Central Bank of Oman Occasional Paper: 2019-1

Liquidity Management Framework and Interbank Overnight Interest Rate in Oman: An Empirical Investigation

Amal Al Raisi, Sunil Kumar and Razan Al Humaidi*

Abstract Oman’s overnight inter-bank rate generally follows the trends in federal fund rate due to currency peg with the US dollar and open capital account, albeit with some intermittent deviations. In our ARDL model, we find federal fund rate along with domestic liquidity conditions as drivers of the overnight inter-bank rate in Oman. Accordingly, intermittent deviations of Oman’s overnight inter-bank rate from the federal fund rate appears to have been caused by evolving domestic liquidity conditions and less than perfect arbitrage due to various prudential limits.

Keywords: Overnight inter-bank rate, federal fund rate, monetary policy, liquidity conditions

JEL Classification: E43, E52, E58

*Amal Al Raisi is a Manager, Sunil Kumar is an Economist, and Razan Al Humaidi is an Associate Research Analyst in Economic Research & Statistics Department of the Central Bank of Oman. The authors would like to express their sincere thanks to Dr. Mazin Al Ryami for his valuable comments/inputs on the paper. The views expressed in this paper and errors, if any, are strictly of the authors and do not belong the organization they work for. All the usual disclaimers apply.

CONFIDENTIAL

Central Bank of Oman Occasional Paper: 2019-1

Introduction

The Central Banks across a large number of countries have shifted from monetary targeting to interest rate targeting as the main instrument of monetary policy. This shift was mainly necessitated by the weakening of relationship between the monetary aggregates and final objective(s) i.e. inflation and growth. Moreover, it was empirically observed that demand for money function had turned unstable in most of the jurisdictions. Under the interest rate targeting, the monetary policy framework comprises three main pillars, viz. policy rate, operating target, and final objective(s). The second pillar (operating target) is the starting point of the monetary policy transmission and therefore, its achievement becomes paramount for effective implementation of the monetary policy for any Central Bank. In most cases, the operating target is the overnight inter-bank rate, which is being achieved by implementing efficient liquidity management framework. Any misalignment of the operating target with the policy rate undermines the monetary policy transmission and hence, such an outcome is considered suboptimal and undesirable. In fact, sustained misalignment of the operating target from the policy rate could jeopardize the credibility of the monetary policy (Central Bank), thereby, warranting a review/ revision in the monetary and liquidity management frameworks.

Oman has a fixed exchange rate, i.e. Rial Omani is pegged to USD and largely open capital account. Therefore, the monetary policy in Oman should not have much of independence theoretically (impossible trinity or macroeconomic trilemma problem), and the US monetary policy should get transmitted automatically that means interest rates in Oman should be at par with prevailing interest rates in the USA, assuming free arbitrage between assets of both countries. It has, however, been observed that although interest rates in Oman follow the trends prevailing in the interest rates of USA, they tend to vary somewhat from the latter at times. In this regard, Espinoza and Prasad (2012) also find that domestic interest rates in Oman 2

Central Bank of Oman Occasional Paper: 2019-1 are somewhat delinked from US FED policy rate. Some variance between two interest rates is expected on account of risk premia and transaction costs. Furthermore, the evolving domestic liquidity conditions and liquidity management operations coupled with some restrictions on free arbitrage due to prudential limits also provide scope for deviation in Oman’s interest rates from the USA interest rates. So the moot question that arises is whether Oman’s monetary policy enjoys some independence. Or such deviations, especially among short-term interest rates, suggest a need to have a relook at the liquidity management framework. It may be underlined that any large deviation of Oman’s interest rate from the USA interest rates for long-period could undermine internal and external balance.

Against the above backdrop, this study makes an attempt to analyze the alignment of the inter-bank overnight rate with CBO’s policy rate and the US Fed Fund Rate as well as examine the factors driving the overnight inter-bank rate in Oman. We have not come across any such study in case of Oman. In fact, a few studies have touched upon this subject in GCC region. A related study by Hamdan, Pattaniak, and Yousuf (2007) examine the transmission mechanism of monetary policy in Oman. Chailloux and Hakura (2009) analyze the U.A.E.’s liquidity management framework in the context of the 2008 global financial crisis. Our study add to the scanty literature on the subject with respect to the GCC countries. The scheme of the study is as follows. The alignment of the overnight inter-bank rate is investigated in Section II, while Section III contains the contours of the current liquidity management framework. The studies on the similar topics are reviewed in Section III, while the factors deriving the overnight inter-bank rate are empirically examined in Section IV. Section V analyzes the impact of liquidity conditions on the interest rates in the economy. The concluding observations are furnished in Section VI.

