_{1}

^{*}

This paper renders new evidence on the predictability of GCC dollar exchange rates using crude oil prices relying on the approach of Westerlund [1] [2] that accounts for salient features of the predictor. The results show the presence of significant in-sample predictability of exchange rates using crude oil prices (Brent and WTI prices) across the GCC countries. The results of forecast evaluation based on the root mean square error (RMSE), Campbell-Thompson (C-T) statistic and Diebold-Mariano (D-M) statistic are rather mixed. The superior forecast performance of the oil-based exchange rate model is highly sensitive to the choice of benchmark time-series models. We, however, conclude the overwhelming forecast performance of time-series models (namely, AR, ARMA, and ARFIMA) over our oil-based exchange rate model in predicting exchange rates across the GCC region.

Many economies, developing and developed, depend on oil for a variety of needs especially in the production of many goods and services [

Thus, the present study contributes to the literature in the following ways. First, it evaluates both the in-sample and out-of-sample forecast performance of oil-based exchange rate model relative to time series models. Second, it accounts for some important features of oil price which may have implications on its forecast performance. Consequently, the approach of Westerlund [

Following this section, the rest of the paper is structured as follows. The next section provides a brief review of the literature followed by Section 3, which presents the predictive model for estimation and the underlying forecasting procedures. Section 4 contains preliminary analyses of data features. Section 5 discusses the results while Section 6 concludes the paper.

The literature has widely taken care of the relationship between exchange rate and oil prices. It must be re-stated from the beginning of this review that most studies are impact analysis focus; very few consider exchange rate forecast through oil price, most especially in the GCC countries. The study of the relationship received much more attention for the United States dollar and currencies of industrial and developed economies, along with OPEC and other oil exporting countries. On the results discovered; the bulk of the studies attesting to the relationship between oil prices and exchange rates argue that movements in oil prices determine the value of a currency. For example, while studying real oil prices in the post Bretton Woods era and their relationship with 16 OECD countries’ real exchange rates, Chaudhuri [

The mixed nature of the various results discovered is no more news, it is important to point out from here that while some impact analyses find positive relationship, some are negative. A group claims that a rise in oil prices leads to the appreciation of the currency under study. For instance, Amano and Norden [

For the other side of coin however, another group of researchers claims that a rise in oil prices would actually worsen the value of a currency leading to its depreciation against other currencies. For instance, as claimed by Akram [

A major gap in the literature relates to the fact that virtually all the known studies involve impact (in-sample) analyses while the issue of predictability between oil price and exchange rate has received very little attention. This is the gap the study intends to fill.

We begin our methodology by specifying a bivariate single predictive model where crude oil price is hypothesized as a predictor of exchange rate:

s t = α + λ p t − 1 + ε t (1)

where s t is the log of dollar exchange rate for each of the six GCC countries’ currencies involving Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirate; and p t is the log of crude oil prices where Brent price and WTI price are used separately in the estimation process. Thus, we have two predictive models for each of the oil price series across the six GCC countries considered (details about the data utilized are provided in the section that follows). The ε t is zero mean idiosyncratic error term on exchange rate and the coefficient λ measures the relative impact of crude oil prices on exchange rate and the underlying null hypothesis of no predictability is that λ = 0 .

In order to resolve any probable endogeneity bias resulting from the correlation between p t and ε t as well as any potential persistence effect, we follow the approach of Lewellen [

s t = α + λ a d j p t − 1 + γ ( p t − ρ 0 p t − 1 ) + η t (2)

where the parameter λ a d j = λ − γ ( ρ − ρ 0 ) is the bias adjusted OLS estimator of Lewellen [

In addition, three forecast measures are used to evaluate the in-sample and out-of-sample forecasts: the root mean square error (also called the mean square error), the (C-T hereafter) test [

We focus attention on the foreign exchange markets of the six GCC countries, namely, Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirate so as to examine the sensitivity of the US dollar exchange rates in each country to changes in crude oil prices. We collect daily data on the two variables of interest, namely, the US dollar exchange rate of the six GCC countries’ currencies and crude oil prices, comprising Brent price and West Texas Intermediate (WTI) price from various sources and over different time periods for most of the countries. All the data used for analyses were sourced from the Bloomberg terminal and the scope ranges from the period of 8^{th} January, 1999 to 15^{th} September, 2017. This applies to the US dollar exchange rates of the GCC countries except Kuwait’s whose exchange rate data ranged from 8^{th} January, 1999 to 1^{st} September, 2017. Thus, the analyses are conducted based on the available data for the individual countries.

