As the second-largest oil producer and natural gas exporter, Russia's war with Ukraine has severely impacted the energy market. To what extent has the war influenced crude oil prices, and has it altered the long-term dynamics of oil prices? An objective analysis of the effects of the Russia–Ukraine war on the crude oil market can assist relevant entities in developing both short-term emergency strategies and long-term response plans. This study establishes an analytical framework of the event analysis method based on multiresolution causality testing (EMC). The results of the multiresolution causality testing reveal a significant one-way causality between the Russia–Ukraine war and crude oil prices. Afterward, using the event analysis based on variational mode decomposition (VMD), from October 1, 2021, to August 25, 2022, as the event window, we found that the war and its chain events caused the West Texas Intermediate (WTI) crude oil prices to increase by $37.14, a 52.33% surge, and the Brent crude oil price to rise by $41.49, a 56.33% increase. During the event window, the Russia–Ukraine war can account for 70.72% and 73.62% of the fluctuation in WTI and Brent crude oil prices, respectively. Furthermore, the war amplified oil price volatility and fundamentally altered the trend of crude oil prices. Consequently, this study proposes four recommendations: the establishment of an emergency management mechanism for the oil market, the diversification of oil and gas imports by energy-importing countries, the steady advancement of energy transformation, and the judicious use of financial instruments by enterprises to hedge risks.
Ever since the outbreak of the Russia–Ukraine war on February 24, 2022, it has persisted for more than one year. This war and its consequent chain of events have adversely impacted the global economy through several channels, such as the commodity market, stock market, and trade. Notably, the energy market has been hit the hardest. According to the data released by the American Oil and Gas Journal, in 2021, global oil production stood at 4.423 billion tons, and Russia's oil production accounted for 534 million tons, which amounts to 12% of the worldwide oil production, making it the second-largest oil producer in the world after the United States. The eruption of the Russia–Ukraine war and the subsequent US energy sanctions imposed on Russia have resulted in a significant surge in crude oil prices. On March 7, 2022, the WTI crude oil futures price touched $133.460/barrel, and the Brent crude oil futures price reached $139.130/barrel, the highest price since July 2008. Since then, crude oil prices have remained consistently high, experiencing short-term fluctuations during the Russia–Ukraine negotiations, G7 sanctions, and different attitudes of European and American countries. In addition, the Fed's interest rate hike and the strengthening of the US dollar have compounded the impact during this period. On March 26, 2022, the Federal Reserve announced the first round of interest rate hikes by 25 basis points, followed by 50 basis points on May 4, and 75 basis points on June 15, July 28, and September 22, respectively. Against the backdrop of the continued strengthening of the US dollar, on September 27, 2022, the WTI crude oil futures price dropped to $76.310/barrel, and the Brent crude oil futures price dropped to $83.650/barrel, returning to the level at the beginning of 2022. Therefore, this study aims to explore three research questions: How much did the Russia–Ukraine war cause the price of crude oil to rise? Does it have the same impact on the crude oil market in America and Europe? How to identify and separate the effects of other events?
The event study method has become a standard analytical tool for evaluating the economic or financial impact of specific unexpected events in the economic and financial fields (MacKinlay, [
The contributions of this paper are as follows:
Firstly, an analytical framework, namely EMC, is proposed to quantitatively study the net impact of extreme events. The impact of the Russia–Ukraine war on crude oil prices has received limited attention in the literature (Vasileiou, [
Secondly, evaluating the impact of extreme events is a topic that has received much attention in the research community. However, there is a prevalent issue in most existing studies, which adopt event analysis methods without distinguishing the impact of additional factors in the event window. This approach results in a deviation in estimating the event's impact. To address this challenge, this study employs multiresolution causality testing to differentiate and separate the overlapping effects of other significant events in the event window. Specifically, the multiresolution causality testing technique assesses geopolitical risk in the event window and analyzes the causal relationship between the US dollar index and crude oil prices. Saâdaoui et al. ([
Thirdly, current event analysis methods for extreme events rely on empirical mode decomposition (EMD). However, the EMD algorithm suffers from imprecise calculation parameter-stopping standards, leading to deviations in the extreme positions of intrinsic mode functions (IMFs). Additionally, the period of the IMF in the main mode is not entirely consistent with the period of the original sequence (Zhang et al., [
The following chapters of the article are arranged as follows: The next section is a literature review and exploration of impact channels; After that, the next introduces the research methods of the article, including the EMC analytical framework, VMD decomposition, and multi-resolution causality testing; The penultimate section shows the research results of the paper; The last section is the conclusion and suggestions of the article.