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Central Bank of Oman Occasional Paper: 2019-1

Section II: Alignment of Oman’s Interest Rates with that of USA

In an environment of free capital mobility and fixed exchange rate, the interest rates in Oman must be closely aligned with the commensurate interest rates prevailing in the US keeping in view the free arbitrage opportunity. Accordingly, the overnight inter-bank rate in Oman should be closely aligned with the US’s overnight inter- bank rate even if the policy rate of Oman differs from that of USA. The monthly data shows that the overnight inter-bank rate in Oman largely follows the trends in the US overnight rate i.e. Effective Federal Fund Rate (EFFR). Notwithstanding following the similar trend, the overnight inter-bank rate in Oman have been found diverging from EFFR and such divergence was quite large at times (Chart 1). For example, Oman’s overnight inter-bank rate remained much lower than EFFR during the period January 2005-August 2008. But it was very closely aligned to EFFR during the subsequent period until December 2016. This is the period when the policy rate was largely sub-zero and liquidity was abundant in the USA due to quantitative easing. In Oman also, the liquidity was surplus largely during this period, reflecting large inflows of foreign exchange due to oil exports. Again Oman’s overnight inter-bank rate diverged downward from EFFR during the period January-May 2017. The large deviations of Oman’s overnight inter-bank rate from EFFR during intermittent periods suggested some decoupling of the former from the latter which could be attributed to domestic factors. On the other hand, Oman’s overnight inter-bank rate had been much lower than the CBO’ policy rate, i.e. repo rate at with banks avail liquidity from the CBO (Chart 1). In fact, misalignment of the overnight interbank-rate with policy rate is by construct as the latter is defined as 50 basis points spread over the monthly US$ LIBOR rate or 1 percent whichever is higher.

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Central Bank of Oman Occasional Paper: 2019-1

Chart 1: Overnight Inter-bank Rate, Repo Rate and 7.00 Federal Fund Rate

6.00 Repo Rate 5.00 Overnight Rate 4.00 Effective Federal Fund Rate

3.00 Percent

2.00

1.00

0.00

Jul-07 Jul-12 Jul-17

Jan-15 Jan-05 Jan-10

Jun-05 Jun-10 Jun-15

Oct-08 Oct-13

Sep-06 Feb-07 Sep-11 Feb-12 Sep-16 Feb-17

Apr-06 Apr-11 Apr-16

Dec-07 Dec-12

Mar-09 Mar-14

Aug-14 Nov-05 Aug-09 Nov-10 Nov-15 May-13 May-08 Notwithstanding intermittent periods of divergence, one inference that could be drawn from the above chart is that Oman’s overnight inter-bank rate is anchored to the US EEFR and not to the policy rate (repo rate), which is quite logical being US$ as the anchor currency for Oman’s pegged exchange rate. It has, however, been noticed that the gap between Oman’s policy rate (repo rate) and the US FFER has narrowed down in the last few years and more so in the recent past. This pattern could also be observed in the narrowing of the gap between the spreads of Oman’s overnight inter-bank rate over the policy rate and over the US FFER (Chart 2).

Chart 2: Spread of Overnight Rate over Repo Rate and Federal Fund Rate

(a) Monthly (b) Daily 3.00 0.60 0.40 2.00 0.20 1.00 0.00 0.00 -0.20 -1.00 -0.40

-2.00 Percent -0.60 Percent -3.00 -0.80 -4.00 -1.00

-5.00 -1.20

Jul-07 Jul-12

Jan-05 Jan-10 Jan-15

Sep-06 Sep-11 Sep-16

Mar-09 Mar-14

Nov-05 Nov-10 Nov-15

May-08 May-13

8-Jun-15

9-Feb-15 1-Feb-17

10-Jul-13 7-Mar-16

6-Nov-13

8-May-13 20-Jan-16 4-May-16

15-Oct-14

10-Sep-13 21-Sep-15

13-Apr-15 28-Apr-16

13-Mar-14

10-Aug-15 17-Nov-15 11-Aug-16 29-Nov-16 Spread over Repo Rate Spread over EFFR Spread over Repo22-May-14 Rate Spread over EFFR

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Central Bank of Oman Occasional Paper: 2019-1

The average monthly spread of the overnight rate over the US EFFR (absolute by ignoring sign) declined from 176 basis points during 2005-2008 to just 5 basis points during the period 2009-2016 but increased to 20 basis points in 2017 (up to October). On the other hand, the average monthly spread over the repo rate dropped from 258 basis points during 2005-08 to 122 basis points during 2009-2016 and further to 73 basis points in 2017 (up to October. The diverse movements in both the spreads (i.e. upward movement in the spread over US EFFR and downward movement in the spread over policy rate) suggest increased alignment of the policy rate with the USA EFFR. This improved alignment had been mainly driven by the increase in federal fund target rate with the onset of monetary policy normalization in the USA as the CBO’s policy rate has a floor of one percent. During the period 2009-2015, the fed fund target rate was sub-zero while the CBO’s policy rate was 1.0 percent due to the floor and this resulted in a large difference between these two rates.

Other interest rates in Oman also follow the movement in the corresponding interest rates prevailing in the USA. For example, the lending rate in Oman generally moved in tandem with the lending rate in the USA, albeit the former remained largely higher than the latter (Chart 3). Furthermore, the lending rate in Oman has been found responding to that of USA gradually and at times, resulted in a large difference between the two (e.g. a difference of 4.1 percentage points in 2003 and 4.2 percentage points in 2009). Following the global financial crisis, the lending rate in the USA declined sharply while that of Oman dropped gradually before inching up somewhat in 2016. Consequently, the difference between the lending rate in Oman and that of USA also came down gradually from 4.2 percentage points in 2009 to 1.5 percentage points in 2015.

