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Oil market conditions and sovereign risk in MENA oil exporters and importers
Energy Policy xxx (xxxx) xxx
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Oil market conditions and sovereign risk in MENA oil exporters and importers Elie Bouri a, *, Imad Kachacha a, David Roubaud b a b
USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon Montpellier Business School, Montpellier, France
A R T I C L E I N F O
A B S T R A C T
JEL classification: C22 G10
We analyze for the first time how various levels of oil returns and oil volatility changes affect sovereign risk in static and time-varying settings. Empirical analyses involve daily data from February 14, 2011 to November 23, 2018 covering a sample of MENA oil-exporters and importers. The results from a quantile-based approach show that the sovereign risk of MENA oil-exporters and importers is directionally predicted by shocks in oil prices and oil volatility, especially during the oil crash of 2014–2016. Overall, the impact of oil returns and volatility changes occur in a very short time span, that is within one day lag, and the quantile specific reactions of sov ereign risk spreads are time varying. The impact of oil returns is asymmetric across quantiles. The results hold when we control for stock market returns. The findings have implications for investors in terms of portfolio and risk management. Importantly, the findings are useful to policymakers for sovereign risk management decisions, the cost of sovereign borrowing, and the market timing of debt issuance. Finally, the findings matter to bankers given that central and domestic banks hold large amounts of sovereign debt, which makes banking systems particularly exposed to their own sovereign stress.
Keywords: Crude oil Sovereign credit risk MENA Oil exporters and importers Dynamic conditional correlation Quantile dependence
1. Introduction Crude oil is a strategic energy commodity that plays a pivotal role in the global macroeconomic and financial scene. The economic and financial impacts of oil market shocks are well known (Montoro, 2012; Oladosu et al., 2018),1 and oil price shocks generally have an influence on production costs, price levels, and consumption dynamics (Hamilton, 2009). In energy-exporting countries especially, oil price fluctuations are extremely important as they can shape energy export revenues, government expenditure, and fiscal balances (e.g., El Anshasy and Bradley, 2012). Breunig and Chia (2015) show that the sovereign rating of oil-producing countries can be adversely affected by an unexpected decline in crude oil prices. Lower oil prices weaken the fiscal position of oil-exporters, and price volatility can induce macroeconomic
fluctuations and lead to a deterioration in financing conditions, making oil-exporting countries more vulnerable to credit events such as default. The collapse in world oil prices from a high of $115 in June 2014 to under $35 in February 2016, and the subsequent weak recovery of the oil market, fuelled many press articles highlighting the potential impact of lower oil prices on the sovereign credit default swap (CDS) spreads of oil exporters.2 These press articles argue that lower oil prices shrank oil revenues, pushing up sovereign yields and destabilizing fiscal sustain ability. This is relevant to many MENA countries, where high crude oil prices are an important catalyst for government spending, economic growth, financial prospects, and budget surpluses.3 While some empir ical evidence shows the importance of (1) oil prices for large developed economies (Lee et al., 2017) and (2) oil volatility for oil-exporting countries (Shahzad et al., 2017), less is known about the impact of oil
* Corresponding author. E-mail addresses: [email protected] (E. Bouri), [email protected] (I. Kachacha), [email protected] (D. Roubaud). 1 The impact of crude oil market conditions on equities (Arouri et al., 2011; Bouri, 2015), currencies (Basher et al., 2012), gold (Bedoui et al., 2018), and agricultural commodities (Nazlioglu, 2011; Ji et al., 2018) is well established in the academic literature. 2 The World Bank reports highlights the positive effect of the rebound in crude oil prices on the sovereign risk of oil-exporters such Russia and Saudi Arabia. http s://www.worldbank.org/content/dam/Worldbank/GEP/GEP2015a/pdfs/GEP2015a_chapter4_report_oil.pdf. 3 In some MENA oil-exporters, such as Gulf states, crude oil accounts for more than 30% of GDP and 80% of government revenue. Non-oil output also depends on energy revenues through government spending on capital projects and salaries. In Saudi Arabi, the petroleum sector represents around 45% of GDP and 90% of export earnings (https://www.weforum.org/agenda/2016/05/which-economies-are-most-reliant-on-oil/). Lower oil prices led the Saudi government budget to have a deficit of more than 78 billion US dollars in 2017. https://doi.org/10.1016/j.enpol.2019.111073 Received 26 January 2019; Received in revised form 8 October 2019; Accepted 25 October 2019 0301-4215/© 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Elie Bouri, Energy Policy, https://doi.org/10.1016/j.enpol.2019.111073
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price and oil volatility on the sovereign CDS spreads of MENA oil-exporters and importers. In fact, MENA oil importers are extremely understudied, and can be affected by movements in crude oil prices in a different way to major European and Asian oil-importing countries.4 Importantly, there is a lack of empirical evidence regarding the impact at extreme tails of distribution and whether the impact of large down ward oil price changes has the same magnitude as that of large upward price changes.5 Furthermore, previous studies often neglect the time-variability in the impact of oil returns and oil implied volatility on the sovereign credit risk. These areas are where we aim to contribute. While the current paper is related to previous studies (e.g., Lee et al., 2017; Shahzad et al., 2017), it differs in several respects. Firstly, it fo cuses on the oil-CDS nexus at various quantiles of both oil and CDS markets, considering both the return and volatility6 of crude oil prices. This represents a nice extension to prior studies that overlook the quantile dependence (Lee et al., 2017) or only consider the impact of oil volatility (Shahzad et al., 2017). Secondly, as opposed to prior studies, our data sample is at daily frequency, which is important in order to better capture the dynamics of the relationships among the oil and CDS markets, rather than using a weekly (Shahzad et al., 2017) or monthly (Lee et al., 2017) frequency. Interestingly, the full sample period is February 14, 2011 to November 23, 2018, covering periods of stable and sharp decline in crude oil prices as well as moderate and high volatility periods in the crude oil market (e.g., the oil price collapse of June 2014–February 2016), allowing us to determine how oil market condi tions impact the various quantiles of sovereign risk. Thirdly, this paper uncovers potential asymmetric effects and time-variability in the quantile dependence through the application of a recursive-rolling window approach which captures the dependence structure in a time-varying setting. As such, it accounts for the potential presence of structural breaks in the relationship between the variables under study. Our results are consistent with the models that highlight the importance of crude oil price in driving sovereign risk (Breunig and Chia, 2015; Fonseca et al., 2016; Liu et al., 2016), although they focus on the tails of the distribution of both oil price returns (oil implied vola tility) and sovereign risk spreads, which adds to the related literature (e. g., Lee et al., 2017; Shahzad et al., 2017). The rest of the paper is structured in five sections. The next section reviews the most relevant literature. Section 3 describes the econometric framework. Section 4 presents the data. Section 5 reports and discusses the empirical results. Section 6 concludes and offers policy implications.
in shaping the sovereign credit risk of developing economics (Longstaff et al., 2011; Amstad et al., 2016; Hibbert and Pavlova, 2017). Given that energy and non-energy commodities represent important global risk fac tors (Koutmos, 2019), a major strand of research focuses on the impact of commodity prices on the sovereign bonds of emerging and developing countries. Hilscher and Nosbusch (2010) consider the sovereign bond yields of emerging markets, and highlight the vulnerability of commodity exporters to external shocks. Similarly, Sun et al. (2011) point out the importance of commodity prices as a driver of the bond spreads of emerging markets. The authors document an inverse relationship between commodity price and sovereign bond spread. Bouri et al. (2017) consider the case of emerging and frontier economies. They apply a Granger cau sality in variance approach and provide evidence that the volatility of commodity and energy prices predict the volatility of sovereign spread risk, indicating that the predictability cannot always be explained by the country’s level of commodity and energy dependence. Other studies consider crude oil price as a determinant of sovereign bond spread and credit risk. Alexandre and de Benoist (2010) find that oil prices impact country bond risk premiums in emerging countries, differentiating between oil exporters and oil importers. They reveal that oil price return and volatility can particularly shape the sovereign bond spreads of oil-exporting countries such as Russia. Sharma and Thur aisamy (2013) consider the impact of oil price uncertainty on sovereign credit risk. Using a sample of Asian Pacific countries, they show that the volatility of crude oil prices can be used as a predictor of sovereign credit risk. Hooper (2015) documents evidence of an association between oil and gas reserves and sovereign spreads in emerging oil-exporting countries. Wegener et al. (2016) study the case of nine countries (Brazil, Malaysia, Norway, Qatar, Russia, Saudi Arabia, UK, USA, and Venezuela) within a bivariate GARCH model. They report evidence that positive oil price shocks reduce the sovereign credit risk of oil-producing countries. Breunig and Chia (2015) consider the effects of high oil prices, in the period 2003–2008, on the ratings of sovereign oil-exporting countries. They point to the potential impact of unanticipated de creases in crude oil prices on sovereign ratings. Taking a different perspective, Liu et al. (2016) focus on the statistical properties of the country risk of oil-exporting countries. They find that oil price volatility can accentuate the volatility of country risk ratings. A major sign of crude oil’s impact can be seen in the sovereign credit default swap (CDS) that offers investors protection against default risk.7 As indicated by Fonseca et al. (2016), the evolution of financial markets has led to the rise of the CDS market as an alternative measure of credit risk, replacing the measure of bond yield spread. Koutmos (2019) argues that CDS spreads reflect default probabilities and investors’ perceptions of credit risk. They are bilateral contracts that allow the buyer to pay a premium in basis points as protection against losses in case of a credit event on sovereign debt. Interestingly, the CDS market has experienced substantial growth in volume and size during the last two decades, allowing market participants to use the insurance policy against credit default in trading, hedging, and speculation activities (Koutmos, 2019). Specifically, sovereign CDS spreads are important elements of policy discussions due to their ability to reflect changes in the credit quality of a country and, thereby, the level of risk in the financial system. Accord ingly, in addition to portfolio managers and speculators, policymakers and bankers are interested in understanding the factors that affect variation in sovereign CDS spreads, such as crude oil prices. Fonseca et al. (2016) study the detriments of CDS spreads in the energy market. They indicate that volatility and jumps in crude oil prices have explanatory power for the stock market returns of energy companies. Using weekly data and a bootstrap rolling window approach, Shahzad
2. Related studies The empirical literature highlights the importance of global risk factors
4 Higher oil prices upsurge the oil import bill and often represent a financial burden for oil-importers that usually pay subsidies on fuel consumption. Conversely, lower in oil price reduces oil import bill to oil-importers, provoking net savings that improve their fiscal position and thereby the quality of their sovereign debt. However, the issue might be less straightforward and more complex in the MENA region. As argued by Bouri (2015), economic and financial conditions in MENA oil importers can be improved by higher oil prices, through various channels including higher remittances from oil rich Gulf countries, more investment in property and non-property markets, higher de posits in banks, and higher tourism receipts. For example, higher oil prices can have positive effects on stock market returns, especially in the real estate and banking sectors (Bouri, 2015). This suggests that the burden of subsidies might be offset by the overall economic benefits of higher oil prices. 5 The empirical literature often treats upside and downside movements symmetrically. 6 Specifically, the volatility of crude oil prices is measured by the forwardlooking measure of implied volatility from the Chicago Board Options Ex change (CBOE), called the OVX, which has the advantage of capturing future expectations of market participants in the crude oil market (Maghyereh et al., 2016).