Variable | Mean | Std. | Skw. | Kurt. | J-B stat | Autocorrelation | Heteroscedasticity | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

k = 30 | k = 60 | k = 90 | k = 30 | k = 60 | k = 90 | |||||||

p t b r | 3.974 | 0.587 | −0.353 | 2.19 | 46.9*** | 118.25*** | 154.44*** | 179.03*** | 1.381* | 1.352** | 1.791*** | |

p t w t i | 3.96 | 0.533 | −0.377 | 2.234 | 46.98*** | 127.49*** | 157.15*** | 185.98*** | 1.591** | 1.194 | 1.332** | |

s t | ||||||||||||

Bahrain | 1.301 | 0.011 | -31.19 | 973.9 | 38,492,311*** | 0.0003 | 0.0019 | 0.0074 | 1.521** | 1.114 | 0.881 | |

Kuwait | −1.231 | 0.039 | −0.447 | 2.348 | 49.631 | 243.49*** | 297.44*** | 343.6*** | 12.952*** | 6.914*** | 4.591*** | |

Oman | −0.955 | 0.011 | −31.159 | 972.6 | 38,387,969*** | 0.0045 | 0.0117 | 0.0228 | 2.532*** | 1.694*** | 1.347** | |

Qatar | 1.292 | 0.01 | −30.006 | 927.1 | 34,870,761*** | 34.97 | 34.978 | 34.991 | 171,976.9*** | 81,595.8*** | 51,426.9*** | |

Saudi Arabia | 1.321 | 0.011 | −31.087 | 969.6 | 38,151,058*** | 0.0016 | 0.007 | 0.0247 | 0.266 | 0.248 | 0.375 | |

United Arab Emirate | 1.301 | 0.011 | −31.191 | 973.9 | 38,492,811*** | 0.0003 | 0.0026 | 0.0074 | 1.521** | 1.114 | 0.881 | |

Note: p t b r , p t w t i , and s t are respectively, the natural logs of Brent price, WTI price, and exchange rate. Std is standard deviation, Skw is skewness, Kurt is Kurtosis, and J-B stands for Jarque-Bera. For autocorrelation and heteroscedasticity tests, the reported values are the Ljung-Box test Q-statistics for the former and the ARCH-LM test F-statistics in the case of the latter. We consider three different lag lengths (k) of 30, 60, and 90 for robustness. The null hypothesis for the autocorrelation test is that there is no serial correlation, while the null for the ARCH-LM test is that there is no conditional heteroscedasticity. ***, ** and * imply the rejection of the null hypothesis in both cases at 1%, 5% and 10% levels of significance, respectively.

both Brent and WTI prices have approximately equal mean values. Also, dollar exchange rate has the same average values in the cases of Bahrain and the United Arab Emirate, with Saudi Arabia having the highest average dollar exchange rate and Oman having the lowest average dollar exchange rate in the group. In terms of standard deviation, Brent price is more volatile than the WTI price. Similarly, among the GCC countries, dollar exchange rate in Kuwait is the most volatile while that in Qatar is the least volatile, with other dollar exchange rates remaining equally volatile.

We also take account of other statistical features including skweness, kurtosis and Jarque-Bera statistic. In terms of skewness, we observe that the both the crude oil prices (Brent and WTI prices) and the dollar exchange rates across the six GCC countries are negatively skewed In terms of kurtosis, crude oil prices and Kuwaiti dollar exchange rate are largely platykurtic (for kurtosis values being less than 3.0), while the remaining five dollar exchange rates are generally leptokurtic (for kurtosis values being greater than 3.0). In addition, Jarque-Bera statistics indicate that both the crude oil prices and all the dollar exchange rates except the Kuwait’s do not follow normal distribution.