Several literature pieces have explored the effects of extreme events on the energy market. These extreme events refer to occurrences with severe and medium to long-term impacts on the energy market (Zhang et al., [
Different types of extreme events have different impacts on the crude oil market. Various literature studies have focused on assessing the influence of different extreme events on the crude oil market. Iglesias and Rivera-Alonso ([
Furthermore, some literature explores the impact channels of extreme events on crude oil prices. Firstly, geopolitical conflicts and epidemics impose supplementary burdens on the global economy, impinging upon oil supply and demand dynamics and augmenting oil price risks. In addition, extreme events can intensify stock market volatility and amplify oil price risks via market-to-market interactions. Meanwhile, extreme events can increase the risk of oil price jumps. Geopolitical conflicts and other analogous occurrences can also precipitate disruptions in oil supply (El Gamal and Jaffe, [
Regarding research methods, event analysis is widely employed for investigating extreme events. In particular, Ma et al. ([
Zhang et al. ([
Overall, there have been some literature studies on the impact of extreme events on the energy market, and event analysis methods are often chosen. However, the existing literature does not consider the influence of other factors within the event window, and the effects of other factors are mixed in the research, resulting in measurement bias, thereby overestimating or underestimating the event's impact, leading to decision-making errors. Based on this, the article proposes an EMC analysis framework and takes the Russia–Ukraine war as an example to calculate the net impact of extreme events on crude oil prices, which is the first innovation of this article. The second innovation of the paper is the use of multiresolution causal testing to separate the impact of mixed events within the event window. This method can determine the causal relationship between sub-sequences based on sequence decomposition and can help determine the causal relationship between events and other factors and oil prices, thereby helping to isolate the net impact of events on oil prices. The third innovation of this article is the use of VMD decomposition research, which effectively avoids the stopping standard problem of EMD algorithm parameter calculation and makes the decomposition results more reliable. This article effectively measures the net impact of extreme events on crude oil prices, providing a reference paradigm for research on such issues.
The price of crude oil exhibits sensitivity to various factors, including the equilibrium relationship between supply and demand, which ultimately determines the long-term trend of oil prices. However, short-term changes in oil prices are primarily influenced by extreme events and monetary factors. The current mainstream event research method does not consider the impact of other factors during the same event period but directly considers the event as the only factor within the analysis window for direct calculation. This approach easily includes other factors, leading to measurement bias. Solving the problem of event mixing and separating the net impact of events is also a difficulty in event research methods. The event analysis method based on EMD decomposition proposed by Zhang et al. ([
Graph: Fig. 1Event analysis method based on Multiresolution Causality testing (EMC) analytical framework.This research framework is used to analyze the short-term net impact of the Russia–Ukraine war on crude oil prices. This framework can also be applied to the short-term net impact of other major crisis events on commodity prices.
The first step is to select the events that need to be studied and determine whether other factors may simultaneously affect the price of crude oil.
The determination of data frequency and analysis window is a crucial step in conducting an accurate assessment of an event's impact. To establish the data frequency, researchers must consider the duration of the event and data availability. The event analysis window consists of an event window and an estimation window, where the former represents the period during which the event occurred and had an impact. The latter is a period when the event has not happened or has no influence. To specify the estimation window, the trading day before the first day of the event window is selected as the final day.