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Central Bank of Oman Occasional Paper: 2019-1

Chart 3: Lending Rate in Oman and USA 12 10 8

Percent 6 4

2

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 USA Oman

Source: World Bank database.

Overall, it has been found that Oman’s interest rates move largely in sync with the US’s interest rates, albeit some decoupling was observed during intermittent periods in case of overnight rate. Nonetheless, the reasons for this decoupling as well needs to be empirically investigated.

Section III: Liquidity Management Framework

The economic theory “Impossible Trinity” (also known as Trilemma) explains that a country cannot pursue fixed exchange rate, free capital mobility and independent monetary policy simultaneously, which means it will have to relinquish one of these policy objectives. If a country has a fixed exchange rate regime and an open capital account, it will have to jettison independent monetary policy. Of course, a country could choose for an interior solution, e.g. pursue fixed exchange rate and some degree of monetary policy independence with some capital controls. Oman’s exchange rate is pegged to USD and at the same time, it has an open capital account. Therefore, Oman does not have an independent monetary policy and the anchor country’s (USA) monetary policy should prevail, which means that interest rates in

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Central Bank of Oman Occasional Paper: 2019-1

Oman should replicate that prevailing in the USA. The next logical question is that if interest rates in Oman are to be decided by the monetary policy stance of the USA, what is the relevance of the liquidity management operations by CBO?

The need for such operations by CBO, however, arises for smoothening the impact of large foreign exchange inflows and outflows that might take place due to various factors, viz. exports and imports, free arbitrage opportunity, etc. The free arbitrage means that investors are free to borrow from one country and lend in another county due to interest rate differentials, e.g. if interest rate in Oman is lower than that of USA, investors can borrow from Oman and invest in the USA to earn interest rate spread and vice versa if interest rate in USA is lower. This phenomenon is also called carry trade in literature. So lower interest rate country will witness capital flight (outflows) for higher returns. The change in interest rates in the USA would provide an arbitrage opportunity and may result in capital inflows or outflows from Oman depending upon direction and extent of such change. Similarly, large swings in domestic liquidity conditions would affect the domestic interest rates in Oman, especially short-term rates and may provide an arbitrage opportunity that may lead to large capital inflows or outflows. It is, however, assumed that market forces would exploit free arbitrage opportunity and align Oman’s interest rates with USA’s interest rates automatically, which means that Oman’s interest rates adjusted for some risk premia should be at par with interest rates prevailing in the USA.

The CBO absorbs the excess foreign exchange inflows and outflows to protect RO’s fixed peg with USD. It would undertake purchase (sale) interventions in case of inflows (outflows) with the resultant impact on domestic liquidity which would necessitate liquidity management operations (absorptions or injection), otherwise it would result in surplus (deficit) liquidity and lower (increase) the overnight rate, triggering capital outflows (inflows).

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Central Bank of Oman Occasional Paper: 2019-1

The extant liquidity management framework of the CBO comprises of both direct and indirect instruments. Direct instruments mainly include minimum reserve requirement, which is 5 percent presently. The banks are mandated to keep 5 percent of their deposit base with CBO. The banks’ investment in unencumbered treasury bills, Government Development Bonds (GDBs), and Government Sukuk up to 2 percent of their deposits was allowed to be counted as part of the eligible reserve from April 2016 to improve the liquidity conditions. On the other hand, indirect instruments of liquidity management mainly include issuance of certificate of deposits (CDs), repurchase agreements (repos), and rediscounting of commercial papers. The CBO has also been using discounting of treasury bills (TBs) on behalf of the Government for such operations lately. It may be pointed out that CBO has been increasingly relying on indirect instruments for liquidity management operations over the last few years. The purpose-wise classification of these instruments, i.e. used for injection or absorption of liquidity is furnished below.

Injection Absorption Repos Issuance of CDs Rediscounting facility Auctioning of treasury bills Lowering of Cash Reserve Requirement Increase in Cash Reserve Requirement

Notwithstanding availability of various direct and indirect instruments, CBO mainly uses repo transactions for liquidity injections wherein overnight liquidity is provided to banks against eligible securities (with a commitment to reverse the transaction next day), and auctioning of Government treasury bills for liquidity absorption since issuance of CDs have been discontinued. The liquidity absorption does not happen to the desired extent by auctioning of TBs as liquidity absorbed goes back into the banking system the moment Government spends it. Moreover, the Government keeps some money as deposits with banks for short-period in order to earn higher 9

Central Bank of Oman Occasional Paper: 2019-1 interest rates. As Oman was earning large foreign exchange until recent period, the banking system was largely experiencing surplus liquidity. Thus, CBO’s liquidity management operations were highly skewed towards liquidity absorptions.

A chart below demonstrate the liquidity management operations by the CBO in a surplus condition. If there is free arbitrage between Oman and USA, interest rate in Oman (adjusted for risk premia) should be at par with commensurate interest rate prevailing in the USA, irrespective of liquidity management operations undertaken by CBO.

