Investigating asymmetric determinants of the CNY–CNH exchange rate spreads: The role of economic policy uncertainty

Journal Pre-proof Investigating asymmetric determinants of the CNY-CNH exchange rate spreads: The role of economic policy uncertainty Xiao-Lin Li, Xin Li, Deng-Kui Si

PII: DOI: Reference:

S0165-1765(19)30418-5 https://doi.org/10.1016/j.econlet.2019.108827 ECOLET 108827

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Economics Letters

Received date : 25 October 2019 Accepted date : 2 November 2019 Please cite this article as: X.-L. Li, X. Li and D.-K. Si, Investigating asymmetric determinants of the CNY-CNH exchange rate spreads: The role of economic policy uncertainty. Economics Letters (2019), doi: https://doi.org/10.1016/j.econlet.2019.108827. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier B.V.

Highlights (for review)

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Highlights

 This study identifies the asymmetric effects of various possible determinants on the CNY−CNH spreads by utilizing the NARDL model.

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 The role of economic policy uncertainty (EPU) has firstly been uncovered.  We in particular construct a composite index based on the EPU indices for China and the G7 countries.

 The results show substantial asymmetric effects of the concerned determinants on the CNY−CNH spreads.

 The composite EPU significantly affects the CNY−CNH spreads, with positive

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shocks to the composite EPU causing widening spreads.

Title Page

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Investigating asymmetric determinants of the CNY−CNH exchange rate spreads: The role of Economic Policy Uncertainty

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Xiao-Lin Li

Department of Finance

Ocean University of China, Qingdao, Shandong, CHINA E-mail: [email protected] Telephone: +86 18562583085

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Xin Li

Department of Finance

Ocean University of China, Qingdao, Shandong, CHINA

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E-mail: [email protected]

Deng-Kui Si

*

Department of Finance

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Qingdao University, Qingdao, Shandong, CHINA, E-mail: [email protected]

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This research was funded by the National Social Science Foundation of China, Grant number 16CJY069.

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Corresponding author: Deng-Kui Si, Department of Finance, Qingdao University, Qingdao, Shandong, China. Tel: +86 15689982996. E-mail: [email protected].

*Manuscript Click here to view linked References

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Investigating asymmetric determinants of the CNY−CNH exchange rate spreads: The role of Economic Policy Uncertainty Abstract

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This study investigates the asymmetric effects of economic policy uncertainty (EPU) and other possible determinants on the CNY−CNH spreads by utilizing the nonlinear ARDL model. For this purpose, we construct a novel EPU index based on the EPU indices of China and the G7 countries by using principal component analysis. The results show substantial asymmetric effects of the concerned determinants on the spreads. Moreover, the composite EPU significantly affects the spreads, with positive shocks to the composite EPU inducing widening CNY−CNH spreads. Keywords: Exchange rate spreads; Economic policy uncertainty; Onshore-offshore FX markets; Nonlinear ARDL model

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JEL classification: F21; F31; G15

1. Introduction

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The establishment of the offshore RMB foreign exchange (FX) market in Hong Kong (known as the CNH market) has raised continuing concern about the issue “one currency, two markets”. That’s because that the onshore RMB FX market (known as the CNY market) is heavily managed and highly regulated by the People’s Bank of China (PBOC), whereas the CNH market is mostly liberalized, without interventions by monetary authorities. Against this backdrop, an inevitable consequence is the persistent spreads between the CNY and CNH exchange rates, although the two rates represent the same financial products. To explain the CNY-CNH spread dynamics, the existing studies concentrate on five sets of drivers: the central bank intervention (Fatum et al., 2013), the economic conditions (Ding et al., 2014), the capital market liberalization policies (Funke et al., 2015), the global investor sentiment (Craig et al., 2013), and the investor attention (Han et al., 2018). However, in an

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effort to understand the spillover effects of uncertainty shocks, Kido (2016) finds that the U.S. economic policy uncertainty (EPU) shock exhibits a positive effect on Japanese Yen rate. Balcilar et al. (2016) also argue that the relative EPU can predict exchange rate returns. Beckmann and Czudaj (2017) point out that the exchange rate expectations can be affected by EPU movements. Unfortunately, the literature has not paid further attention to the linkages of the EPU with the exchange rate spreads. We are therefore inspired to detect whether the EPU can help us forecast the CNY−CNH spreads. Considering the increased economic connectedness of China with the G7 countries, we pay attention to the cross-national spillover effect of EPU shocks on the spreads, rather 1