7 The size of the CDS market has grown steadily in the last two decades. The reader can refer to the Bank for International Settlements for statistics regarding the size and importance of these financial credit derivatives. https://www.bis. org/publ/otc_hy1711.htm.
2
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et al. (2017) find evidence that the volatility of crude oil prices can predict the sovereign credit risk of several oil-exporters. However, they conduct a mean-based rolling window causality test, which overlooks potential time variation in quantile dependence. Furthermore, they limit their analyses to the dependence between oil volatility and sovereign risk, ignoring the impact of oil returns on sovereign risk. Another related study is that conducted by Lee et al. (2017) who use monthly data and apply a structural vector autoregression model. They show that oil price shocks lead to a reduction in country risk for net oil exporters, while the same heightens the country risk for net oil importers. However, Lee et al. (2017) overlook the impact of oil implied volatility on sovereign risk. Price dynamics among financial variables can often be better captured by higher frequency data such as daily data, and models that account for the entire return distribution (Uddin et al., 2019). Considering the above survey of the related literature, especially in regard to the works of Lee et al. (2017) and Shahzad et al. (2017), we extend our understanding of the effect of crude oil prices and volatility on sovereign credit risk in MENA oil exporters and importers. To do this, we examine the daily average and cross-dependence at various quantiles of both oil returns (oil implied volatility changes) and CDS spread changes in static and time-varying settings, while controlling for stock market returns. This is particularly relevant for MENA oil-exporting countries as it is important for energy security and regional social-political stability. Furthermore, understanding the impact of oil price returns and volatility on sovereign CDS spreads of MENA countries has implications for poli cymakers, investors, speculators, and academics. It can have important implications for the debt policies of countries, the volatility of debt, and the market timing of debt issuance. For example, a country can decide to borrow in a favorable interest rate environment, i.e. before an expected plunge in crude oil prices makes the borrowing more costly. Investors and speculators can use any evidence of significant effects when making in vestment and trading decisions. Bankers are interested in any evidence of the association between oil market conditions and sovereign risk because central and domestic banks in emerging economies generally hold large amounts of government debt (Arslanalp and Tsuda, 2014). Such a concentrated exposure of the banks limits the ability of the banking system to absorb shocks in the event of sovereign stress (Angeloni and Wolff, 2012), and, in the worst case scenario, impairs banks’ balance sheets and threatens the solvency of the banking system. This suggests the need for central and domestic banks to monitor any potential factors capable of elevating sovereign risk, including crude oil market condi tions, with the aim of maintaining banking and financial system stability. Academics, among others, can build on their analyses when modelling the structure of dependence between oil and CDS markets.
Table 1 Fuel exports as percentage of merchandise exports. United Arab Emirates Bahrain Egypt Lebanon Morocco
2010
2011
2012
2013
2014
2015
2016
n/a
n/a
53.52
51.52
42.50
n/a
20.23
74.35 29.83 0.17 1.07
71.84 30.91 0.14 2.61
64.15 31.54 2.94 3.97
59.01 26.93 9.97 5.03
66.83 23.88 1.10 3.29
58.14 18.42 n/a 1.48
55.03 16.35 0.61 0.86
Source: World Development Indicators, World Bank. n/a denoted not available. Disaggregated data on the fuel exports as percentage of merchandise exports are not available for Abu Dhabi and Dubai, which leads us to report the aggregated data for the United Arab Emirates.
3.1. Quantile dependence using the cross-quantilogram approach of Han et al. (2016) The cross-quantilogram approach of Han et al. (2016) emerges as a suitable approach to study the cross-quantile dependence between the variables, and capture asymmetries in the dependence structure. It con structs estimates of the effect the quantiles of oil returns (oil implied volatility changes) have on the quantiles of the CDS spread changes, providing a complete picture of the dependence structure. It offers flexi bility in estimating the quantile lead-lag relation between the variables at different lags. The output of the cross-quantilogram approach is repre sented in the form of a heat map of lag lengths. The recursive-rolling window approach is used to capture any potential time variation in the cross-dependence. The cross-quantilogram approach of Han et al. (2016) requires that the variables under study follow a stationary stochastic process. Consider two stationary time series fxi;t ; t ε Zg, i ¼ 1, 2, where x1;t de notes the oil returns (oil volatility changes) and x2;t denotes the country CDS spread changes. The density and distribution functions of the time series xi;t are labelled fi ð⋅Þ and Fi ð⋅Þ. The quantile of xi;t is represented as qi ð∝i Þ ¼ inffv : Fi ðvÞ � αi g for αi ε ð0; 1Þ; and the expression of two-dimensional series of quantiles are represented by ðq1 ð∝1 Þq2 ð∝2 ÞÞτ , for α � ð∝1 ; ∝2 Þτ . The cross-quantilogram for α-quantile with k lags can be written as: E½Ψ ðx q ðα ÞÞΨ ðx q ðα ÞÞ� ffiffiffiffiffiffiffiffiffiffiffiffiffi1ffiffiffiffiffiffiffi1ffiffiffiffiffiqαffi2ffiffiffiffiffiffi2;tffiffiffiffiffikffiffiffiffiffiffiffiffiffiffiffiffi2ffiffiffiffiffiffi2ffiffiffiffiffiffiffiffiffiffiffiffi ρα ðkÞ ¼ qffiffiffiffiffiffiffiffiffiαffiffi1ffiffiffiffiffiffi1;t 2 2 E½Ψ α1 ðx1;t q1 ðα1 ÞÞ� E½Ψ α2 ðx2;t q2 ðα2 ÞÞ�
(1)
where, k is the number of lead-lag periods to time t, Ψ a ðμÞ � 1½μ < 0�, 1ð ⋅Þ denotes the indicator function and 1½xi;t � qi ðαi Þ� is the quantile exceedance process. At different quantiles, the serial dependence be tween the two time series is captured through ρα ðkÞ. In the present framework, ρα ð1Þ measures the cross-correlation be tween oil returns - and alternatively oil volatility changes – below or above the quantile qoil returns ðαoil returns Þ at time t and the CDS spread being above or below the quantile qCDS spread changes ðαCDS spread changes Þ at time t. ρα ð1Þ ¼ 0 indicates that oil returns being below or above the quantile qoil returns ðαoil returns Þ at time t does not contain useful information for pre dicting whether the country CDS spread changes are below or above the quantile qcds ðαcds Þ during the next trading day (t þ 1). On the contrary, ρα ð1Þ 6¼ 0 indicates a one-day directional predictability from oil returns to the country CDS spread changes at α ¼ αoil returns ðαCDS spread changes Þ. The sample counterpart of the cross-quantilogram is estimated by: PT Ψ α1 ðx1;t q1 ðα1 ÞÞΨ α2 ðx2; t k b q ðα2 ÞÞ b ffiffiffiffiffiffiffiffiffiffiffiffit¼kþ1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiqffiP ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ρ α ðkÞ ¼ qffiP (2) T T 2 2 Ψ ðx b q ð α ÞÞ Ψ ðx b q 2 ðα2 ÞÞ 1 1 t¼kþ1 α1 1; t t¼kþ1 α2 2; t k
3. Models To analyze the dependence structure between crude oil and sover eign CDS markets, we apply the cross-quantilogram approach of Han et al. (2016), which is based on the cross-quantiles of both dependent and independent variables. Interestingly, this approach allows us to examine average and tail dependence and control explanatory variables. We also conduct a recursive analysis of the quantile dependence to examine the time variation in the dependence after controlling for stock returns.8
where, b q i ðαi Þ represents the unconditional sample quantile of xi;t ; as defined by Han et al. (2016). For p > 1, Han et al. (2016) suggest a quantile version of the Ljung-Box-Pierce statistic to test H0 : ρα ðkÞ ¼ 0 for all k, 1 � k � p against the alternative hypothesis H1 : ρα ðkÞ 6¼
8
We could have also used the quantile-on-quantile approach of Sim and Zhou et al. (2015), however, the cross-quantilogram approach allows us to account more easily for explanatory variables (i.e. stock returns) in the modelling of dependence. The recursive analysis of the cross-quantilogram allows us to ac count for the presence of structural breaks in the dependence. 3
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Table 2 Summary statistics and stationarity tests. Brent oil Oil OVX Abu Dhabi Dubai Bahrain Egypt Lebanon Morocco
Mean
Maximum
0.029 0.031 0.022 0.172 0.014 0.091 0.265 0.029
9.896 42.497 16.020 48.830 180.550 96.930 77.100 65.000
Minimum 8.245 43.991 15.980 29.570 189.010 141.520 82.850 35.230
Std. Dev.
Skewness
Kurtosis
1.889 4.874 2.275 6.112 9.734 12.166 10.464 5.425
0.206 0.987 0.582 0.783 0.399 0.353 0.044 0.483
5.938 14.343 10.545 11.566 135.060 23.467 13.737 20.358
Notes: This table presents summary statistics of daily data: crude oil returns, changes in oil volatility, changes in CDS spreads. The sample period is February 14, 2011 to November 23, 2018.