Chart 4: Surplus Liquidity Management Operations

I D3 D1 1 D2 c d I1 a e

I* b f I2

R* R2 R1 R3 R4 R

The demand for Central Bank’s reserve (liquidity) is downward sloping, exhibited by D1curve, which means that there is an inverse relationship between interest rate (price) and demand for reserves. Reserves and liquidity have been used interchangeably here. At policy rate I*, the demand for liquidity is R* and the CBO supplies the same amount (R*) and equilibrium is achieved at point (a). Now suppose, demand curve shifts downward from D1 to D2, meaning thereby that demand for liquidity has come down. Now at policy rate I*, demand for liquidity has fallen from R* to R1 and there is surplus liquidity equivalent to R*-R1 that the CBO needs to absorb if it wants the overnight inter-bank rate to be equal to the policy rate (I*). Otherwise, the overnight inter-bank rate would plunge to I2. Now suppose,

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Central Bank of Oman Occasional Paper: 2019-1

USA increases its policy rate to I1, i.e. raises target rate for overnight inter-bank rate (Federal Fund Rate) to I1, the CBO would need to absorb higher liquidity equivalent to R*-R2 in order to align overnight inter-bank rate with new rate (I1). In the new circumstances, even if the CBO decides to absorb lower liquidity at a rate lower than USA’s new policy rate (I1), the overnight rate in Oman should converge to the USA’s overnight rate due to free arbitrage between two countries.

For example, let us assume that the CBO decides to keep the policy rate at I* or any other rate lower than USA’s policy rate (I1) and accordingly, absorbs liquidity from the system lower than what is required to be absorbed to align the overnight rate with USA’ new policy rate. In this situation, the market participants would find an arbitrage opportunity and would like to borrow from Oman and invest in the USA. It would shift the demand curve for reserves upward from D2 to D1, increasing demand for reserves from R1 to R* but the CBO’s supply of reserves at R1 should push the overnight rate from I* to I1. If the CBO decides to supply reserves equivalent to the new demand R* to keep the overnight rate at I*, the arbitrage opportunity would shift the demand curve further upward and this process would continue until the overnight rate converges to the US overnight rate. On the other hand, if suppose the demand curve shifts upward from D1 to D3, meaning thereby that demand for reserves is gone up from R* to R3 at interest rate I*, the CBO needs to inject additional liquidity equivalent to R3-R*. If the CBO continues to supply R* liquidity, the overnight interest rate will shoot up to I1. Now suppose, the US reduces its policy rate to I2, the CBO would require injecting additional liquidity equivalent to R4-R*, instead of earlier R3-R*, in order to align its overnight rate with the US’s policy rate. If the CBO continues to supply less liquidity, the overnight rate in Oman should eventually converge to the US policy rate through free arbitrage.

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Central Bank of Oman Occasional Paper: 2019-1

It has been observed that CBO absorbs surplus liquidity at an interest rate lower than USA’s policy rate or provides liquidity at higher interest rate than USA’s policy rate and Oman’s overnight inter-bank rate also does not always converge to the USA’s federal fund rate. The divergence between Oman’s overnight inter-bank and USA’s federal fund rate may suggest the absence of free arbitrage between Oman and USA as some restrictions on capital flows are imposed on prudent considerations.

Section IV: A Glance through the Select Literature

Literature investigating the GCC countries’ monetary policy in terms of its alignment with the US monetary policy and the liquidity management frameworks implemented to achieve such alignment is scanty. Espinoza and Prasad (2012) find that Oman’s domestic interest rates are somewhat delinked from the US policy rate, i.e. federal fund rate - the former persisted to be lower than the latter without any synchronization between the two in terms of trend. While investigating the pass- through of domestic policy rates to bank retail rates, they find it low not only for Oman but for most GCC countries reflecting the shallowness of their financial markets and prudential regulations on the banking sector. They, however, conclude that the transmission of the GCC countries’ semi-independent monetary policies has a strong and statistically significant impact on broad money, non-oil activity, and inflation. Similarly, Cevik and Teksoz (2012) while investigating the effectiveness of monetary policy transmission in GCC countries find that the relatively effective transmission channels are the interest rate and bank lending. They also conclude similar to that of Espinoza and Prasad’s (2012) about the need of strengthening financial intermediation and development of liquid capital markets for enhancing the effectiveness of monetary policy transmission in GCC region.

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Central Bank of Oman Occasional Paper: 2019-1

In a more focused study, Chailloux and Hakura (2009) examine and analyze the liquidity management framework of the United Arab Emirates Central Bank (CBU) and its role in alleviating liquidity pressures on commercial banks after the 2008 global crisis. The authors highlight that, prior to the crisis, banks maintained ample liquidity in U.S. dollars, therefore limiting their need to raise liquidity in UAE Dirhams and limiting the CBU’s initiative of providing diverse liquidity management instruments for the banks. Further, as the local money market remained shallow with short-tenor instruments (including certificates of deposit) and no local repo market for government paper or on any other private debt instrument, they emphasize the need for CBU to diversify the liquidity management instruments, including the introduction of a domestic bond market. Elsamadisy et. al (2013) study the liquidity management framework of Qatar’s Central Bank before and after the 2008 financial crisis. As in the case of many emerging market economies (EMEs) and especially those of the GCC, the Qatari economy contained a structural primary liquidity surplus, mainly due to large capital inflows from oil revenues. They establish that such surplus caused the weakening of the interest rate transmission channel. Before the global crisis, Qatar’s liquidity management framework was limited to reserve requirements, certificates of deposit, repurchase agreements and standing facilities, however, after the crisis, the QCB adopted new liquidity management tools (such as an overnight liquidity window at 3% interest rate). Bhattacharyya and Sahoo (2011) provide comparative mixes of liquidity management frameworks to derive the optimal mix for stabilizing liquidity prices in EMEs. The authors establish the efficacy of coupling both rate and quantum monetary policy instruments to achieve effective liquidity management under the condition that market sensitivity to central bank liquidity operations must be stronger than its response to autonomous liquidity factors. In a study that assesses macro- prudential regulation policy in the GCC, Arvai et. al (2014) touch upon the