Journal Pre-proof than merely considering the effect of the domestic EPU. For this purpose, we construct a composite EPU index using principal component analysis (PCA). Fig. 1 plots the composite EPU and the CNY−CNH spreads between January 2012 and December 2018. Interestingly, we notice that a number of spikes in the EPU coincide with the evolution of the CNY−CNH spreads, which may presage an underlying linkage between them. 0.08

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CNY−CNH spreads (right)

CNH

CNY

0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 Jul-18

-0.1 Oct-18

Jan-18

Apr-18

Oct-15

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Jul-15

Apr-15

Jan-15

Oct-14

Jul-14

Apr-14

Jan-14

Oct-13

Jul-13

Apr-13

Jan-13

Oct-12

Jul-12

Apr-12

Jan-12

-3

Jul-17

-2

Oct-17

-1

Jan-17

0

Apr-17

1

Jul-16

2

Oct-16

3

Apr-16

4

Jan-16

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0.06

6

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Fig. 1. The composite EPU index, CNY (CNH) exchange rates, and CNY−CNH spreads between January 2012 and December 2018.

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This study also contributes to the existing literature by applying the Nonlinear ARDL (NARDL) model developed by Shin et al. (2014) to identify the potentially asymmetric transmission of the concerned determinants to the CNY−CNH spreads, which would shed new light on the literature merely considering the linear effect of the determinants on the exchange rate spreads.

2. Methodology

By decomposing the independent variables into their partial cumulative sum of the positive and the negative changes, we obtain the asymmetric long-run equilibrium as follows:

yt    xt    xt   t ,

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(1)

where  +' and  ' represent the asymmetric long-run parameters, εt indicates a random error term, yt is the dependent variable. xt+ and xt denote partial cumulative sum processes of the positive and the negative changes in independent variables xt . By combining equation (1) with the unrestricted linear ARDL (p, q) specification, we obtain the following general form of the NARDL model: 2

Journal Pre-proof yt   0  ryt 1  q  xt1  q  xt_1   j 1 j j yt  j  p 1

 j 0 ( pj xt j  pj xt j )  et , q

(2)

where  j 0  j and  j 0  j represent the short-run asymmetric dynamics of xt , and the asymmetric long-run coefficients of xt can be calculated as follows: q-1



q-1



   q  r ,    q  r .

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(3)

3. Data

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The CNY–CNH spread (CNY-CNH) is defined as the differential between the CNY and the CNH exchange rates. Both the two rates are expressed in the direct quotation1. The determinants we consider are the composite EPU index constructed by PCA method (EPU)2, the interest rate differential (ID), the exchange rate expectation (NDF), and the FX intervention (INV)3. The EPU indices of China and the G7 are obtained from the economic policy uncertainty website4 and the other datasets are obtained from the Wind database of China. The sample period ranges from January 2012 to December 2018. Each set of data is at the monthly frequency.

4. Empirical results

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The results of the model selection are shown in Table 1.The FPSS statistics suggest stable long-run relationship between the CNY−CNH spreads and their possible determinants in four concerned NARDL models. However, AIC and SC both reach their minimum, and the adjusted R2 achieves the maximum in the NARDL model with both long and short-run asymmetries. Therefore, this specific model is chosen to represent the impacts of the underlying determinants on the CNY−CNH spreads5.

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Table 2 presents the estimation results of the best-suited model. The CNY−CNH spreads show a stronger response to positive changes in the EPU than to negative changes both in the short and long run. However, the spreads are positively affected by the EPU in the long run, but negatively affected by the EPU in the short run. One possible explanation is that under the context of higher EPU, financiers might be more willing to engage in offshore RMB trading owing to excess return compensation for bearing this extra policy risk, which might induce a An increase (decrease) in the two rates indicates depreciation (appreciation) of the RMB against the U.S. dollar. The composite index is calculated as the first principal component of the EPU indices of China and the G7. 3 We employ the difference between 3-month Shibor and Hibor for the interest rate differential, and utilize 3-month non-deliverable forward rate for the RMB against the U.S. Dollar as a proxy for the exchange rate expectations. Moreover, we make use of the logarithmic difference of the foreign exchange reserves as a proxy for the FX intervention. 4 http://www.policyuncertainty.com/us_monthly.html. 5 The results from the ADF and PP tests indicate that all involved variables are I (0) or I (1) processes, meeting the requirements of the NARDL model. Moreover, we utilize a lag order of 6 since the CUSUM test shows that the estimated parameters of the selected NARDL model are stable. 2