Dhabi, Dubai, and Bahrain) and three oil-importers (Lebanon, Egypt, and Morocco) within the Arab MENA region10. The crude oil OVX is computed by the CBOE, following the computation method of the S&P 500 implied volatility index, the VIX. It reflects market expectations of the 30-day implied volatility of oil prices, which makes it a powerful gauge of the forward-looking risk in the crude oil market. The CDS data consists of sovereign CDS mid-spreads of contracts with five years to maturity, which is typically the most liquid contract (Fonseca et al., 2016). In fact, CDS spread is expressed in basis points (bps) and quoted in annual percentage bps. The sample period spans February 14, 2011 to November 23, 2018, implying 1982 daily observations, chosen to be common across the oil and CDS markets. The beginning of the sample period is dictated by the lack of liquidity in some CDS spreads before February 14, 2011. The sample period is made interesting by the sharp decrease (increase) in the price (volatility) of crude oil during June 30, 2014 and February 29, 2016 and the concurrent movements in the CDS spreads of most oil exporters. All daily data series were extracted from Bloomberg Terminal. Figure A1 in the Appendix presents the evolution of crude oil prices, oil implied volatility, and sovereign CDS spread over the sample period. Interestingly, crude oil prices generally exhibit large fluctuations, especially around June 2014–February 2016 which corresponds to the meltdown of the crude market. Crude oil implied volatility spikes, signaling higher risk in the crude oil market. Furthermore, many sov ereign CDS spreads of oil exporters increase, reflecting the instability and decline of external revenues from oil exportation. However, an in crease in CDS spreads, particularly for Lebanon, is observed towards the end of the sample period, possibly driven by political events. Table 1 provides the percentage of fuel exports of merchandise ex ports. The largest percentages are for Bahrain, where there is a declining trend. A smaller percentage is reported for the United Arab Emirates (which encompasses Abu Dhabi and Dubai) that experienced more decline in fuel exports as a percentage of merchandise, to around 20%, which makes it the least dependent on petroleum revenue exports of the oil-exporters under study. Interestingly, in Egypt the percentage declined from around 30% in 2010 to less than 17% in 2016. Overall, Table 1 points to the possibility of oil-exporting countries with high percentage fuel exports co-moving with the market conditions of crude oil. To conduct empirical analyses, we use logarithmic-returns of crude oil prices, changes in oil implied volatility, and changes in sovereign CDS spreads.11 However, before we establish the econometric frame work, we present summary statistics for logarithmic-returns for crude oil
ðpÞ
b , 0 for at least one k, 1 � k � p, under the portmanteau test statistic, Q α for directional predictability from one variable to another for up to p lags over the quantile pair α ¼ ðα1 ; α2 Þ. Pp 2 ρ α ðkÞ b ðpÞ ¼ TðT þ 2Þ k¼1 b Q (3) α T k Given that the asymptotic distribution of cross-quantilogram con tains noise under H0 , Han et al. (2016) use the stationary bootstrap9 of Politis and Romano (1994) to approximate the distribution of the testing statistic under the null hypothesis that can then be used for statistical inference. 4. Data description The data set is at daily frequency. It includes Brent crude oil spot prices, the crude oil OVX, and CDS spreads for three oil-exporters (Abu Table 3 Stationarity tests. Panel A: Levels series Trend and intercept ADF Brent oil Oil OVX Abu Dhabi Dubai Bahrain Egypt Lebanon Morocco
1.675 2.800 2.618 2.670 2.491 2.361 2.086 2.568
Lag length for the ADF test
PP 1.777 2.651 2.406 2.631 2.377 2.498 1.300 2.630
0 3 2 0 2 1 0 2
Panel B: Changes and returns series Trend and intercept ADF PP
Lag length for the ADF test
Brent oil Oil OVX Abu Dhabi Dubai Bahrain Egypt Lebanon Morocco
0 2 1 0 1 1 0 4
42.979*** 28.661*** 28.541*** 45.781*** 37.226*** 31.633*** 47.711*** 25.477***
43.098*** 48.446*** 44.138*** 45.783*** 47.812*** 51.619*** 47.693*** 42.775***
Notes: Panel A presents the statistics of ADF and PP tests that are applied on oil prices, CDS spreads, and oil OVX levels. Panel B presents the statistics of the ADF and PP tests that are applied to the CDS changes, oil price returns, and oil OVX changes. In both panels, the ADF and PP tests are conducted with trend and intercept. The lag length is selected based on the Schwarz information criterion. *** indicates significance at the 1% level. The sample period is February 14, 2011 to November 23, 2018.
9
10 The choice of Bahrain, Egypt, Lebanon, Morocco, and two largest emirates of the UAE (Abu Dhabi and Dubai), is determined by the availability of sov ereign CDS. Unfortunately, the daily CDS spreads of Saudi Arabia and Qatar are very illiquid. 11 Since both CDS spreads and oil implied volatility are measured in per centage points or basis points, taking ‘ordinary’ first differences should be sufficient.
In this study, we use 1000 bootstrap iterations for statistical inference. 4
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Table 4 Unconditional correlation matrix. Brent oil Oil OVX Abu Dhabi Dubai Bahrain Egypt Lebanon Morocco
Brent oil
Oil OVX
Abu Dhabi
Dubai
Bahrain
Egypt
Lebanon
Morocco
1.000 0.292 0.175 0.173 0.140 0.029 0.053 0.081
1.000 0.174 0.200 0.099 0.017 0.078 0.118
1.000 0.557 0.254 0.152 0.191 0.246
1.000 0.307 0.187 0.172 0.272
1.000 0.117 0.142 0.199
1.000 0.131 0.179
1.000 0.108
1.000
Notes: This table provides pairwise Pearson correlation coefficients across the variables under study. The sample period is February 14, 2011 to November 23, 2018.
prices, changes in oil implied volatility, and changes in sovereign CDS spreads (see Table 2). The highest mean CDS spread is for Lebanon, whereas the highest standard deviation is for Egypt. By contrast, most oil exporters have a negative mean CDS. Crude oil price has a negative mean return, whereas oil implied volatility changes have a positive mean value. Generally, CDS spread changes have higher values for skewness and kurtosis than crude oil returns. We also apply two unit root tests, the augmented Dickey-Fuller (ADF) and Phillips Perron (PP), as described in Dickey and Fuller (1979) and Phillips and Perron (1988), respectively. The results are reported in Table 3. Based on the CDS changes, oil price returns and oil OVX changes, all series are stationary, as confirmed by the ADF and PP test statistics (see Table 3 Panel B). Note that those two tests are also applied to the levels, but the results show no evidence of stationarity for all level series (see Table 3 Panel A). Pairwise Pearson correlation coefficients across the variables (CDS changes, oil price returns, and oil OVX changes) are given in Table 4. Overall, the correlation is negative between crude oil and CDS markets, whereas it is positive between oil implied volatility and CDS markets. Specifically, more negative correlations are shown for oil exporters as compared to oil importers. The same is true for the correlation between oil implied volatility and CDS markets, although the correlation is positive.
generally insignificant or negative. This might stem from the structure of these oil-importers and their close ties with rich oil-exporters that make their economics, and thereby their public finance, affected positively by the presence of higher oil prices. This evidence partially concords with previous findings highlighting the positive effect of higher oil prices on stock market returns of MENA oil-importers such as Lebanon, Jordan and Morocco (Bouri, 2015). The results from Fig. 2 show evidence of a significant positive impact of oil volatility changes on CDS spread changes for both oil-exporters and importers, especially for lag 1. This is observed in various quan tiles, especially the middle and upper quantiles of oil volatility changes and CDS spread changes. The impact is more pronounced for Abu Dhabi specifically, contradicting the results of Shahzad et al. (2017) who report no significant impact. This contradiction is probably due to Shahzad et al.’s (2017) use of weekly data, while we use daily data. This points to the importance of using daily data to uncover significant quantile dependence that might not be shown by weekly data. This finding is further highlighted by the decrease in the impact at higher lags. Accordingly, the sovereign CDS market in some MENA countries and Emirates absorbs information quickly. This result also indicates that having a large wealth fund and a rational fiscal policy did not prevent the economy of Abu Dhabi, and to a lesser extent Dubai, feeling the negative impact of oil market conditions on sovereign risk. The results suggest the need for more work, and probably more reform, to reduce the impact of oil market conditions on sovereign risk, and show that extreme negative changes in oil volatility have no impact on the CDS spread changes in Bahrain, whereas moderate and positive changes in oil volatility are followed by increases in CDS spread changes. As a robustness check, we control for the effect of stock market returns in each country under study. To this end, we follow Han et al. (2016) by applying the partial cross-quantilogram model that represents an extension of the standard cross-quantilogram model and allows us to control for intermediate events between t and t-k.12 Fig. 3 presents the results of the dependence structure of CDS spread changes on oil returns (oil volatility changes) for lag 1, after incorporating stock market returns as a state variable.13 As previously shown in Figs. 1 and 2, sovereign CDS spreads have a negative dependence on oil returns and a positive dependence on oil volatility changes across numerous quantiles. This result suggests that the dependence of CDS spreads on oil market con ditions is not affected by the state of the stock market.