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Central Bank of Oman Occasional Paper: 2019-1 importance of developing GCC countries’ liquidity management frameworks for crafting appropriate macro-prudential instruments. Liquidity management relies primarily on reserve requirements and standing facilities (also in the form of certificates of deposit) for liquidity absorption, with the latter being a passive instrument.

Section V: Data, Methodology and Empirical Investigation Data & Variables

The monthly data has been used from January 2005 to August 2017. The monthly frequency is chosen to cover a longer period of empirical exercise as daily data is available only for a limited period, and also to cover all possible indicators of domestic liquidity conditions. Oman’s overnight inter-bank rate (OIBR) is supposed to be aligned with EFFR due to the currency peg arrangement and open capital account. Furthermore, the policy rate (repo rate) is defined as monthly US$ LIBOR rate plus a spread of 50 basis points or 1 percent whichever is higher. Consequently, EFFR should be the main driver of OIBR in Oman. Besides EFFR, the evolving domestic liquidity conditions could also have some influence on the OIBR. Thus, the evolving liquidity conditions are captured through various variables: (i) the net liquidity availed by the banks from the CBO, (ii) excess reserves maintained by banks with CBO, (iii) difference between the growths of banks’ deposits and credit, and (iv) lending ratio. The net liquidity availed by the banks from the CBO as well as the excess reserves maintained by banks with CBO have been scaled by taking their ratio to the required reserves. The variables used along with their description are given in Table 1 below.

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Central Bank of Oman Occasional Paper: 2019-1

Table 1: Variables’ Description Variable Description Source Expected impact on OIBR OIBR Overnight inter-bank rate in CBO Oman SPREAD The difference OIBR and CBO Repo rate SPREAD1 The difference OIBR and CBO and EFFR Federal Board EFFR Effective Federal Fund Rate Federal + (EFFR). This is an overnight Reserve Board inter-bank rate in the USA. LQDT1 Net liquidity availed by the CBO + banks from the Central Bank of Oman (liquidity injected through repos minus liquidity absorbed through the issuance of CDs) LQDT2 Net liquidity availed by the CBO + banks from the Central Bank of Oman (liquidity injected through repos minus liquidity absorbed through the issuance of CDs and treasury bills (TBs). LQCD1R LQCD1 as the ratio to the CBO + required reserves LQCD2R LQCD2 as the ratio to the CBO + required reserves EXCSRES1 Average daily excess CBO - average reserve as the ratio to the average daily required reserves EXCSRES2 Average daily excess CBO - average reserve as the ratio to total deposits DIFBG Difference between the CBO - growth of banks’ deposits and loans.

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Central Bank of Oman Occasional Paper: 2019-1

The movements of various domestic liquidity indicators in the same direction may not convey the similar inference about liquidity conditions. Increase in LQCD1R and LQCD2R, and LDRATIO would mean tightening of liquidity conditions and decease would mean an easing of liquidity conditions, whereas the increase in EXCSRES1, EXCSRES2, and DIFBG would reflect an easing of liquidity conditions and decrease would suggest tightening of liquidity conditions.

Methodology

Since all variables are not stationary in their level, the simple regression model may provide spurious results. Furthermore, as all variables are not of the same unit root order, the Autoregressive Distributed Lags (ARDL) model is used for estimating long-run relationship. ARDL model is also found to be yielding consistent and asymptotically normal estimates of the long-run coefficients in case of both I(0) and I(1) regressors (Pesaran and Shin, 1997). Another major advantage of ARDL model is that it considers sufficient as well as varying numbers of lags to capture the data generating process in a general-to-specific framework (Laurenceson and Chai 2003). Additionally, ARDL model provides robust and consistent estimates even in case of small sample size†. The functional form of the error correction ARDL framework, as per Pesaran and Pesaran (1997) and Pesaran and Shin (2001), is as under:

푝−1 푝−1

∆푦푡 = 푐0 + 푐1푡 + ∅푦푥푧푡−1 + ∑ 훽푖 ∆푦푡−1 + ∑ 휗푖 ∆푥푡−1 + 푢푡 푡 = 1, … … … 푛 (1) 푖=1 푖=0

† ARDL model can also identify dependent and independent variables in case of a single long-run relationship. Furthermore, a simple linear transformation under ARDL model provides error correction model (ECM) wherein short-run adjustments reinstate long run equilibrium.

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Central Bank of Oman Occasional Paper: 2019-1

Where yt is the dependent variable, t is the trend, the coefficients βi and ϑi represents the short run dynamics of the model, and zt is the vector of independent variables

(xt, yt) in level, and Øyx are long-run coefficients.