Here, we conduct autocorrelation and heteroscedasticity tests using Ljung-Box test Q-statistics and Autoregressive conditional heteroscedasticity lagrangian multiplier (ARCH-LM) test F-statistics, respectively (see

To further strengthen the choice of estimator, we test for persistence and endogeneity in the predictors, which comprise crude oil prices (Brent and WTI prices) in this case (see

Persistence | Endogeneity | |||
---|---|---|---|---|

p t b r | p t w t i | p t b r | p t w t i | |

s t | ||||

Bahrain | 0.994*** | 0.994*** | −0.008 | −0.007 |

Kuwait | 0.994*** | 0.994*** | 0.053*** | 0.059*** |

Oman | 0.994*** | 0.994*** | −0.008 | −0.006 |

Qatar | 0.994*** | 0.994*** | −0.008 | −0.007 |

Saudi Arabia | 0.994*** | 0.994*** | −0.009 | −0.008 |

United Arab Emirate | 0.994*** | 0.994*** | −0.008 | −0.007 |

Note: This table reports the endogeneity and persistence test results. Starting with the former, the test follows a three-step procedure: First, we run the following predictive regression model: s t = α + β x t − 1 + ε s , t where s t represents exchange rate and x t − 1 is the predictor variable (which are crude oil prices, in this case). In the second step, we follow [

largely exogenous across the GCC countries, with Kuwait being an exception.

In line with [

^{1}Lizardo, et al. [

The predictability power of a potential economic predictor hinges on the statistical significance of the first-order autoregressive coefficient in the theoretical (predictive) model at the conventional levels of significance, namely, 1%, 5%, and 10%. It can be observed that irrespective of measures of oil price series (Brent and WTI prices), the null hypothesis of no predictability is rejected at 1% level of significance (see ^{1} and [

s t | ||
---|---|---|

p t b r | p t w t i | |

Bahrain | −0.0000114*** (0.000000112) | −0.0000129*** (0.000000112) |

Kuwait | −0.049*** (0.0003) | −0.054*** (0.0004) |

Oman | −0.0000264*** (0.000000401) | −0.0000896*** (0.0000000337) |

Qatar | 0.994*** (0.002) | 0.994*** (0.002) |

Saudi Arabia | −0.0000319*** (0.000000232) | −0.0000478*** (0.000000265) |

United Arab Emirate | −0.0000123*** (0.000000111) | −0.0000153*** (0.000000113) |

Note: The in-sample predictability in a bivariate model case is obtained by estimating the equation s t = μ + δ z t − 1 + η ( z t − ρ z t − 1 ) + ε t where δ denotes the coefficient on the predictor z, which in this case stands for crude oil prices. We employ both Brent and WTI prices as proxies for crude oil prices. ***implies the rejection of the null hypothesis of no predictability at 1% level of significance. The values in parentheses are the standard errors associated with the first-order autoregressive coefficients in our predictive model. Here, we consider 75% of the full sample data.

We further compare the in-sample and out-of-sample forecast performance of our oil-based exchange rate model with three time-series models including AR, ARMA, and ARFIMA using the RMSE, the C-T and the D-M statistics (see Tables 4-9). Our results are however mixed. For Kuwait and Saudi Arabia, we find that our oil-based predictive exchange rate model significantly outperforms the time series models both in-sample and out-of-sample, irrespective of the choice of oil price series and the choice of benchmark time-series models^{2}. This conclusion is reached on the basis of positive C-T statistics (see

^{2}This result parallels the findings of [

We however establish an opposing conclusion of the superior forecast performance of the time-series models (AR, ARMA and ARFIMA) over our predictive exchange rate model in the cases of Bahrain, Qatar, and the United Arab Emirate both in-sample and out-of-sample, irrespective of the choice of oil price series, and the choice of benchmark time-series models. This conclusion is reached on the basis of negative C-T statistics (see