However, the time scale of IMF is restricted by the frequency and window size, with the maximum period extracted from the signal limiting to no more than half of the data points (Zhang et al., [
The third step is to decompose the crude oil price series and related variables selected based on data frequency and analysis window by VMD and obtain IMFs with different center frequencies.
The fourth step is to conduct a multiresolution causality test on the interested events and preliminarily judge whether the events are the causes of crude oil price fluctuations. At the same time, other important factors that may affect the fluctuation of crude oil prices in the same period shall be tested for cause and effect. If there is a significant impact, it needs to be stripped; if there is no significant impact, it can be ignored.
Identifying the main mode is paramount in the fifth step of the analysis process. The VMD decomposition technique yields IMFs that are indicative of various influencing factors. The sum of one or multiple IMFs can represent the impact of extreme events and is considered the main mode in the analysis (Zhang et al., [
The second task involves assessing the influence of extreme events on other modes. The analysis of the average cycle of IMFs is crucial to determine the specific meaning of each IMF. Subsequently, spectrum analysis, T-test, and other techniques can be employed to ascertain whether extreme events lead to a more significant fluctuation in crude oil prices.
To judge whether the event has a long-term impact, it takes longer to test the breakpoint of the data to test whether the event has changed the inherent pattern of the trend of crude oil prices.
According to the results of the first six steps, summarize the short-term, medium-term, and long-term impacts of the incident on crude oil prices and give corresponding economic explanations.
VMD assumes that any signal f is composed of a series of sub-signals (modes) u
Different from the concept of IMF proposed by Huang, the VMD algorithm redefines the intrinsic modal function of limited bandwidth with stricter constraints, which is defined as the component modal function of AM and FM, namely:
Graph
In which A
Based on classical wiener filtering, VMD obtains the center frequency and bandwidth limitation by solving the variational problem, finds the effective components of each center frequency in the frequency domain, and obtains the modal function.
The decomposition process of VMD is the solution process of a variational problem, which mainly includes the following constraints: (
2
Graph
Compared with EMD, VMD is a more effective signal decomposition method, which avoids the endpoint effect and modal aliasing in EMD decomposition through image continuation.
Based on MRA theory, the time series signal f(t) is decomposed into multiple regular or irregular sequences when MRDA is decomposed. Applying VMD decomposition to MRDA, we can get:
3
Graph
Among them,
Let
4
Graph
where ~ represents the equivalence of distribution. The following assumptions are made on each scale j:
H
H
A P-order bivariate multi-scale vector autoregressive model can test this causal relationship:
5
Graph
where k = 1, ..., N, j = 1, ... J. Z = (Z
If the following original hypothesis is not rejected, f
6
Graph
Vice versa.
Then, construct the following F-test statistics:
7
Graph
where
This study focuses on the impact of the Russia–Ukraine war on crude oil prices. On February 24, 2022, Russia announced special military action against Ukraine, and the Russia–Ukraine war broke out. The Russia–Ukraine war did not break out suddenly; it has a long history. The Crimean Crisis in 2014 laid a crisis for the war. The crisis in eastern Ukraine from October 2021 to February 2022 finally evolved into the Russia–Ukraine war. Until October 27, the war continued.
The general principle for selecting event windows is the starting and ending points of the event. To ensure the robustness of the decomposition results, the estimation window and event window need to be symmetrically distributed. However, the Russia–Ukraine war has not yet ended. Therefore, the selection principle of the event window in this article is to include the critical nodes of the war, including the outbreak of the war, sanctions imposed by the United States and EU countries on Russia, and a cap on Russian oil export prices by the G7 countries. Therefore, the event window is determined to be from February 24, 2022, to October 27, 2022, and the estimated window is from June 24, 2021, to February 23, 2022. The estimation window and the event window are symmetrically distributed. The analysis window is from June 24, 2021, to October 27, 2022, with 350 data points. The event window and estimation window can be adjusted appropriately within the selection principles. To ensure the robustness of the analysis results, the article also selected two event windows for analysis: February 24, 2022–September 22, 2022, and from February 24, 2022, to December 1, 2022. The corresponding estimation windows are from July 29, 2021, to February 23, 2022, and from May 19, 2022 to February 23, 2022. The results are detailed in Appendices B and C.