As per equation (1), our model to estimate determinants of the overnight inter-bank rate in the model could be written in the ARDL functional form as under:

푝−1

∆푂퐼퐵푅푡 = 푐0 + ∅1푂퐼퐵푅푡−1 + ∅2퐿푄퐶퐷푡−1 + ∅3퐹퐹퐸푅푡−1 + ∑ 훽푖 ∆푂퐼퐵푅푡−1 푖=1 푝−1 푝−1

+ ∑ 휗푖 ∆퐿푄퐶퐷푡−1 + ∑ Ω푖 ∆퐹퐹퐸푅푡−1 + 푢푡 푡 = 1, … … … 푛 (2) 푖=0 푖=1

Where parameters βi, ϑi, and Ωi are short-run coefficients and parameters Ø1, Ø2, and

Ø3 are long-run coefficients. In first test, the null hypothesis of no cointegration (i.e.

Ø1= Ø2 = Ø3 =0) is tested by computing F-statistics. The computed F-Statistic is compared with the Pesaran et al. (2001)’s critical values (lower bound and upper bound). If F Statistics is higher than the upper bound value, then the null hypothesis of no cointegration will be rejected, which means there exists a long-run relationship between the dependent and independent variables. The F-statistics smaller than the lower bound suggests acceptance of null hypothesis, i.e. no long-run relationship while the value between lower bound and upper bound indicates the inconclusive result. Once the existence of a long-run relationship is established by bound test, the long-run and short-run versions of the models are estimated. The error correction coefficient of the short-run model would establish whether long-run equilibrium will be reinstated once any deviation happens.

Results First, we have tested for unit root priority of the variables using Augmented Dickey- Fuller (ADF) and Phillips Perron (PP) tests, and the results are furnished in Table 2. The results of both tests indicate that dependent variables, viz. OIBR, SPREAD, and

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Central Bank of Oman Occasional Paper: 2019-1

SPREAD1 are stationary in their first difference, i.e. they are I(1), whereas all explanatory variables (viz. LQCD1R, LQCD2R, EXCSRES1, EXCSRES2, LDRATIO and DIFFBG), except for EFFR, are found to be stationary in their levels. EFFR is found to be stationary in level as per the ADF test but only stationary in the first difference as per the PP test. DIFFBG is also weakly stationary in the first difference.

Table 2: Results of Unit Root Tests (Monthly Data) Variable Augmented Dickey-Fuller Phillips Perron (ADF) (PP) t-stats Prob. t-stats Prob. OIBR -1.375 0.593 -1.566 0.497 ΔOIBR -14.729 0.000 -14.613 0.000 SPREAD -1.690 0.434 -17.058 0.000 Δ SPREAD -2.038 0.271 -18.619 0.000 EFFR -3.074 0.031 -1.132 0.702 ΔEFFR -5.383 0.000 -5.257 0.000 LQCD1R -2.741 0.069 -4.345 0.000 LQCD2R -3.029 0.034 -4.762 0.000 EXCSRES1 -6.298 0.000 -6.280 0.000 EXCSRES2 -5.945 0.000 -5.886 0.000 LDRATIO -3.095 0.029 -3.984 0.001 DIFFBG -2.668 0.082 -2.599 0.095

We have estimated three ARDL models with OIBR, SPREAD, and SPREAD1 as dependent variables with six alternative specifications for each model. The six alternative specifications for each model have EFFR as common explanatory variable and a different explanatory variable to represent the domestic liquidity conditions. Firstly, we test for the existence of cointegration by estimating ARDL Bound test, and Appendix Tables 1 to 3 contain the results. The F-Statistics in all models is found to be above the critical values of upper bound tabulated in Pesaran et al. (2001). Therefore, we reject the null hypothesis of no cointegration, means

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Central Bank of Oman Occasional Paper: 2019-1 thereby that there exists a long-run relationship between the dependent variables (OIBR, SPREAD, SPREAD1) and other explanatory variables (EFFR, LQCD1R, LQCD2R, LDRATIO, EXSRES1, EXCSRES2, and DIFFBG FFER, and LDRT).

After establishing that there exists a long-run cointegrating relationship between dependent variables and their explanatory variables, we estimate long-run and short- run (error correction mechanism) relationships in ARDL models. The Schwarz Criterion (SC) has been used for optimal lag selection in the ARDL model as Pesaran and Smith (1998) also argue in favour of this criteria due to its parsimonious specifications. Furthermore, this criterion also fits well in small data sample (Pahlavani, 2005). The results of ARDL models are furnished for investigation in Tables 6-8. The ARDL model with OIBR as dependent variable finds that EFFR along with liquidity conditions are drivers of OIBR in Oman. The coefficient of EFFR is positive and statistically significant, and suggests that increase in EFFR leads to rising in OIBR (Table 3).