5. Results and discussion We present the results of the cross-quantilogram approach of Han et al. (2016), which emerges as a suitable approach for uncovering the predictability of sovereign CDS spread changes based on oil market conditions across the various quantiles, and capturing the time-variability in the dependence structure. 5.1. Cross-quantile dependence - static results Fig. 1 presents the heat maps of the impact of oil returns on CDS spread changes at various lags (1, 2, 4 and 6) and quantiles. The hori zontal (vertical) axis presents the quantiles of oil returns (CDS spread changes). The multicoloured bar at the end of the figure helps measure the magnitude of the impact (i.e., cross dependence), which varies from dark blue (negative), to light green (insignificant), to red (positive). Generally, and in all countries under study, there is a significant negative impact of oil returns on CDS spread changes, especially for lag 1. Intui tively, the impact is more pronounced for oil exporters than oil importers. For Abu Dhabi and Bahrain specifically, extreme oil positive returns are followed by decreases in CDS spread changes the next day. Conversely, extreme oil negative returns are followed by increases in CDS spread changes the next day, especially in Abu Dhabi. For Dubai, which has a more diversified economy, next day changes in the CDS spreads are not related to today’s extreme positive or extreme negative oil returns. However, the impact of oil returns is generally negative when both oil returns and CDS spread changes are in their middle quantiles. For oil-importers, oil returns have a less clear and more mixed impact on sovereign CDS spreads than for oil-exporters, although the impact is
5.2. Time-varying results of cross-dependence – recursive-rolling window analysis In this subsection, we move the analysis of the impact of oil market conditions on the sovereign CDS spreads from a static to a time-varying 12
For more details, the reader is referred to Han et al. (2016). For each of the five countries under study, stock market index is represented by its respective USD denominated MSCI stock index. This is relevant to Abu Dhabi and Dubai where the unavailability of an MSCI stock index for each of them forces us to the UAE MSCI stock index as a control variable. 13
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Fig. 1. Heat maps of cross-dependence between oil returns and CDS spread changes.
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Fig. 2. Heat maps of cross-dependence between oil volatility and CDS spread changes Note: These figures show the cross-quantilogram in the form of heat maps. The quantile levels with no significant directional predictability are set to zero. The coloured rectangles are the predictable regions where the Box–Ljung test statistic is statistically significant. In each heat map, the horizontal axis represents the quantiles of oil volatility changes, while the vertical axis represents CDS spreads quantiles.
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setting (Uddin et al., 2019). To this end, we apply a recursive-rolling window framework14 to selected quantiles (0.05, 0.5, and 0.95) for both oil returns (oil volatility changes) and CDS spread changes, for lag 1, using stock market returns as a control variable. For reliable results, our initial sample includes 500 observations (approx. 2 years) and the length of the sample window increases by 22 days (approx. 1 month) for the estimation in each step. This recursive estimation process continues until the last observation of the sample is reached. The results are shown in Figs. 4 and 5, in which the horizontal axis shows the time period and the vertical axis shows the quantilogram correlation. The left, middle and right panels of Fig. 4 (Fig. 5) show respectively 0.05, 0.5 and 0.95 quantiles for oil returns (oil volatility changes), while red, blue and green represent 0.05, 0.5, and 0.95 quantiles of CDS spread changes. Generally, the results show that the impact decreases towards the end of the sample period. A broad look at the plots confirm our findings based on full sample analysis reported previously that oil returns (volatility) generally have a negative (posi tive) impact on the next day CDS spread changes. However, these im pacts are subject to change over time, and noticeably the impact decreases towards the end of sample period. In the case of oil returns, we find that the negative impact on CDS spreads for Abu Dhabi, and Bahrain are higher during 2014–2015 period. Once again, the impact of oil volatility on CDS spreads (see Fig. 5) is positive but decreases over time. We assess the robustness of our above results using a recursive-rolling window of 1000 days. Un reported results, which are available from the authors, show quite similar findings, suggesting the robustness of our time-varying analysis to the choice of window size.
potential effects on the values of currencies and, as a result, on the level of inflation and interest rates. The findings above have implications for bankers, given that central and domestic banks in emerging countries often hold large amounts of government debt (Arslanalp and Tsuda, 2014). It is therefore recommended that banks monitor oil market conditions to avoid any potential risk arising from lower oil prices on the value of sovereign debt held by the banking system, and thus on the banks’ balance sheets. This is particularly relevant for countries such as Lebanon, where the Central Bank, and local banks, are the main holders of Lebanese debt. Secondly, the results point to an asymmetric effect that has not been re ported in previous studies (e. g, Lee et al., 2017). In the case of Abu Dhabi, the CDS spreads and oil returns relate to opposite quantiles, with low quantile oil returns negatively impacting higher quantile CDS spreads. Also, higher quantiles of oil returns have a significant negative impact on lower quantiles of CDS spreads. An opposite situation is evident in the cases of Egypt and Morocco, where lower (higher) quantiles of oil returns impact lower (higher) quantiles of CDS spreads. For Dubai and Lebanon, oil returns negatively and significantly impact the CDS spreads only in the middle quantiles. These asymmetries in the relationship between oil returns and CDS spreads can only be explored through methods that pro vide quantile specific estimates such as cross-quantilogram analysis. Such evidence of asymmetry in the dependence structure between oil prices and sovereign risk is interesting and new. It suggests that investors and port folio managers should design their investment and hedging decisions by taking into account the levels of oil prices and sovereign risk. Furthermore, investors can rebalance their international portfolios with sovereign CDS spreads of MENA countries based on the dependence structure. As for policymakers, they should account for those asymmetric effects while designing regulatory policies for the benefit of the local economy. Inter estingly, the above evidence of asymmetry has not been reported before, which opens new avenues for future research into the determinants of the asymmetry. Thirdly, sharp decreases in oil prices are related to increases in sovereign risk, especially in oil-exporters. For example, Abu Dhabi shows no resilience to extreme negative changes in crude oil prices although it has large foreign reserves and a sovereign wealth fund (Mohaddes and Raissi, 2017), which does not seem to act as a cushion absorbing the impact of lower oil prices on sovereign credit risk. However, more research is needed to examine the explicit role of wealth funds and other foreign reserves within the oil-CDS nexus. Fourthly, changes in oil uncertainty is positively related to changes in sovereign risk of oil-exporters, and the relationship seems to hold for most oil-importers. This finding suggests that oil-implied volatility contains useful information relevant for the sovereign risk of both oil exporters and importers. Such information can be used by investors, policymakers and bankers in policy decision-making, especially during periods of high uncertainty such as the oil-crash of 2014–2016. It also suggests the need of economic actors to have a close look to periods of stunning fall in oil prices when formulating their policies. Fifthly, the re sults show a time-varying dependence structure that is not reported in previous studies. Specifically, the negative (positive) impacts of oil returns (oil volatility) on the next day CDS spread changes are subject to change over time, and noticeably the impacts decrease towards the end of the sample period. This finding suggests the need for investors and policy makers to adopt time-varying models. Our analyses draw a more nuanced picture of the relation between oil price and sovereign risk in the quantiles, and thereby provide novel empirical evidence of how different levels of oil price fluctuation relate to sovereign risk in MENA. Importantly, the revealed tail effect could not be uncovered by a linear model, suggesting the suitability of our econometric models. Overall, the cross-quantile im pacts of oil returns and oil volatility changes on CDS spread changes are a very short-term phenomenon, as shown by the inverse relationship be tween lag lengths and the magnitude and significance of dependence. Finally, our analyses show that stock market returns do not moderate the dependence structure. Future research could consider the portfolio implications of adding crude oil and oil implied volatility indices to a portfolio of sovereign CDS products. Another extension could involve the study of the impact of oil
6. Conclusions and policy implications Motivated by press releases that usually relate movements in sov ereign CDS spreads to oil market conditions, and by the rationale that deteriorating macroeconomic fundamentals and fiscal sustainability can increase the probability of sovereign debt default (Hilscher and Nos busch, 2010), we add to the limited empirical evidence regarding the oil-CDS nexus in MENA oil-exporters and importers. Unlike previous work (Lee et al., 2017; Shahzad et al., 2017), we provide novel evidence on the static and time-varying dependence of CDS spread changes on oil returns and oil volatility changes. Using the cross-quantilogram approach of Han et al. (2016) in a sample period from February 14, 2011 to November 23, 2018, our main findings are summarized as follows. Firstly, the relationship between oil prices and sovereign risk is negative for oil-exporters, concurring with the findings of Lee et al. (2017). However, it is less pronounced for some oil-importers. It could be that MENA oil-importers benefit from higher oil prices through different channels, as explained in the introduction section. The result might also be driven by our reliance on higher frequency data and/or a different econometric method. These results are useful for investors and policymakers in preparation for any increase in sovereign credit risk. Specifically, investors can use information from the crude oil market to predict changes in sovereign risk in MENA countries and take the necessary decisions regarding risk management. Thereby, they can profit from more refined betting on the direction in which changes in sovereign CDS spreads will move as well as form a hedging strategy to reduce the risk emanating from sovereign risk. Policymakers can adjust sovereign debt policy and the timing of debt issuance. For example, they can lock attractive borrowing rates when the state of crude oil prices is favorable, and, therefore, use the potential savings from the lower cost of borrowing to spur governmental spending and investment in the real economy. Further implications include
14 We prefer recursive-rolling window analysis over simple rolling window analysis because cross-quantilogram estimations are sensitive to the number of observations (the higher, the better). However, rolling window results, which are available on request, provide similar findings.
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Fig. 3. Heat maps of cross-dependence between oil returns (oil volatility changes) and CDS spread changes – with stock market returns as state variable and lag ¼ 1 Note: see notes to Figs. 1 and 2.
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Fig. 4. Recursive-rolling window cross-quantilogram between oil returns and CDS spread changes – with stock market returns as a state variable Notes: The vertical (horizontal) axis represents the quantile hits for the CDS spreads (time). The starting year of the recursive-rolling window is marked on the horizontal axis. The left, middle and right columns, respectively, show the 5%, 50%, and 95% quantiles for oil returns, while the red, blue, and green lines represent the 5%, 50%, and 95% quantiles for the CDS spreads. Lag p ¼ 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
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Fig. 5. Recursive-rolling window cross-quantilogram between daily oil volatility and CDS spread changes – with stock market returns as a state variable. Note: The vertical (horizontal) axis represents the quantile hits for the CDS spreads (time). The starting year of the recursive-rolling window is marked on the horizontal axis. The left, middle and right columns, respectively, show the 5%, 50%, and 95% quantiles for oil volatility changes, while the red, blue, and green lines represent the 5%, 50%, and 95% quantiles for the CDS spreads. Lag p ¼ 1. . (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
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price and volatility on the CDS markets of banking entities in MENA oil exporters. On the methodological front, future research could take inspi ration from the innovative approach of Sim (2016) while studying the oil-CDS nexus.
Acknowledgements The first and the second authors gratefully acknowledge the financial support by the CNRS-L/USEK.