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Journal Pre-proof depreciation of the CNH rate, followed by an expected appreciation in the future. All else equal, an increase in EPU would therefore narrow the CNY−CNH spreads in the short run, but widen the spreads in the long run.

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The interest rate differential has a positive impact on the CNY−CNH spread both in the long and short run. One possible explanation for the positive impact is that a higher onshore RMB interest rate relative to its offshore counterpart tends to reflect a relatively high rate of inflation in mainland China, which often creates pressure toward the CNY depreciation and in

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turn, widens the CNY–CNH spread. Moreover, we also observe that the effect of positive shocks to the interest rate differential always defeats the effect of negative shocks. The exchange rate expectation has a negative impact on the CNY−CNH spread in the long run, with the spreads responding sensitively to the appreciation expectations. In the short run, however, the spreads show a stronger response to the depreciation expectation. That’s because that when investors expect the RMB to appreciate, the accelerated capital inflows would cause greater appreciation of the RMB in the CNH market than in the CNY market as the former market is less regulated and more frequently linked with global markets, which finally leads to widening CNY–CNH spreads. The FX intervention has a negative impact on the CNY−CNH spread, with the effect of

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positive shocks to the intervention dominates the effect of negative shocks both in the long and short run. The result suggests that the positive FX intervention helps curb the enlargement of the spreads in the long run. The underlying reason might be that when the PBOC implements prolonged positive FX interventions, it would release at the same time a strong signal that the CNY has pressure to appreciate possibly as a consequence of large-scale capital inflows, which is exactly the fact of China before the 2008 global financial crisis, and in turn would narrow the CNY−CNH spreads.

5. Conclusions

This study uses the NARDL model to examine the effects of various determinants on the

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CNY−CNH spreads. The results show substantial asymmetric impacts of the concerned determinants on the spreads. As expected, the composite EPU constructed by the EPU indices of China and the G7 affects the spread movements, with positive shocks to the composite EPU inducing widening spreads. Our findings suggest that the linear model estimations are not suitable to seize the actual adjustment process of the CNY−CNH spreads. The EPU dynamics can provide useful information to help investors and policymakers forecast the spreads. Finally, considering prevailing uncertainties, the Chinese government should fully prepare to react to the materialization of these risks so as to reduce the excess volatility in the FX markets. 4

Journal Pre-proof References Beckmann, J., Czudaj, R., 2017. Exchange rate expectations and economic policy uncertainty. European Journal of Political Economy, 47, 148-162. Balcilar, M., Gupta, R., Kyei, C., Wohar, M. E., 2016. Does economic policy uncertainty predict exchange rate returns and volatility? Evidence from a nonparametric causality-in-quantiles test. Open Economies Review, 27, 229-250.

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Craig, R.S., Hua, C., Ng, P., Yuen, R., 2013. Development of the Renminbi market in Hong Kong SAR: assessing onshore-offshore market integration. IMF Working Paper 13/268.

Ding, D. K., Tse, Y., Williams, M. R., 2014. The price discovery puzzle in offshore Yuan trading: different contributions for different contracts. Journal of Futures Markets, 34, 103-123. Fatum, R., Pedersen, J., Sørensen, P. N., 2013. The intraday effects of central bank intervention on exchange rate spreads. Journal of International Money and Finance, 33, 103–117. Funke, M., Shu, C., Cheng, X., Eraslan, S., 2015. Assessing the CNH-CNY pricing differential: role of fundamentals, contagion and policy. Journal of International Money and Finance, 59, 245–262.

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Han, L.Y., Xu, Y., Yin, L.B., 2018. Forecasting the CNY-CNH pricing differential: the role of investor attention. Pacific-Basin Finance Journal, 49, 232-247.