Table 3: Results of Long-Run Relationship and Error Correction Mechanism (ECM) (Dependent variable: OIBR) Regressor Alternative Specifications I II III IV V VI EFFR 0.609 0.611 0.509 0.460 0.583 0.547 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) LQCD1R 0.052 (0.002) LQCD2R 0.057 (0.001) EXCSRES1 -0.010 (0.036) EXCSRS2 -0.249 (0.035) LDRATIO -0.025 (0.496) DIFFBG -0.029 (0.066) 19

Central Bank of Oman Occasional Paper: 2019-1

C 0.321 0.354 0.684 0.779 2.062 0.087 (0.001) (0.000) (0.017) (0.019) (0.477) (0.009) ECM -0.459 -0.465 -0.247 -0.269 -0.312 -0.379 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Adjusted R2 0.437 0.447 0.387 0.382 0.426 0.473 DW Stat 1.931 1.936 1.959 1.991 2.026 2.044

The coefficient of LQCD1R, and LQCD2R are positive and statistically significant, while the coefficients of EXCSRES1, EXCSRES2, and DIFFBG are negative and statistically significant. The positive coefficient of the liquidity conditions suggests that tightening of liquidity causes a rise in OIBR, while easing of that causes a decline in OIBR. Based on the above results, it may be fair to infer that any temporary deviation of OIBR from EFFR is caused by the evolving domestic liquidity conditions. For example, the higher growth in banks’ deposits than that in banks’ lending is likely to result in easing of domestic liquidity conditions at least until arbitrage align the liquidity conditions with that of USA.

The results of ARDL model with SPREAD as a dependent variable, furnished in Table 4, are helpful in corroborating the above results. The SPREAD is defined as the difference between OIBR and Repo Rate, and the Repo Rate is linked to the US$ LIBOR which in turn reflects the movements in EFFR. The negative coefficient of EFFR suggests that the transmission of EFFR to OIBR happens over the period and not instantly, and accordingly, an increase in EFFR reduces SPREAD initially. Therefore, any temporary deviation of OIBR from EFFR is also contributed by the slow transmission process. Liquidity conditions have, however, been found impacting the SPREAD in a similar way as in case of OIBR, i.e. tightening leads to rise in SPREAD and easing results in decline in SPREAD. Accordingly, these results also corroborate that the evolving domestic liquidity conditions also contribute to the OIBR deviation from EFFR.

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Table 4: Results of Long-Run Relationship and Error Correction Mechanism (ECM) (Dependent variable: SPREAD) Regressor Alternative Specifications I II III IV V VI EFFR -0.464 -0.469 -0.156 -0.287 -0.296 -0.666 (0.000) (0.000) (0.013) (0.000) (0.005) (0.000) LQCD1R 0.087 (0.053) LQCD2R 0.090 (0.000) EXCSRES1 -0.013 (0.002) EXCSRS2 -0.275 (0.030) LDRATIO 0.141 (0.052) DIFFBG -0.038 (0.266) C -0.601 -0.567 0.0157 0.012 0.014 -0.898 (0.007) (0.028) (0.000) (0.000) (0.000) (0.000) ECM -0.191 -0.378 -0.386 -0.232 -0.232 -0.128 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Adjusted R2 0.326 0.326 0.279 0.225 0.225 0.358 DW Stat 2.015 2.017 2.184 1.987 1.987 2.072

We have also estimated ARDL model with SPREAD1 as a dependent variable in order to further corroborate the results of above models with OIBR and SPREAD as dependent variables. Table 5 contains the results of this model and it could be seen that EFFR and liquidity conditions are main movers of the SPREAD1 as in case of OIBR and SPREAD.

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Table 5: Results of Long-Run Relationship and Error Correction Mechanism (ECM) (Dependent variable: SPREAD1) Regressor Alternative Specifications I II III IV V VI EFFR -0.391 -0.391 -0.491 -0.540 -0.417 -0.453 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) LQCD1R 0.052 (0.002) LQCD2R 0.054 (0.002) EXCSRES1 -0.010 (0.036) EXCSRS2 -0.250 (0.035) LDRATIO -0.025 (0.495) DIFFBG -0.026 (0.058) C 0.322 0.337 0.689 0.782 2.070 -0.080 (0.001) (0.001) (0.017) (0.019) (0.475) (0.006) ECM -0.459 -0.465 -0.246 -0.269 -0.312 -0.391 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Adjusted R2 0.392 0.405 0.327 0.322 0.425 0.424 DW Stat 1.932 1.927 1.957 1.989 2.025 2.020

The impulse response of OIBR to one standard deviation shock to EEFR and liquidity indicators (viz. LQCD1R, EXCSRES, DIFFBG, and LDRATIO) is furnished in Chart 5 below. The response of OIBR to all these shocks is found to be on the expected lines, however, response to DIFFBG is small and dissipates after some time.

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Chart 5: Impulse Response of OIBR

Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.

Response of OIBR to OIBR Response of OIBR to EFFR

.20 .20

.15 .15

.10 .10

.05 .05

.00 .00

-.05 -.05

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Response of OIBR to LQCD1R Response of INTBR to EXCSRES

.20 .20

.15 .15

.10 .10

.05 .05

.00 .00

-.05 -.05

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Response of INTBR to DIFFBG Response of INTBR to LDRATIO

.20 .20

.15 .15

.10 .10

.05 .05

.00 .00

-.05 -.05

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

The results of the short-term relationship between dependent variable and explanatory variables in all three models, furnished in Table 3 to 5 above, find that the coefficient of error correction mechanism (ECM) is negative, less than one, and statistically significant. Based on these results, we conclude that cointegrating relationship among all these models is stable as any deviation in the dependent variable (OIBR, SPREAD, and SPREAD1) from long-run equilibrium path will be corrected by error correction mechanism over next few periods.

