{i} + \varepsilon _{i,t} \end{aligned}$$

    where \(x_{i,t}\) denotes daily CIP deviations, and \(D_{i,t-s}\) is a dummy variable indicating whether date \(t-s\) is the first day that a 3-month forward contract settles in the next calendar year. These regressions also include currency fixed effects. In Fig. 4, we report 95% confidence intervals based on heteroskedasticity-robust standard errors as the number of clusters to compute clustered standard errors is very small.

  20. Note that we use the same interest rate and spot exchange rate throughout to compute offshore and onshore CIP deviations for each currency.

  21. The increase in overall trading of emerging market currencies is largely driven by a shift in hedging demand from bank and insurance companies to international asset managers (Caballero et al. 2022). For capital inflows to emerging markets after countries’ inclusion in international equity and bond indices, see Raddatz et al. (2017). For international mutual funds’ currency hedging practices, see Sialm and Zhu (2021).

  22. Krogstrup and Tille (2018) attributes the heterogeneous sensitivity of foreign currency capital flows to global risk factors to intermediaries’ ex-ante currency exposure.

  23. Huang et al. (2022) study the role of constrained dealers in supplying liquidity in the FX market by considering a similar dealer leverage measure. The He et al. (2017) primary dealer leverage ratio measure is a powerful predictor of CIP deviations for advanced economy currencies (Augustin et al. 2020; Cerutti et al. 2021).

  24. The survey results can be accessed at https://www.euromoney.com/surveys/foreign-exchange-survey. The use of top-ten dealers in each survey is without loss of generality, as the offshore FX market is significantly concentrated. According to the 2022 Euromoney FX survey, the top 20 FX dealer banks of EM currencies account for nearly 90% of the total market. Appendix Fig. 15 shows that our dealer leverage measure strongly comoves with He et al. (2017) primary dealer leverage ratio.

  25. See the discussion in Cerutti et al. (2021). The inclusion of nominal interest rate differentials in the regressions is also justified by the fact that, if \(x_{t,t+n} \ne 0\), a regression of forward premium (\(f-s\)) onto the interest rate differential yields a coefficient generally not equal to 1. Subtracting from both sides of the equation leads to a mechanical relationship between the basis and the interest rate differentials.

  26. In Table 12, we report results from the same specifications but generated using CIP deviations constructed with 3-month USD Libor rate.

  27. Our safe-haven common factor is only marginally correlated with VIX, with a correlation coefficient of 0.1.

  28. In untabulated robustness exercise, we find that the significant correlation between dealer leverage and CIP deviations is not sensitive to excluding the sample after 2020 marked by the COVID-19 disruption.

  29. The coefficient for the common factor and the residuals does not have a straightforward interpretation, as principal components are invariant to a scaling factor.

  30. For details on the offshore-onshore Renminbi market and important differences of CNH from CNY, see Funke et al. (2015); Cheung et al. (2021).

  31. For robustness, we also produce a table with the same specification, but using CIP deviations constructed with 3-month USD Libor rate. See panel (b) of Table 12.

  32. In untabulated results, we find little association between CIP deviations and rating downgrades to sovereign bond by international rating agencies.

  33. For the pervasive impact of a strong dollar on emerging markets, see Obstfeld and Zhou (2022).

  34. Such interventions are in similar spirit to the dollar swap line operations conducted by central banks in the advanced economies. To see why the cost of intervening in the NDF market would likely be small, note that ex ante, the cost of intervention is the gap between the forward rate and the expected exchange rate at settlement. The central bank incurs ex-post loss if the realized depreciation is such that the prevailing spot exchange rate at settlement is higher than the agreed-upon forward exchange rate, and receives a transfer otherwise. To the extent that the intervention efforts of the central bank may stabilize exchange rate expectations, such operations could be profitable both ex ante and ex post. Sandri (2020) provides evidence on the profitability of Central Bank of Brazil’s FX swap operations.

  35. This point has received attention from policymakers in Asia-Pacific EMs. See Bank for International Settlements (2022).

  36. See Schmittmann and Chua (2020) for more information on Asian countries’ policy approaches to NDF market integration, and Reserve Bank of India (2019, 2020) for arguments in favor of opening up the onshore market.

  37. Also see Domanski et al. (2016) for a general discussion, and Jermann et al. (2022) for China’s intervention in the offshore CNH market.