s t | ||||||
---|---|---|---|---|---|---|

p t b r | p t w t i | |||||

In-sample | Out-of-sample | In-sample | Out-of-sample | |||

h = 30 | h = 60 | h = 30 | h = 60 | |||

Bahrain | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 |

Kuwait | 0.019 | 0.019 | 0.019 | 0.019 | 0.019 | 0.018 |

Oman | 0.0003 | 0.0003 | 0.0003 | 0.0003 | 0.0003 | 0.0003 |

Qatar | 0.0003 | 0.0003 | 0.0003 | 0.0003 | 0.0003 | 0.0003 |

Saudi Arabia | 0.0006 | 0.0006 | 0.0005 | 0.0006 | 0.0006 | 0.0005 |

United Arab Emirate | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 |

Note: Capturing 75% of the full sample, we evaluate the in-sample and out-of-sample forecast performance (using 30and 60 days as the forecast horizons) of our predictive model, which in this case is the oil-based exchange rate model (using Brent and WTI prices) with the aid of root mean square error (RMSE). The smaller the root mean square error (RMSE), the greater the predictive power of a model and vice versa.

AR* | ARMA** | ARFIMA*** | |||||||
---|---|---|---|---|---|---|---|---|---|

In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | ||||

h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | ||||

Bahrain | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 |

Kuwait | 0.039 | 0.039 | 0.039 | 0.036 | 0.036 | 0.036 | 0.040 | 0.039 | 0.039 |

Oman | 0.0003 | 0.0003 | 0.0003 | 0.0003 | 0.0003 | 0.0003 | 0.0003 | 0.0003 | 0.0003 |

Qatar | 0.0003 | 0.0002 | 0.0002 | 0.0003 | 0.0002 | 0.0002 | 0.0003 | 0.0002 | 0.0002 |

Saudi Arabia | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 |

United Arab Emirate | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 |

Note: *AR stands for autoregressive process/model; **ARMA for autoregressive moving average, and process/model, and ***ARFIMA for fractionally integrated autoregressive moving average process/model. Capturing 75% of the full sample, we evaluate the predictive power of the ARFIMA model both for the in-sample data and out-of-sample data cutting across the forecast horizons of 30 and 60 days using the root mean square error (RMSE). The smaller the root mean square error (RMSE), the greater the predictive power of a model and vice versa.

On the whole, we conclude the overwhelming predictive power of time-series models over our oil-based exchange rate model across the GCC region. That time-series models (AR, ARMA, and ARFIMA), in the majority of cases, predict exchange rates better than our oil-based exchange rate model can do is reflected in the predictability graphs associated with both models (Compare

OEM versus AR | OEM versus ARMA | OEM versus ARFIMA | |||||||
---|---|---|---|---|---|---|---|---|---|

In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | ||||

h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | ||||

Bahrain | −0.029 | −0.029 | −0.028 | −0.029 | −0.029 | −0.028 | −0.028 | −0.028 | −0.027 |

Kuwait | 0.508 | 0.513 | 0.519 | 0.468 | 0.472 | 0.478 | 0.519 | 0.520 | 0.523 |

Oman | −0.009 | −0.009 | −0.008 | −0.009 | −0.009 | −0.008 | 0.028 | 0.027 | 0.023 |

Qatar | −0.094 | −0.084 | −0.067 | −0.094 | −0.085 | −0.068 | −0.090 | −0.083 | −0.069 |

Saudi Arabia | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.004 | 0.003 | 0.003 |

United Arab Emirate | −0.028 | −0.027 | −0.026 | −0.030 | −0.029 | −0.028 | −0.028 | −0.027 | −0.026 |

Note: The Campbell-Thompson (C-T) test statistics as used here compares the unrestricted model, which in this case is the oil-based exchange rate model (using Brent price) with the time-series models (AR, ARMA, and ARFIMA), which constitute the class of restricted models. Positive C-T stat implies that the oil-based exchange rate model (using Brent price) is preferred to AR, MA, ARMA, and ARFIMA models in predicting exchange rates using the in-sample data covering 75% of the full sample and the out-of-sample forecast horizons of 30 and 60 days. On the other hand, negative C-T stat implies that AR, MA, ARMA, and ARFIMA models are preferred to the oil-based exchange rate model (using Brent price) in predicting exchange rates using the in-sample data covering 75% of the full sample the out-of-sample forecast horizons of 30 and 60 days.