Appendix
Figure A1. Time evaluation of the data level series 12
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References
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Contents lists available at ScienceDirect
Energy Policy journal homepage: http://www.elsevier.com/locate/enpol
Oil market conditions and sovereign risk in MENA oil exporters and importers Elie Bouri a, *, Imad Kachacha a, David Roubaud b a b
USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon Montpellier Business School, Montpellier, France
A R T I C L E I N F O
A B S T R A C T
JEL classification: C22 G10
We analyze for the first time how various levels of oil returns and oil volatility changes affect sovereign risk in static and time-varying settings. Empirical analyses involve daily data from February 14, 2011 to November 23, 2018 covering a sample of MENA oil-exporters and importers. The results from a quantile-based approach show that the sovereign risk of MENA oil-exporters and importers is directionally predicted by shocks in oil prices and oil volatility, especially during the oil crash of 2014–2016. Overall, the impact of oil returns and volatility changes occur in a very short time span, that is within one day lag, and the quantile specific reactions of sov ereign risk spreads are time varying. The impact of oil returns is asymmetric across quantiles. The results hold when we control for stock market returns. The findings have implications for investors in terms of portfolio and risk management. Importantly, the findings are useful to policymakers for sovereign risk management decisions, the cost of sovereign borrowing, and the market timing of debt issuance. Finally, the findings matter to bankers given that central and domestic banks hold large amounts of sovereign debt, which makes banking systems particularly exposed to their own sovereign stress.
Keywords: Crude oil Sovereign credit risk MENA Oil exporters and importers Dynamic conditional correlation Quantile dependence
1. Introduction Crude oil is a strategic energy commodity that plays a pivotal role in the global macroeconomic and financial scene. The economic and financial impacts of oil market shocks are well known (Montoro, 2012; Oladosu et al., 2018),1 and oil price shocks generally have an influence on production costs, price levels, and consumption dynamics (Hamilton, 2009). In energy-exporting countries especially, oil price fluctuations are extremely important as they can shape energy export revenues, government expenditure, and fiscal balances (e.g., El Anshasy and Bradley, 2012). Breunig and Chia (2015) show that the sovereign rating of oil-producing countries can be adversely affected by an unexpected decline in crude oil prices. Lower oil prices weaken the fiscal position of oil-exporters, and price volatility can induce macroeconomic
fluctuations and lead to a deterioration in financing conditions, making oil-exporting countries more vulnerable to credit events such as default. The collapse in world oil prices from a high of $115 in June 2014 to under $35 in February 2016, and the subsequent weak recovery of the oil market, fuelled many press articles highlighting the potential impact of lower oil prices on the sovereign credit default swap (CDS) spreads of oil exporters.2 These press articles argue that lower oil prices shrank oil revenues, pushing up sovereign yields and destabilizing fiscal sustain ability. This is relevant to many MENA countries, where high crude oil prices are an important catalyst for government spending, economic growth, financial prospects, and budget surpluses.3 While some empir ical evidence shows the importance of (1) oil prices for large developed economies (Lee et al., 2017) and (2) oil volatility for oil-exporting countries (Shahzad et al., 2017), less is known about the impact of oil
* Corresponding author. E-mail addresses: [email protected] (E. Bouri), [email protected] (I. Kachacha), [email protected] (D. Roubaud). 1 The impact of crude oil market conditions on equities (Arouri et al., 2011; Bouri, 2015), currencies (Basher et al., 2012), gold (Bedoui et al., 2018), and agricultural commodities (Nazlioglu, 2011; Ji et al., 2018) is well established in the academic literature. 2 The World Bank reports highlights the positive effect of the rebound in crude oil prices on the sovereign risk of oil-exporters such Russia and Saudi Arabia. http s://www.worldbank.org/content/dam/Worldbank/GEP/GEP2015a/pdfs/GEP2015a_chapter4_report_oil.pdf. 3 In some MENA oil-exporters, such as Gulf states, crude oil accounts for more than 30% of GDP and 80% of government revenue. Non-oil output also depends on energy revenues through government spending on capital projects and salaries. In Saudi Arabi, the petroleum sector represents around 45% of GDP and 90% of export earnings (https://www.weforum.org/agenda/2016/05/which-economies-are-most-reliant-on-oil/). Lower oil prices led the Saudi government budget to have a deficit of more than 78 billion US dollars in 2017. https://doi.org/10.1016/j.enpol.2019.111073 Received 26 January 2019; Received in revised form 8 October 2019; Accepted 25 October 2019 0301-4215/© 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Elie Bouri, Energy Policy, https://doi.org/10.1016/j.enpol.2019.111073
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price and oil volatility on the sovereign CDS spreads of MENA oil-exporters and importers. In fact, MENA oil importers are extremely understudied, and can be affected by movements in crude oil prices in a different way to major European and Asian oil-importing countries.4 Importantly, there is a lack of empirical evidence regarding the impact at extreme tails of distribution and whether the impact of large down ward oil price changes has the same magnitude as that of large upward price changes.5 Furthermore, previous studies often neglect the time-variability in the impact of oil returns and oil implied volatility on the sovereign credit risk. These areas are where we aim to contribute. While the current paper is related to previous studies (e.g., Lee et al., 2017; Shahzad et al., 2017), it differs in several respects. Firstly, it fo cuses on the oil-CDS nexus at various quantiles of both oil and CDS markets, considering both the return and volatility6 of crude oil prices. This represents a nice extension to prior studies that overlook the quantile dependence (Lee et al., 2017) or only consider the impact of oil volatility (Shahzad et al., 2017). Secondly, as opposed to prior studies, our data sample is at daily frequency, which is important in order to better capture the dynamics of the relationships among the oil and CDS markets, rather than using a weekly (Shahzad et al., 2017) or monthly (Lee et al., 2017) frequency. Interestingly, the full sample period is February 14, 2011 to November 23, 2018, covering periods of stable and sharp decline in crude oil prices as well as moderate and high volatility periods in the crude oil market (e.g., the oil price collapse of June 2014–February 2016), allowing us to determine how oil market condi tions impact the various quantiles of sovereign risk. Thirdly, this paper uncovers potential asymmetric effects and time-variability in the quantile dependence through the application of a recursive-rolling window approach which captures the dependence structure in a time-varying setting. As such, it accounts for the potential presence of structural breaks in the relationship between the variables under study. Our results are consistent with the models that highlight the importance of crude oil price in driving sovereign risk (Breunig and Chia, 2015; Fonseca et al., 2016; Liu et al., 2016), although they focus on the tails of the distribution of both oil price returns (oil implied vola tility) and sovereign risk spreads, which adds to the related literature (e. g., Lee et al., 2017; Shahzad et al., 2017). The rest of the paper is structured in five sections. The next section reviews the most relevant literature. Section 3 describes the econometric framework. Section 4 presents the data. Section 5 reports and discusses the empirical results. Section 6 concludes and offers policy implications.
in shaping the sovereign credit risk of developing economics (Longstaff et al., 2011; Amstad et al., 2016; Hibbert and Pavlova, 2017). Given that energy and non-energy commodities represent important global risk fac tors (Koutmos, 2019), a major strand of research focuses on the impact of commodity prices on the sovereign bonds of emerging and developing countries. Hilscher and Nosbusch (2010) consider the sovereign bond yields of emerging markets, and highlight the vulnerability of commodity exporters to external shocks. Similarly, Sun et al. (2011) point out the importance of commodity prices as a driver of the bond spreads of emerging markets. The authors document an inverse relationship between commodity price and sovereign bond spread. Bouri et al. (2017) consider the case of emerging and frontier economies. They apply a Granger cau sality in variance approach and provide evidence that the volatility of commodity and energy prices predict the volatility of sovereign spread risk, indicating that the predictability cannot always be explained by the country’s level of commodity and energy dependence. Other studies consider crude oil price as a determinant of sovereign bond spread and credit risk. Alexandre and de Benoist (2010) find that oil prices impact country bond risk premiums in emerging countries, differentiating between oil exporters and oil importers. They reveal that oil price return and volatility can particularly shape the sovereign bond spreads of oil-exporting countries such as Russia. Sharma and Thur aisamy (2013) consider the impact of oil price uncertainty on sovereign credit risk. Using a sample of Asian Pacific countries, they show that the volatility of crude oil prices can be used as a predictor of sovereign credit risk. Hooper (2015) documents evidence of an association between oil and gas reserves and sovereign spreads in emerging oil-exporting countries. Wegener et al. (2016) study the case of nine countries (Brazil, Malaysia, Norway, Qatar, Russia, Saudi Arabia, UK, USA, and Venezuela) within a bivariate GARCH model. They report evidence that positive oil price shocks reduce the sovereign credit risk of oil-producing countries. Breunig and Chia (2015) consider the effects of high oil prices, in the period 2003–2008, on the ratings of sovereign oil-exporting countries. They point to the potential impact of unanticipated de creases in crude oil prices on sovereign ratings. Taking a different perspective, Liu et al. (2016) focus on the statistical properties of the country risk of oil-exporting countries. They find that oil price volatility can accentuate the volatility of country risk ratings. A major sign of crude oil’s impact can be seen in the sovereign credit default swap (CDS) that offers investors protection against default risk.7 As indicated by Fonseca et al. (2016), the evolution of financial markets has led to the rise of the CDS market as an alternative measure of credit risk, replacing the measure of bond yield spread. Koutmos (2019) argues that CDS spreads reflect default probabilities and investors’ perceptions of credit risk. They are bilateral contracts that allow the buyer to pay a premium in basis points as protection against losses in case of a credit event on sovereign debt. Interestingly, the CDS market has experienced substantial growth in volume and size during the last two decades, allowing market participants to use the insurance policy against credit default in trading, hedging, and speculation activities (Koutmos, 2019). Specifically, sovereign CDS spreads are important elements of policy discussions due to their ability to reflect changes in the credit quality of a country and, thereby, the level of risk in the financial system. Accord ingly, in addition to portfolio managers and speculators, policymakers and bankers are interested in understanding the factors that affect variation in sovereign CDS spreads, such as crude oil prices. Fonseca et al. (2016) study the detriments of CDS spreads in the energy market. They indicate that volatility and jumps in crude oil prices have explanatory power for the stock market returns of energy companies. Using weekly data and a bootstrap rolling window approach, Shahzad
2. Related studies The empirical literature highlights the importance of global risk factors
4 Higher oil prices upsurge the oil import bill and often represent a financial burden for oil-importers that usually pay subsidies on fuel consumption. Conversely, lower in oil price reduces oil import bill to oil-importers, provoking net savings that improve their fiscal position and thereby the quality of their sovereign debt. However, the issue might be less straightforward and more complex in the MENA region. As argued by Bouri (2015), economic and financial conditions in MENA oil importers can be improved by higher oil prices, through various channels including higher remittances from oil rich Gulf countries, more investment in property and non-property markets, higher de posits in banks, and higher tourism receipts. For example, higher oil prices can have positive effects on stock market returns, especially in the real estate and banking sectors (Bouri, 2015). This suggests that the burden of subsidies might be offset by the overall economic benefits of higher oil prices. 5 The empirical literature often treats upside and downside movements symmetrically. 6 Specifically, the volatility of crude oil prices is measured by the forwardlooking measure of implied volatility from the Chicago Board Options Ex change (CBOE), called the OVX, which has the advantage of capturing future expectations of market participants in the crude oil market (Maghyereh et al., 2016).