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Authors and Affiliations

  1. Strategy, Policy, and Review Department, International Monetary Fund, Washington, USA

    Eugenio Cerutti

  2. Department of Economics, Princeton University, Princeton, USA

    Haonan Zhou

Authors
  1. Eugenio Cerutti
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  2. Haonan Zhou
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Correspondence to Haonan Zhou.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We thank Agnes Isnawangsih for help with downloading Bloomberg data, Ken Froot and Hyeyoon Jung for insightful discussion, Gustavo Adler and Jochen Schmittmann for helpful conversations, the Editor, two anonymous referees, and Suman Basu, Lukas Boer, Sonali Das, Jorge Leon, Monica Petrescu, Carlos de Barros Serrao, Tatjana Schulze, Yizhi Xu, and conference participants at the 23rd Jacques Polak Annual Research Conference and 2nd Annual International Roles of the U.S. Dollar Conference for comments. Zhou acknowledges generous financial support from the International Economics Section and the Griswold Center for Economic Policy Studies at Princeton University. The views expressed in this paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Appendices

Appendix

Appendix 1: Data Appendix: Constructing EM CIP Deviations

In this data appendix, we provide a step-by-step guide to compute short-term CIP deviations.

  1. 1.

    Download all necessary data from Bloomberg/Refinitiv. Table 5 contains the tickers for the forward, spot exchange rates and interest rates used in the paper.

    1. (a)

      One also needs to download contract settlement dates from Bloomberg to take into account potential differences in the actual maturity of the contract. For instance, a three-month contract may settle in 29 days instead of 30 days. This can be done by applying the BDP function in the Excel API to the tickers corresponding to the forward contract, with field DAYS_TO_MTY. One would need to override the default date setting by supplying the date when the contract is priced. In our calculations, we assume that onshore and offshore contract priced at the same date has the same days until maturity, and use the offshore contract as the benchmark.

    2. (b)

      The forward exchange rates downloaded from Bloomberg/Refinitiv are, in fact, forward points to be added onto the spot exchange rates to arrive at the outright forward rates. For advanced economy currencies (except Japanese Yen), forward points are usually quoted with a scale factor of 10,000, so that the outright forward exchange rate is given by \(F_{t} = S_{t} + FP_{t}/10000\), where \(FP_{t}\) is forward point. Across emerging market currencies, however, market conventions differ. One needs to make sure to apply the correct scaling factor to each currency to arrive at the correct forward exchange rates. Similarly, interest rate day count convention also differs across currencies.

  2. 2.

    With the data in hand, we are in a position to compute CIP deviations. Without loss of generality, we focus on offshore forward rate-based 3-month basis for a particular currency i. We break the task down in several steps:

    1. (a)

      Compute outright forward rate using forward points: \(F_{t,t+3m} = S_{t} + \frac{FP_{t,t+3m}}{\text {scaling factor}}\).

    2. (b)

      Given an interest rate series \(y_{t,t+3m}\) (or the dollar equivalent) downloaded from Bloomberg/Refinitiv and expressed in percentage points, continuously compound interest rates.

      $$\begin{aligned} i_{t,t+3m} = 100\times \frac{360}{\text {days}}\times \log (1+\frac{y_{t,t+3m}}{100}\times \frac{\text {days}}{360}) \end{aligned}$$

      where \(\text {days}\) is the days to maturity for a forward contract. 360 may be replaced with 365/252 depending on the day count convention associated with a particular interest rate.

    3. (c)

      Similarly, back out forward premium \(\rho _{t,t+3m}\), expressed in percentage points:

      $$\begin{aligned} \rho _{t,t+3m} = 100*\frac{360}{\text {days}}\times \log \Big ( \frac{F_{t,t+3m}}{S_{t}} \Big ) \end{aligned}$$
    4. (d)

      Combine. Compute CIP deviations (in basis points) from

      $$\begin{aligned} x_{t,t+3m} = 100*(i_{t,t+3m}^{{{\$}}}-(i_{t,t+3m}-\rho _{t,t+3m})). \end{aligned}$$
Table 5 Data sources and tickers—3-month CIP deviations
Full size table
Table 6 Data sources and tickers—FX dealer leverage
Full size table

Appendix 2: Additional Figures and Tables

See Figs. 8, 9, 10, 11, 12, 13, 14, 15 and Tables 7, 8, 9, 10, 11, 12, 13 and 14 .

Fig. 8
figure 8

Evolution of CIP deviations: G-10 currencies. Note: Gray vertical lines correspond to Jan 2009 and Mar 2020. 10-day moving averages expressed in basis points, 2004–2021. Sources: Bloomberg, Refinitiv, authors’ calculation

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Fig. 9
figure 9

Currencies with segmented FX forward markets: onshore/offshore 3-month CIP deviations (2004–2021). Note: Daily onshore (red) and offshore (blue) 3-month CIP deviations for BRL, CNY, IDR, INR, MYR, PHP, THB and TWD. Gray horizontal lines refer to levels of zero