Section V: Concluding Observations and Policy Inferences

The overnight interbank rate in Oman has largely been following the trends in the federal fund rate as Omani Rial (OMR) is pegged to USD. Nonetheless, its alignment 23

Central Bank of Oman Occasional Paper: 2019-1 with the latter has been found weak at times, despite Oman’s capital account is largely open. For example, the overnight inter-bank rate of Oman, on an average basis, was lower by 28 basis points than effective federal fund rate during the first half of 2017. The empirical results also suggest that transmission of a change in federal fund rate to the overnight inter-bank rate of Oman is not complete. Notwithstanding exchange rate pegged to USD and open capital account, lower pass- through of federal fund rate to Oman’s overnight inter-bank rate could be mainly attributed to less than free arbitrage between Oman and USA due to various factors such as prudential limits imposed on banks’ exposure in foreign exchange. Additionally, CBO does not have any floor rate to target the overnight rate, especially due to the absence of any standing deposit facility or any other non- discretionary instrument of liquidity absorption. In such situation, any large surplus not fully absorbed by the CBO will pull down the overnight rate leading to its deviation from the federal fund rate until arbitrage realigns it to some extent. Besides federal fund rate, the evolving domestic liquidity conditions also cause movement in the overnight inter-bank rate as depicted by the significance of various liquidity indicators in the model. Increase in the ratio of excess reserves to the required reserve as well as the difference between growth in deposits and credit reduces the overnight inter-bank rate reflecting easing of liquidity conditions, while net liquidity availed by banks from the CBO indicates tightening of liquidity condition and pushes the overnight inter-bank rate upward. Therefore, the evolving domestic liquidity conditions also explain the deviation of overnight inter-bank rate from federal fund rate, besides less than perfect arbitrage. One policy inference that may be drawn from these results is that all possible liquidity indicators may be used to analyze the evolving liquidity conditions.

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Select References: Árvai, Z., Prasad, A., & Katayama, K. (2014). “Macroprudential policy in the GCC countries”. Washington, DC: International Monetary Fund (IMF). Cevik, S., & Teksoz, K. (2012). “Lost in Transmission? The Effectiveness of Monetary Policy Transmission Channels in the GCC Countries”. Washington: International Monetary Fund. Chailloux, A., & Hakura, D. S. (2009). “Systemic liquidity management in the U.A.E: Issues and options”. Washington, D.C.: International Monetary Fund. Bhattacharyya, I & Sahoo, S. (2011). “Comparative Statics of Central Bank Liquidity Management: Some Insights”. Economics Research International. Elsamadisy, E. M., Alkhater, K. R., & Basher, S. A. (2013). “Pre- versus post-crisis central banking in Qatar”. Journal of Policy Modeling. Espinoza, R. A., & Prasad, A. (2012). “Monetary Policy Transmission in the GCC Countries”. Washington, DC: International Monetary Fund (IMF).

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Appendix Table 1: ARDL Bound Test (OIBR as a dependent variable) Model F-statistics Cointegration OIBR/LQCD1R, FFER, 9.154*** Yes OIBR/LQCD2R, FEER, 9.494*** Yes OIBR/LDRT, FEER, 15.861*** Yes OIBR/EXCSRES, FFER, 7.041*** Yes OIBR/EXCSRES1, FEER, 7.419*** Yes OIBR/DIFFBG, FEER, 11.453*** Yes Critical Values Lower Bound Upper Bound 1% 5.15 6.36 5% 3.79 4.85 10% 3.17 4.14 ***, **, and * indicate significant at 1%, 5%, and 10% significance level.

Appendix Table 2: ARDL Bound Test (SPREAD as a dependent variable) Model F-statistics Cointegration SPREAD/LQCD1R, FFER, 6.739*** Yes SPREAD/LQCD2R, FEER, 6.546*** Yes SPREAD /LDRT, FEER, 6.046** Yes SPREAD /EXCSRES, FFER, 8.230*** Yes SPREAD/EXCSRES1, FEER, 7.834*** Yes SPREAD /DIFFBG, FEER, 6.613*** Yes Critical Values Lower Bound Upper Bound 1% 5.15 6.36 5% 3.79 4.85 10% 3.17 4.14 ***, **, and * indicate significant at 1%, 5%, and 10% significance level.

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Appendix Table 3: ARDL Bound Test (SPREAD1 as a dependent variable) Model F-statistics Cointegration SPREAD1/LQCD1R, FFER, 9.151*** Yes SPREAD1/LQCD2R, FEER, 8.793*** Yes SPREAD1 /LDRT, FEER, 9.761*** Yes SPREAD1 /EXCSRES, FFER, 7.016*** Yes SPREAD1/EXCSRES1, FEER, 7.394*** Yes SPREAD1/DIFFBG, FEER, 11.653*** Critical Values Lower Bound Upper Bound 1% 5.15 6.36 5% 3.79 4.85 10% 3.17 4.14 ***, **, and * indicate significant at 1%, 5%, and 10% significance level.

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