7 The size of the CDS market has grown steadily in the last two decades. The reader can refer to the Bank for International Settlements for statistics regarding the size and importance of these financial credit derivatives. https://www.bis. org/publ/otc_hy1711.htm.
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et al. (2017) find evidence that the volatility of crude oil prices can predict the sovereign credit risk of several oil-exporters. However, they conduct a mean-based rolling window causality test, which overlooks potential time variation in quantile dependence. Furthermore, they limit their analyses to the dependence between oil volatility and sovereign risk, ignoring the impact of oil returns on sovereign risk. Another related study is that conducted by Lee et al. (2017) who use monthly data and apply a structural vector autoregression model. They show that oil price shocks lead to a reduction in country risk for net oil exporters, while the same heightens the country risk for net oil importers. However, Lee et al. (2017) overlook the impact of oil implied volatility on sovereign risk. Price dynamics among financial variables can often be better captured by higher frequency data such as daily data, and models that account for the entire return distribution (Uddin et al., 2019). Considering the above survey of the related literature, especially in regard to the works of Lee et al. (2017) and Shahzad et al. (2017), we extend our understanding of the effect of crude oil prices and volatility on sovereign credit risk in MENA oil exporters and importers. To do this, we examine the daily average and cross-dependence at various quantiles of both oil returns (oil implied volatility changes) and CDS spread changes in static and time-varying settings, while controlling for stock market returns. This is particularly relevant for MENA oil-exporting countries as it is important for energy security and regional social-political stability. Furthermore, understanding the impact of oil price returns and volatility on sovereign CDS spreads of MENA countries has implications for poli cymakers, investors, speculators, and academics. It can have important implications for the debt policies of countries, the volatility of debt, and the market timing of debt issuance. For example, a country can decide to borrow in a favorable interest rate environment, i.e. before an expected plunge in crude oil prices makes the borrowing more costly. Investors and speculators can use any evidence of significant effects when making in vestment and trading decisions. Bankers are interested in any evidence of the association between oil market conditions and sovereign risk because central and domestic banks in emerging economies generally hold large amounts of government debt (Arslanalp and Tsuda, 2014). Such a concentrated exposure of the banks limits the ability of the banking system to absorb shocks in the event of sovereign stress (Angeloni and Wolff, 2012), and, in the worst case scenario, impairs banks’ balance sheets and threatens the solvency of the banking system. This suggests the need for central and domestic banks to monitor any potential factors capable of elevating sovereign risk, including crude oil market condi tions, with the aim of maintaining banking and financial system stability. Academics, among others, can build on their analyses when modelling the structure of dependence between oil and CDS markets.
Table 1 Fuel exports as percentage of merchandise exports. United Arab Emirates Bahrain Egypt Lebanon Morocco
2010
2011
2012
2013
2014
2015
2016
n/a
n/a
53.52
51.52
42.50
n/a
20.23
74.35 29.83 0.17 1.07
71.84 30.91 0.14 2.61
64.15 31.54 2.94 3.97
59.01 26.93 9.97 5.03
66.83 23.88 1.10 3.29
58.14 18.42 n/a 1.48
55.03 16.35 0.61 0.86
Source: World Development Indicators, World Bank. n/a denoted not available. Disaggregated data on the fuel exports as percentage of merchandise exports are not available for Abu Dhabi and Dubai, which leads us to report the aggregated data for the United Arab Emirates.
3.1. Quantile dependence using the cross-quantilogram approach of Han et al. (2016) The cross-quantilogram approach of Han et al. (2016) emerges as a suitable approach to study the cross-quantile dependence between the variables, and capture asymmetries in the dependence structure. It con structs estimates of the effect the quantiles of oil returns (oil implied volatility changes) have on the quantiles of the CDS spread changes, providing a complete picture of the dependence structure. It offers flexi bility in estimating the quantile lead-lag relation between the variables at different lags. The output of the cross-quantilogram approach is repre sented in the form of a heat map of lag lengths. The recursive-rolling window approach is used to capture any potential time variation in the cross-dependence. The cross-quantilogram approach of Han et al. (2016) requires that the variables under study follow a stationary stochastic process. Consider two stationary time series fxi;t ; t ε Zg, i ¼ 1, 2, where x1;t de notes the oil returns (oil volatility changes) and x2;t denotes the country CDS spread changes. The density and distribution functions of the time series xi;t are labelled fi ð⋅Þ and Fi ð⋅Þ. The quantile of xi;t is represented as qi ð∝i Þ ¼ inffv : Fi ðvÞ � αi g for αi ε ð0; 1Þ; and the expression of two-dimensional series of quantiles are represented by ðq1 ð∝1 Þq2 ð∝2 ÞÞτ , for α � ð∝1 ; ∝2 Þτ . The cross-quantilogram for α-quantile with k lags can be written as: E½Ψ ðx q ðα ÞÞΨ ðx q ðα ÞÞ� ffiffiffiffiffiffiffiffiffiffiffiffiffi1ffiffiffiffiffiffiffi1ffiffiffiffiffiqαffi2ffiffiffiffiffiffi2;tffiffiffiffiffikffiffiffiffiffiffiffiffiffiffiffiffi2ffiffiffiffiffiffi2ffiffiffiffiffiffiffiffiffiffiffiffi ρα ðkÞ ¼ qffiffiffiffiffiffiffiffiffiαffiffi1ffiffiffiffiffiffi1;t 2 2 E½Ψ α1 ðx1;t q1 ðα1 ÞÞ� E½Ψ α2 ðx2;t q2 ðα2 ÞÞ�
(1)
where, k is the number of lead-lag periods to time t, Ψ a ðμÞ � 1½μ < 0�, 1ð ⋅Þ denotes the indicator function and 1½xi;t � qi ðαi Þ� is the quantile exceedance process. At different quantiles, the serial dependence be tween the two time series is captured through ρα ðkÞ. In the present framework, ρα ð1Þ measures the cross-correlation be tween oil returns - and alternatively oil volatility changes – below or above the quantile qoil returns ðαoil returns Þ at time t and the CDS spread being above or below the quantile qCDS spread changes ðαCDS spread changes Þ at time t. ρα ð1Þ ¼ 0 indicates that oil returns being below or above the quantile qoil returns ðαoil returns Þ at time t does not contain useful information for pre dicting whether the country CDS spread changes are below or above the quantile qcds ðαcds Þ during the next trading day (t þ 1). On the contrary, ρα ð1Þ 6¼ 0 indicates a one-day directional predictability from oil returns to the country CDS spread changes at α ¼ αoil returns ðαCDS spread changes Þ. The sample counterpart of the cross-quantilogram is estimated by: PT Ψ α1 ðx1;t q1 ðα1 ÞÞΨ α2 ðx2; t k b q ðα2 ÞÞ b ffiffiffiffiffiffiffiffiffiffiffiffit¼kþ1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiqffiP ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ρ α ðkÞ ¼ qffiP (2) T T 2 2 Ψ ðx b q ð α ÞÞ Ψ ðx b q 2 ðα2 ÞÞ 1 1 t¼kþ1 α1 1; t t¼kþ1 α2 2; t k
3. Models To analyze the dependence structure between crude oil and sover eign CDS markets, we apply the cross-quantilogram approach of Han et al. (2016), which is based on the cross-quantiles of both dependent and independent variables. Interestingly, this approach allows us to examine average and tail dependence and control explanatory variables. We also conduct a recursive analysis of the quantile dependence to examine the time variation in the dependence after controlling for stock returns.8
where, b q i ðαi Þ represents the unconditional sample quantile of xi;t ; as defined by Han et al. (2016). For p > 1, Han et al. (2016) suggest a quantile version of the Ljung-Box-Pierce statistic to test H0 : ρα ðkÞ ¼ 0 for all k, 1 � k � p against the alternative hypothesis H1 : ρα ðkÞ 6¼
8
We could have also used the quantile-on-quantile approach of Sim and Zhou et al. (2015), however, the cross-quantilogram approach allows us to account more easily for explanatory variables (i.e. stock returns) in the modelling of dependence. The recursive analysis of the cross-quantilogram allows us to ac count for the presence of structural breaks in the dependence. 3
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Table 2 Summary statistics and stationarity tests. Brent oil Oil OVX Abu Dhabi Dubai Bahrain Egypt Lebanon Morocco
Mean
Maximum
0.029 0.031 0.022 0.172 0.014 0.091 0.265 0.029
9.896 42.497 16.020 48.830 180.550 96.930 77.100 65.000
Minimum 8.245 43.991 15.980 29.570 189.010 141.520 82.850 35.230
Std. Dev.
Skewness
Kurtosis
1.889 4.874 2.275 6.112 9.734 12.166 10.464 5.425
0.206 0.987 0.582 0.783 0.399 0.353 0.044 0.483
5.938 14.343 10.545 11.566 135.060 23.467 13.737 20.358
Notes: This table presents summary statistics of daily data: crude oil returns, changes in oil volatility, changes in CDS spreads. The sample period is February 14, 2011 to November 23, 2018.