OEM versus AR | OEM versus ARMA | OEM versus ARFIMA | |||||||
---|---|---|---|---|---|---|---|---|---|

In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | ||||

h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | ||||

Bahrain | −0.027 | −0.026 | −0.025 | −0.027 | −0.027 | −0.026 | −0.026 | −0.026 | −0.024 |

Kuwait | 0.519 | 0.524 | 0.529 | 0.479 | 0.483 | 0.489 | 0.529 | 0.531 | 0.533 |

Oman | 0.011 | 0.011 | 0.011 | −0.007 | −0.008 | −0.006 | 0.029 | 0.028 | 0.024 |

Qatar | −0.109 | −0.099 | −0.082 | −0.109 | −0.099 | −0.082 | −0.106 | −0.098 | −0.084 |

Saudi Arabia | 0.005 | 0.004 | 0.004 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 |

United Arab Emirate | −0.024 | −0.024 | −0.023 | −0.028 | −0.028 | −0.026 | −0.025 | −0.025 | −0.023 |

Note: The Campbell-Thompson (C-T) test statistics as used here compares the unrestricted model, which in this case is the oil-based exchange rate model (using WTI price) with the time-series models (AR, ARMA, and ARFIMA), which constitute the class of restricted models. Positive C-T stat implies that the oil-based exchange rate model (using WTI price) is preferred to AR, ARMA, and ARFIMA models in predicting exchange rates using the in-sample data covering 75% of the full sample and the out-of-sample forecast horizons of 30 and 60 days. On the other hand, negative C-T stat implies that AR, ARMA, and ARFIMA models are preferred to the oil-based exchange rate model (using WTI price) in predicting exchange rates using the in-sample data covering 75% of the full sample the out-of-sample forecast horizons of 30 and 60 days.

OEM versus AR | OEM versus ARMA | OEM versus ARFIMA | |||||||
---|---|---|---|---|---|---|---|---|---|

In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | ||||

h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | ||||

Bahrain | 3.224*** | 3.179*** | 3.053*** | 3.240*** | 3.199*** | 3.072*** | 3.146*** | 3.105*** | 2.977*** |

Kuwait | −15.956*** | −16.548*** | −17.257*** | −15.280*** | −15.773*** | −16.397*** | −24.269*** | −24.549*** | −25.044*** |

Oman | 1.494 | 1.540 | 1.309 | 1.541 | 1.589 | 1.356 | −6.433*** | −6.023*** | −5.053*** |

Qatar | 4.821*** | 4.365*** | 3.532*** | 4.831*** | 4.374*** | 3.541*** | 5.423*** | 5.012*** | 4.209*** |

Saudi Arabia | −10.329*** | −10.146*** | −9.890*** | −10.985*** | −10.824*** | −10.599*** | −6.152*** | −6.031*** | −5.856 |

United Arab Emirate | 3.121*** | 3.074*** | 2.948*** | 3.252*** | 3.215*** | 3.087*** | 3.129*** | 3.085*** | 2.957*** |

Note: The Diebold-Mariano (D-M) test statistic as used here compares the forecast errors of the unrestricted model, which in this case is the oil-based exchange rate model (using Brent price) and the restricted model comprising the time-series models (AR, ARMA, and ARFIMA). The negative and statistical significance at 1% (***), 5% (**) and 10% (*) implies that the oil-based exchange rate model (using Brent price) significantly outperforms the AR, ARMA, and ARFIMA models using in-sample data covering 75% of the full sample and out-of-sample forecast horizons of 30 and 60 days. However, the positive and statistical significance at 1% (***), 5% (**) and 10% (*) implies that the AR, ARMA, and ARFIMA models significantly outperform the oil-based exchange rate model (using Brent price) using in-sample data covering 75% of the full sample and out-of-sample forecast horizons of 30 and 60 days.