Kido, Y., 2016. On the link between the US economic policy uncertainty and exchange rates. Economics Letters, 144, 49-52.

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Shin, Y., Yu B., Greenwood-Nimmo, M., 2014. Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. Festschrift in Honor of Peter Schmidt. Springer,

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New York, 281–314.

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Journal Pre-proof Table 1 Bound Cointegration Test and the NARDL model selection. SR and LR Symmetry FPSS

17.097***

LR Asymmetry and SR Symmetry FPSS 10.476***

LR Symmetry and SR Asymmetry FPSS 35.085*** WSREPU

WLREPU

0.616 WSRID

16.110***

1.088 WSRNDF

WLR

NDF

0.013 0.018

48.823***

WSREPU

7.013**

WLREPU

4.173**

WSRID

37.551***

WLRID

54.784***

5.518**

WSRNDF

0.668

NDF

2.845*

WLR

WSRINV WLRINV

FPSS

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WLRID

18.931***

LR and SR Asymmetry

28.727***

WSRINV

60.868***

WLRINV

83.394***

Adj. R2

0.696

Adj. R2

0.662

Adj. R2

0.861

Adj. R2

0.939

AIC

-5.727

AIC

-5.575

AIC

-6.352

AIC

-7.227

SC 2 χSC

-5.334

SC 2 χSC

-5.088

SC 2 χSC

-5.378

SC 2 χSC

-5.675

0.579 [0.6788]

0.842 [0.5042]

0.277 [0.8914]

1.686 [0.1892]

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Note: FPSS is F-statistic for testing the null hypothesis of no cointegration. The 10%, 5% and 1% upper bound critical values are 3.52, 4.01 and 5.06 when k = 4, respectively. WLR is the Wald statistic for long-run symmetry under the null of = , q 1 q -1   2      χ j j j 0 j 0 and WSR is tested for short-run symmetry under the null of . SC represents the LM tests for serial correlation under order 6. The superscripts *, **and *** indicate significance at 10%, 5% and 1% level, respectively. The numbers in brackets are the associated p-values.

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Journal Pre-proof Table 2 Estimation result of the NARDL models for the determinants of CNY−CNH spread. Long-run coefficient

Short-run coefficient

0.070***

∆CNY−CNH t-2

-0.177***

∆EPU t-6–

-0.006***

∆NDF –

0.135***

CNY−CNHt-1

-1.448***

∆CNY−CNH t-3

-0.332***

∆ID +

0.014**

∆NDF t-3–

-0.091**

∆EPU t-1+

-0.036***

∆ID t-1+

0.089***

∆NDF t-6–

-0.097**

∆ID t-2+

0.059***

∆INV t-1+

-0.028***

LEPU+

0.033***

∆EPU t-2+

-0.027***

LEPU –

0.030***

∆EPU t-3+

-0.019***

LID+

0.062***

∆EPU t-4+

-0.012***

LID –

0.006**

∆EPU t-5+

-0.013***

LNDF+

-0.018*

∆EPU t-6+

-0.007***

LNDF –

-0.041***

∆EPU –

0.015***

LINV+

-0.026***

∆EPU t-1–

-0.025***

LINV –

0.002

∆EPU t-2–

-0.024***

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Constant

∆EPU t-3–

0.020**

∆INV t-2+

-0.029***

∆ID t-4+

0.046***

∆INV t-3+

-0.033***

∆ID t-5+

0.029***

∆INVt-4+

-0.022***

∆ID t-6+

0.025***

∆INV t-5+

-0.024***

∆ID t-2–

0.021***

∆INV t-6+

-0.021***

∆ID t-4–

0.042***

∆INV –

0.009***

∆ID t-6–

0.015*

∆INV t-1–

-0.022***

-0.026***

∆NDF +

-0.156***

∆INV t-5–

-0.011***

∆EPU t-4–

-0.016***

∆NDF t-3+

0.072**

∆INV t-6–

-0.018***

∆EPU t-5–

-0.006***

∆NDF t-5+

-0.059**

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∆ID t-3+

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Note: The superscripts “+” and “–” represent positive and negative components of the corresponding variables. The superscripts ***, ** and * indicate significance at 1%, 5% and 10% level, respectively.

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