Dhabi, Dubai, and Bahrain) and three oil-importers (Lebanon, Egypt, and Morocco) within the Arab MENA region10. The crude oil OVX is computed by the CBOE, following the computation method of the S&P 500 implied volatility index, the VIX. It reflects market expectations of the 30-day implied volatility of oil prices, which makes it a powerful gauge of the forward-looking risk in the crude oil market. The CDS data consists of sovereign CDS mid-spreads of contracts with five years to maturity, which is typically the most liquid contract (Fonseca et al., 2016). In fact, CDS spread is expressed in basis points (bps) and quoted in annual percentage bps. The sample period spans February 14, 2011 to November 23, 2018, implying 1982 daily observations, chosen to be common across the oil and CDS markets. The beginning of the sample period is dictated by the lack of liquidity in some CDS spreads before February 14, 2011. The sample period is made interesting by the sharp decrease (increase) in the price (volatility) of crude oil during June 30, 2014 and February 29, 2016 and the concurrent movements in the CDS spreads of most oil exporters. All daily data series were extracted from Bloomberg Terminal. Figure A1 in the Appendix presents the evolution of crude oil prices, oil implied volatility, and sovereign CDS spread over the sample period. Interestingly, crude oil prices generally exhibit large fluctuations, especially around June 2014–February 2016 which corresponds to the meltdown of the crude market. Crude oil implied volatility spikes, signaling higher risk in the crude oil market. Furthermore, many sov ereign CDS spreads of oil exporters increase, reflecting the instability and decline of external revenues from oil exportation. However, an in crease in CDS spreads, particularly for Lebanon, is observed towards the end of the sample period, possibly driven by political events. Table 1 provides the percentage of fuel exports of merchandise ex ports. The largest percentages are for Bahrain, where there is a declining trend. A smaller percentage is reported for the United Arab Emirates (which encompasses Abu Dhabi and Dubai) that experienced more decline in fuel exports as a percentage of merchandise, to around 20%, which makes it the least dependent on petroleum revenue exports of the oil-exporters under study. Interestingly, in Egypt the percentage declined from around 30% in 2010 to less than 17% in 2016. Overall, Table 1 points to the possibility of oil-exporting countries with high percentage fuel exports co-moving with the market conditions of crude oil. To conduct empirical analyses, we use logarithmic-returns of crude oil prices, changes in oil implied volatility, and changes in sovereign CDS spreads.11 However, before we establish the econometric frame work, we present summary statistics for logarithmic-returns for crude oil
ðpÞ
b , 0 for at least one k, 1 � k � p, under the portmanteau test statistic, Q α for directional predictability from one variable to another for up to p lags over the quantile pair α ¼ ðα1 ; α2 Þ. Pp 2 ρ α ðkÞ b ðpÞ ¼ TðT þ 2Þ k¼1 b Q (3) α T k Given that the asymptotic distribution of cross-quantilogram con tains noise under H0 , Han et al. (2016) use the stationary bootstrap9 of Politis and Romano (1994) to approximate the distribution of the testing statistic under the null hypothesis that can then be used for statistical inference. 4. Data description The data set is at daily frequency. It includes Brent crude oil spot prices, the crude oil OVX, and CDS spreads for three oil-exporters (Abu Table 3 Stationarity tests. Panel A: Levels series Trend and intercept ADF Brent oil Oil OVX Abu Dhabi Dubai Bahrain Egypt Lebanon Morocco
1.675 2.800 2.618 2.670 2.491 2.361 2.086 2.568
Lag length for the ADF test
PP 1.777 2.651 2.406 2.631 2.377 2.498 1.300 2.630
0 3 2 0 2 1 0 2
Panel B: Changes and returns series Trend and intercept ADF PP
Lag length for the ADF test
Brent oil Oil OVX Abu Dhabi Dubai Bahrain Egypt Lebanon Morocco
0 2 1 0 1 1 0 4
42.979*** 28.661*** 28.541*** 45.781*** 37.226*** 31.633*** 47.711*** 25.477***
43.098*** 48.446*** 44.138*** 45.783*** 47.812*** 51.619*** 47.693*** 42.775***
Notes: Panel A presents the statistics of ADF and PP tests that are applied on oil prices, CDS spreads, and oil OVX levels. Panel B presents the statistics of the ADF and PP tests that are applied to the CDS changes, oil price returns, and oil OVX changes. In both panels, the ADF and PP tests are conducted with trend and intercept. The lag length is selected based on the Schwarz information criterion. *** indicates significance at the 1% level. The sample period is February 14, 2011 to November 23, 2018.
9
10 The choice of Bahrain, Egypt, Lebanon, Morocco, and two largest emirates of the UAE (Abu Dhabi and Dubai), is determined by the availability of sov ereign CDS. Unfortunately, the daily CDS spreads of Saudi Arabia and Qatar are very illiquid. 11 Since both CDS spreads and oil implied volatility are measured in per centage points or basis points, taking ‘ordinary’ first differences should be sufficient.
In this study, we use 1000 bootstrap iterations for statistical inference. 4
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Table 4 Unconditional correlation matrix. Brent oil Oil OVX Abu Dhabi Dubai Bahrain Egypt Lebanon Morocco
Brent oil
Oil OVX
Abu Dhabi
Dubai
Bahrain
Egypt
Lebanon
Morocco
1.000 0.292 0.175 0.173 0.140 0.029 0.053 0.081
1.000 0.174 0.200 0.099 0.017 0.078 0.118
1.000 0.557 0.254 0.152 0.191 0.246
1.000 0.307 0.187 0.172 0.272
1.000 0.117 0.142 0.199
1.000 0.131 0.179
1.000 0.108
1.000
Notes: This table provides pairwise Pearson correlation coefficients across the variables under study. The sample period is February 14, 2011 to November 23, 2018.
prices, changes in oil implied volatility, and changes in sovereign CDS spreads (see Table 2). The highest mean CDS spread is for Lebanon, whereas the highest standard deviation is for Egypt. By contrast, most oil exporters have a negative mean CDS. Crude oil price has a negative mean return, whereas oil implied volatility changes have a positive mean value. Generally, CDS spread changes have higher values for skewness and kurtosis than crude oil returns. We also apply two unit root tests, the augmented Dickey-Fuller (ADF) and Phillips Perron (PP), as described in Dickey and Fuller (1979) and Phillips and Perron (1988), respectively. The results are reported in Table 3. Based on the CDS changes, oil price returns and oil OVX changes, all series are stationary, as confirmed by the ADF and PP test statistics (see Table 3 Panel B). Note that those two tests are also applied to the levels, but the results show no evidence of stationarity for all level series (see Table 3 Panel A). Pairwise Pearson correlation coefficients across the variables (CDS changes, oil price returns, and oil OVX changes) are given in Table 4. Overall, the correlation is negative between crude oil and CDS markets, whereas it is positive between oil implied volatility and CDS markets. Specifically, more negative correlations are shown for oil exporters as compared to oil importers. The same is true for the correlation between oil implied volatility and CDS markets, although the correlation is positive.
generally insignificant or negative. This might stem from the structure of these oil-importers and their close ties with rich oil-exporters that make their economics, and thereby their public finance, affected positively by the presence of higher oil prices. This evidence partially concords with previous findings highlighting the positive effect of higher oil prices on stock market returns of MENA oil-importers such as Lebanon, Jordan and Morocco (Bouri, 2015). The results from Fig. 2 show evidence of a significant positive impact of oil volatility changes on CDS spread changes for both oil-exporters and importers, especially for lag 1. This is observed in various quan tiles, especially the middle and upper quantiles of oil volatility changes and CDS spread changes. The impact is more pronounced for Abu Dhabi specifically, contradicting the results of Shahzad et al. (2017) who report no significant impact. This contradiction is probably due to Shahzad et al.’s (2017) use of weekly data, while we use daily data. This points to the importance of using daily data to uncover significant quantile dependence that might not be shown by weekly data. This finding is further highlighted by the decrease in the impact at higher lags. Accordingly, the sovereign CDS market in some MENA countries and Emirates absorbs information quickly. This result also indicates that having a large wealth fund and a rational fiscal policy did not prevent the economy of Abu Dhabi, and to a lesser extent Dubai, feeling the negative impact of oil market conditions on sovereign risk. The results suggest the need for more work, and probably more reform, to reduce the impact of oil market conditions on sovereign risk, and show that extreme negative changes in oil volatility have no impact on the CDS spread changes in Bahrain, whereas moderate and positive changes in oil volatility are followed by increases in CDS spread changes. As a robustness check, we control for the effect of stock market returns in each country under study. To this end, we follow Han et al. (2016) by applying the partial cross-quantilogram model that represents an extension of the standard cross-quantilogram model and allows us to control for intermediate events between t and t-k.12 Fig. 3 presents the results of the dependence structure of CDS spread changes on oil returns (oil volatility changes) for lag 1, after incorporating stock market returns as a state variable.13 As previously shown in Figs. 1 and 2, sovereign CDS spreads have a negative dependence on oil returns and a positive dependence on oil volatility changes across numerous quantiles. This result suggests that the dependence of CDS spreads on oil market con ditions is not affected by the state of the stock market.