OEM versus AR | OEM versus ARMA | OEM versus ARFIMA | |||||||
---|---|---|---|---|---|---|---|---|---|

In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | ||||

h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | ||||

Bahrain | 3.081*** | 3.032*** | 2.903*** | 3.118*** | 3.070*** | 2.941*** | 3.022*** | 2.974*** | 2.843*** |

Kuwait | −15.418*** | −15.988*** | −16.653*** | −14.673*** | −15.145*** | −15.721*** | −23.245*** | −23.526*** | −23.975*** |

Oman | −4.539*** | −4.515*** | −4.320*** | 1.251 | 1.307 | 1.076 | −6.424*** | −6.033*** | −5.108*** |

Qatar | 5.352*** | 4.902*** | 4.104*** | 5.359*** | 4.909*** | 4.112*** | 6.006*** | 5.598*** | 4.834*** |

Saudi Arabia | −6.917*** | −6.798*** | −6.637*** | −11.411*** | −11.299*** | −11.158*** | −6.573*** | −6.481*** | −6.351*** |

United Arab Emirate | 2.888*** | 2.835*** | 2.707*** | 3.146*** | 3.100*** | 2.971*** | 2.981*** | 2.932*** | 2.802*** |

Note: The Diebold-Mariano (D-M) test statistic as used here compares the forecast errors of the unrestricted model, which in this case is the oil-based exchange rate model (using WTI price) and the restricted model comprising the time-series models (AR, ARMA, and ARFIMA). The negative and statistical significance at 1% (***), 5% (**) and 10% (*) implies that the oil-based exchange rate model (using WTI price) significantly outperforms the AR, ARMA, and ARFIMA models using in-sample data covering 75% of the full sample and out-of-sample forecast horizons of 30 and 60 days. However, the positive and statistical significance at 1% (***), 5% (**) and 10% (*) implies that the AR, ARMA, and ARFIMA models significantly outperform the oil-based exchange rate model (using WTI price) using in-sample data covering 75% of the full sample and out-of-sample forecast horizons of 30 and 60 days.

in the region, and as noted by Amin [

We offer new evidence on the predictability of exchange rates using crude oil prices, namely Brent and WTI prices, across the six GCC countries comprising Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirate. Driven by the need to account for some salient features usually present in high frequency time-series data, we employ the estimator proposed by Lewellen [

Further, our results show the presence of significant in-sample predictability of exchange rates using crude oil prices (Brent and WTI prices) across the GCC countries. The results of forecast evaluation based on the root mean square error (RMSE), Campbell-Thompson (C-T) statistic and Diebold-Mariano (D-M) statistic are rather mixed. We obtain greater forecast performance in favour of our predictive exchange rate model in the cases of Kuwait and Saudi Arabia, while we establish a superior forecast accuracy of time-series models (AR, ARMA, and ARFIMA) in the contexts of Bahrain, Qatar, and the United Arab Emirate. The forecast performance of our predictive exchange rate model and time-series model is highly sensitive to the choice of benchmark time-series models: while AR and ARMA models predict the dollar exchange rate better than our predictive model, the reverse is the case for the ARFIMA model using both in-sample and out-of-sample periods. We, however, conclude the overwhelming forecast performance of time-series models (namely, AR, ARMA, and ARFIMA) over our oil-based exchange rate model in predicting exchange rates across the GCC region.

Meanwhile, some policy implications can be highlighted from the results of this study. The significance of oil price in influencing the exchange rate behavior of some GCC countries will be useful to financial analysts and investors who rely on such information for investment decisions and to policy makers when making policy decisions. Notwithstanding the usefulness of the research findings of the study, a number of areas can still be explored to improve the paper and are therefore suggested for future research. The first area relates to the choice of countries; future research can conduct same for other countries particularly net oil importers and non-OPEC net oil exporters. The latter is also important to see if the results of the giant members of OPEC can be generalized for the non-members in terms of the predictive power of oil price in forecasting stock returns. The second area relates to other statistical properties underlying exchange rate which are not captured in the current study. These properties include structural breaks and asymmetries. We therefore suggest that further studies investigate if accounting for structural breaks and asymmetries in oil-exchange rate nexus would improve the predictability of exchange rates using crude oil prices. A considerable attention can also be drawn towards the use of real exchange rate, which is a measure of a country’s international competitiveness in the foreign market.

The author declares no conflicts of interest regarding the publication of this paper.

Nnachi, O.I. (2018) Improving the Forecast Accuracy of Oil-Exchange Rate Nexus in GCC Countries. Theoretical Economics Letters, 8, 3267-3284. https://doi.org/10.4236/tel.2018.815202

Predictability Graphs for oil-based exchange rate models (WTI price).