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Fig. 10
figure 10

Global factor-\(\beta\) (2010–2021) for 3-month offshore CIP deviations of other EM currencies. Note: Time-series \(\beta\) of monthly change of offshore/NDF 3-month CIP deviations on monthly changes in log FX dealer leverage ratio (Panel a) or safe-haven currency common factor (Panel b, constructed following Cerutti et al. (2021)), in a regression that also controls for interest rate differential and forward bid-ask spread. Error bands correspond to 90% confidence interval with Newey-West standard errors with 12 lags. The CIP deviations are winsorized at 1% and 99%

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Fig. 11
figure 11

Broad dollar-\(\beta\) (2010–2021) for 3-month offshore CIP deviations: Currencies with segmented markets. Note: Time-series \(\beta\) of monthly change of onshore (red) and offshore/NDF (blue) 3-month CIP deviations on monthly change in log of broad dollar index (see Avdjiev et al. (2019)), in a regression that also controls for interest rate differential and forward bid-ask spread. Error bands correspond to 90% confidence interval with Newey-West standard errors with 12 lags. Error bands correspond to 90% confidence interval with Newey-West standard errors with 12 lags. The CIP deviations are winsorized at 1% and 99%

Full size image
Fig. 12
figure 12

CIP deviations and macro correlates across countries (2010–2021), with TWD. Note: figure plots time-series averages of benchmark 3-month CIP deviations against key macro-financial aggregates of emerging markets and advanced economies. The benchmark 3-month CIP deviations are offshore quotes or quotes on non-deliverable forwards. Interest rate spread for emerging market currencies is calculated by taking the difference between 3-month money market rate and 3-month US A2/P2 commercial paper rate. For advanced economies, dollar interest rate is Libor rate. Net international investment position (IIP) are annual observations from the Milesi-Ferretti and Lane (2017) dataset, updated to 2021 (link: https://www.brookings.edu/research/the-external-wealth-of-nations-database/). We subtract reserves from the aggregate international investment position for each country. Sample period: 2010–2021. The daily deviations from CIP are winsorized at 1% and 99% before being aggregated for the graphs

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Fig. 13
figure 13

CIP deviations and macro correlates across countries (2010–2021), currencies with integrated markets. Note: Fig. 13 repeats the analysis of Fig. 2 to plot the cross-sectional relationship between interest rate differential, net international investment position, and CIP deviations. but restricts the sample to currencies with integrated offshore and onshore FX markets (i.e., dropping the following currencies: BRL, CNY, IDR, INR, MYR, PHP, THB, TWD). The benchmark 3-month CIP deviations are offshore quotes or quotes on non-deliverable forwards. Interest rate spread for emerging market currencies is calculated by taking the difference between 3-month money market rate and 3-month US A2/P2 commercial paper rate. Net international investment position (IIP) are annual observations from the Milesi-Ferretti and Lane (2017) dataset, updated to 2021 (link: https://www.brookings.edu/research/the-external-wealth-of-nations-database/). We subtract reserves from the aggregate international investment position for each country. Sample period: 2010–2021. The daily deviations from CIP are winsorized at 1% and 99% before being aggregated for the graphs

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Fig. 14
figure 14

Czech Kruna: 3-month CIP deviations and forward premia during period of exchange rate floor (2013–2017). Note: Fig. 14 plots the 3-month CIP deviations and forward premia for CZK. The exchange rate floor against EUR lasted from 11/07/2013 to 04/06/2017. The forward premium is computed as the difference between log 3-month forward exchange rate and log spot exchange rate, both in units of Kruna per USD

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Fig. 15
figure 15

Evolution of key global factors. Note: a plots the monthly evolution of intermediary leverage ratio used in the regressions in Section 4. He–Kelly–Manela primary dealer leverage refers to the He et al. (2017) leverage ratio computed from a set of designated treasury market primary dealers. FX dealer leverage refers to the measure constructed in this paper from a set of FX dealer banks of EM currencies with the largest market share according to Euromoney annual FX survey. b plots the safe haven currency common factor used in Cerutti et al. (2021) and the safe haven residuals for USD. The safe haven currency common factor is the first principal component of nominal effective exchange rate for USD, CHF, JPY. The residuals are obtained by regressing the USD nominal effective exchange rate on the common factor

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Table 7 Average CIP deviations by G-10 currency (bps)
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Table 8 Regressions: Summary statistics
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Table 9 EM CIP deviations and broad dollar index: Baseline panel regressions
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Table 10 G10 currency CIP deviations (IBOR) and intermediary leverage factor
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Table 11 EM CIP deviations and global factors: 1-month tenor
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Table 12 CIP deviations, global and country-specific factors: IBOR basis
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Table 13 EM CIP deviations (IBOR) and global factors: European currencies
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Table 14 EM forward premium and global factors
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Cerutti, E., Zhou, H. Uncovering CIP Deviations in Emerging Markets: Distinctions, Determinants, and Disconnect. IMF Econ Rev 72, 196–252 (2024). https://doi.org/10.1057/s41308-023-00222-x

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