5. Results and discussion We present the results of the cross-quantilogram approach of Han et al. (2016), which emerges as a suitable approach for uncovering the predictability of sovereign CDS spread changes based on oil market conditions across the various quantiles, and capturing the time-variability in the dependence structure. 5.1. Cross-quantile dependence - static results Fig. 1 presents the heat maps of the impact of oil returns on CDS spread changes at various lags (1, 2, 4 and 6) and quantiles. The hori zontal (vertical) axis presents the quantiles of oil returns (CDS spread changes). The multicoloured bar at the end of the figure helps measure the magnitude of the impact (i.e., cross dependence), which varies from dark blue (negative), to light green (insignificant), to red (positive). Generally, and in all countries under study, there is a significant negative impact of oil returns on CDS spread changes, especially for lag 1. Intui tively, the impact is more pronounced for oil exporters than oil importers. For Abu Dhabi and Bahrain specifically, extreme oil positive returns are followed by decreases in CDS spread changes the next day. Conversely, extreme oil negative returns are followed by increases in CDS spread changes the next day, especially in Abu Dhabi. For Dubai, which has a more diversified economy, next day changes in the CDS spreads are not related to today’s extreme positive or extreme negative oil returns. However, the impact of oil returns is generally negative when both oil returns and CDS spread changes are in their middle quantiles. For oil-importers, oil returns have a less clear and more mixed impact on sovereign CDS spreads than for oil-exporters, although the impact is
5.2. Time-varying results of cross-dependence – recursive-rolling window analysis In this subsection, we move the analysis of the impact of oil market conditions on the sovereign CDS spreads from a static to a time-varying 12
For more details, the reader is referred to Han et al. (2016). For each of the five countries under study, stock market index is represented by its respective USD denominated MSCI stock index. This is relevant to Abu Dhabi and Dubai where the unavailability of an MSCI stock index for each of them forces us to the UAE MSCI stock index as a control variable. 13
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Fig. 1. Heat maps of cross-dependence between oil returns and CDS spread changes.
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Fig. 2. Heat maps of cross-dependence between oil volatility and CDS spread changes Note: These figures show the cross-quantilogram in the form of heat maps. The quantile levels with no significant directional predictability are set to zero. The coloured rectangles are the predictable regions where the Box–Ljung test statistic is statistically significant. In each heat map, the horizontal axis represents the quantiles of oil volatility changes, while the vertical axis represents CDS spreads quantiles.
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setting (Uddin et al., 2019). To this end, we apply a recursive-rolling window framework14 to selected quantiles (0.05, 0.5, and 0.95) for both oil returns (oil volatility changes) and CDS spread changes, for lag 1, using stock market returns as a control variable. For reliable results, our initial sample includes 500 observations (approx. 2 years) and the length of the sample window increases by 22 days (approx. 1 month) for the estimation in each step. This recursive estimation process continues until the last observation of the sample is reached. The results are shown in Figs. 4 and 5, in which the horizontal axis shows the time period and the vertical axis shows the quantilogram correlation. The left, middle and right panels of Fig. 4 (Fig. 5) show respectively 0.05, 0.5 and 0.95 quantiles for oil returns (oil volatility changes), while red, blue and green represent 0.05, 0.5, and 0.95 quantiles of CDS spread changes. Generally, the results show that the impact decreases towards the end of the sample period. A broad look at the plots confirm our findings based on full sample analysis reported previously that oil returns (volatility) generally have a negative (posi tive) impact on the next day CDS spread changes. However, these im pacts are subject to change over time, and noticeably the impact decreases towards the end of sample period. In the case of oil returns, we find that the negative impact on CDS spreads for Abu Dhabi, and Bahrain are higher during 2014–2015 period. Once again, the impact of oil volatility on CDS spreads (see Fig. 5) is positive but decreases over time. We assess the robustness of our above results using a recursive-rolling window of 1000 days. Un reported results, which are available from the authors, show quite similar findings, suggesting the robustness of our time-varying analysis to the choice of window size.
potential effects on the values of currencies and, as a result, on the level of inflation and interest rates. The findings above have implications for bankers, given that central and domestic banks in emerging countries often hold large amounts of government debt (Arslanalp and Tsuda, 2014). It is therefore recommended that banks monitor oil market conditions to avoid any potential risk arising from lower oil prices on the value of sovereign debt held by the banking system, and thus on the banks’ balance sheets. This is particularly relevant for countries such as Lebanon, where the Central Bank, and local banks, are the main holders of Lebanese debt. Secondly, the results point to an asymmetric effect that has not been re ported in previous studies (e. g, Lee et al., 2017). In the case of Abu Dhabi, the CDS spreads and oil returns relate to opposite quantiles, with low quantile oil returns negatively impacting higher quantile CDS spreads. Also, higher quantiles of oil returns have a significant negative impact on lower quantiles of CDS spreads. An opposite situation is evident in the cases of Egypt and Morocco, where lower (higher) quantiles of oil returns impact lower (higher) quantiles of CDS spreads. For Dubai and Lebanon, oil returns negatively and significantly impact the CDS spreads only in the middle quantiles. These asymmetries in the relationship between oil returns and CDS spreads can only be explored through methods that pro vide quantile specific estimates such as cross-quantilogram analysis. Such evidence of asymmetry in the dependence structure between oil prices and sovereign risk is interesting and new. It suggests that investors and port folio managers should design their investment and hedging decisions by taking into account the levels of oil prices and sovereign risk. Furthermore, investors can rebalance their international portfolios with sovereign CDS spreads of MENA countries based on the dependence structure. As for policymakers, they should account for those asymmetric effects while designing regulatory policies for the benefit of the local economy. Inter estingly, the above evidence of asymmetry has not been reported before, which opens new avenues for future research into the determinants of the asymmetry. Thirdly, sharp decreases in oil prices are related to increases in sovereign risk, especially in oil-exporters. For example, Abu Dhabi shows no resilience to extreme negative changes in crude oil prices although it has large foreign reserves and a sovereign wealth fund (Mohaddes and Raissi, 2017), which does not seem to act as a cushion absorbing the impact of lower oil prices on sovereign credit risk. However, more research is needed to examine the explicit role of wealth funds and other foreign reserves within the oil-CDS nexus. Fourthly, changes in oil uncertainty is positively related to changes in sovereign risk of oil-exporters, and the relationship seems to hold for most oil-importers. This finding suggests that oil-implied volatility contains useful information relevant for the sovereign risk of both oil exporters and importers. Such information can be used by investors, policymakers and bankers in policy decision-making, especially during periods of high uncertainty such as the oil-crash of 2014–2016. It also suggests the need of economic actors to have a close look to periods of stunning fall in oil prices when formulating their policies. Fifthly, the re sults show a time-varying dependence structure that is not reported in previous studies. Specifically, the negative (positive) impacts of oil returns (oil volatility) on the next day CDS spread changes are subject to change over time, and noticeably the impacts decrease towards the end of the sample period. This finding suggests the need for investors and policy makers to adopt time-varying models. Our analyses draw a more nuanced picture of the relation between oil price and sovereign risk in the quantiles, and thereby provide novel empirical evidence of how different levels of oil price fluctuation relate to sovereign risk in MENA. Importantly, the revealed tail effect could not be uncovered by a linear model, suggesting the suitability of our econometric models. Overall, the cross-quantile im pacts of oil returns and oil volatility changes on CDS spread changes are a very short-term phenomenon, as shown by the inverse relationship be tween lag lengths and the magnitude and significance of dependence. Finally, our analyses show that stock market returns do not moderate the dependence structure. Future research could consider the portfolio implications of adding crude oil and oil implied volatility indices to a portfolio of sovereign CDS products. Another extension could involve the study of the impact of oil
6. Conclusions and policy implications Motivated by press releases that usually relate movements in sov ereign CDS spreads to oil market conditions, and by the rationale that deteriorating macroeconomic fundamentals and fiscal sustainability can increase the probability of sovereign debt default (Hilscher and Nos busch, 2010), we add to the limited empirical evidence regarding the oil-CDS nexus in MENA oil-exporters and importers. Unlike previous work (Lee et al., 2017; Shahzad et al., 2017), we provide novel evidence on the static and time-varying dependence of CDS spread changes on oil returns and oil volatility changes. Using the cross-quantilogram approach of Han et al. (2016) in a sample period from February 14, 2011 to November 23, 2018, our main findings are summarized as follows. Firstly, the relationship between oil prices and sovereign risk is negative for oil-exporters, concurring with the findings of Lee et al. (2017). However, it is less pronounced for some oil-importers. It could be that MENA oil-importers benefit from higher oil prices through different channels, as explained in the introduction section. The result might also be driven by our reliance on higher frequency data and/or a different econometric method. These results are useful for investors and policymakers in preparation for any increase in sovereign credit risk. Specifically, investors can use information from the crude oil market to predict changes in sovereign risk in MENA countries and take the necessary decisions regarding risk management. Thereby, they can profit from more refined betting on the direction in which changes in sovereign CDS spreads will move as well as form a hedging strategy to reduce the risk emanating from sovereign risk. Policymakers can adjust sovereign debt policy and the timing of debt issuance. For example, they can lock attractive borrowing rates when the state of crude oil prices is favorable, and, therefore, use the potential savings from the lower cost of borrowing to spur governmental spending and investment in the real economy. Further implications include
14 We prefer recursive-rolling window analysis over simple rolling window analysis because cross-quantilogram estimations are sensitive to the number of observations (the higher, the better). However, rolling window results, which are available on request, provide similar findings.
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Fig. 3. Heat maps of cross-dependence between oil returns (oil volatility changes) and CDS spread changes – with stock market returns as state variable and lag ¼ 1 Note: see notes to Figs. 1 and 2.
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Fig. 4. Recursive-rolling window cross-quantilogram between oil returns and CDS spread changes – with stock market returns as a state variable Notes: The vertical (horizontal) axis represents the quantile hits for the CDS spreads (time). The starting year of the recursive-rolling window is marked on the horizontal axis. The left, middle and right columns, respectively, show the 5%, 50%, and 95% quantiles for oil returns, while the red, blue, and green lines represent the 5%, 50%, and 95% quantiles for the CDS spreads. Lag p ¼ 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
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Fig. 5. Recursive-rolling window cross-quantilogram between daily oil volatility and CDS spread changes – with stock market returns as a state variable. Note: The vertical (horizontal) axis represents the quantile hits for the CDS spreads (time). The starting year of the recursive-rolling window is marked on the horizontal axis. The left, middle and right columns, respectively, show the 5%, 50%, and 95% quantiles for oil volatility changes, while the red, blue, and green lines represent the 5%, 50%, and 95% quantiles for the CDS spreads. Lag p ¼ 1. . (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
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price and volatility on the CDS markets of banking entities in MENA oil exporters. On the methodological front, future research could take inspi ration from the innovative approach of Sim (2016) while studying the oil-CDS nexus.
Acknowledgements The first and the second authors gratefully acknowledge the financial support by the CNRS-L/USEK.
Appendix
Figure A1. Time evaluation of the data level series 12
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