This study utilizes the daily spot prices of WTI and Brent crude oil for research. The trend chart in Fig. 2 highlights the fluctuations in crude oil prices during the analysis window and marks the factors contributing to these changes.
Graph: Fig. 2Prices of WTI and Brent crude oil from June 24, 2021 to October 27, 2022.This line chart displays the trend of crude oil prices within the analysis window and indicates the reasons for the significant fluctuations in crude oil prices during this period.
Specifically, the Russia–Ukraine war and its consequent chain of events significantly impacted the crude oil market, resulting in sharp price fluctuations. Additionally, the continuous interest rate increase of the Federal Reserve and the sustained strength of the US dollar are identified as further factors influencing the market. In light of these findings, this paper employs the geopolitical risk (GPR) index[
VMD decomposition of WTI, Brent crude oil price, GPR index, and US dollar index is carried out, respectively. Each series has 350 data points, and the decomposition level is fixed at 4. The results are shown in Fig. 3. IMF1 and IMF2 are high-frequency sub-signals, IMF3 is medium-frequency sub-signals, and IMF4 is low-frequency sub-signals.
Graph: Fig. 3VMD decomposition results of each variable.a VMD decomposition results of WTI and Brent. solid line WTI, dotted line Brent. b VMD decomposition results of GPR index. c VMD decomposition results of the dollar index.
This study initially performs a Granger causality test on the original series to assess the causative correlation between the Russia–Ukraine war, the US dollar index, and crude oil prices. Subsequently, the study utilizes the multiresolution causality test approach to examine the decomposed signal IMFs. The outcomes of the analysis are presented in Table 1[
Graph
Based on the findings presented in Table 1, the one-way causal relationship between the GPR index and the WTI and Brent crude oil prices is highly significant, whether using the original series or IMFs. However, no causal relationship exists between the original US dollar index series and the WTI and Brent crude oil prices. Additionally, there is no causal relationship between decomposed sub-series. Subsequently, we conduct further analysis on the IMF4 charts of WTI, Brent, and the US dollar index.
As illustrated in Fig. 4, the IMF4 of WTI and Brent has steadily increased since the onset of the Russia–Ukraine War on February 24, 2022. The Federal Reserve has announced multiple rounds of interest rate increases, starting with an initial 25 basis points increase on March 26, 2022, followed by additional increments of 50 basis points on May 4 and 75 basis points on June 15, July 28, and September 22, respectively. Oil is priced in US dollars, and if the US dollar strengthens, oil prices will inevitably fall. Nevertheless, in the third round of interest rate hikes, oil prices began to show a slow downward trend under various factors, indirectly illustrating the severe impact of the Russo-Ukraine war on oil prices. Consequently, during the subsequent event analysis, the impact of the strengthening of the US dollar can be ignored, and the estimated impact of the Russia–Ukraine war on oil prices can be considered a lower limit of the actual impact.
Graph: Fig. 4IMF4 trend of WTI, Brent, and dollar index.This figure illustrates no correlation between the IMF4 of WTI, Brent crude oil prices, and the IMF4 of the dollar index. At the same time, the date of the Federal Reserve's interest rate hike indicates that the Fed's multiple rate hikes have not been able to lower crude oil prices. Therefore, the impact of a stronger US dollar can be ignored within the event window.
To determine the primary mode of a given sequence, the decomposed IMF must undergo statistical testing to compute the average period, correlation coefficient, and variance percentage of each IMF in the original sequence variance. The average period is determined by dividing the total number of points in each IMF by the number of peaks within it. The correlation coefficient measures the degree of correlation between each IMF and the original sequence, while the percentage of variance reflects the contribution of each IMF toward the original sequence. Table 2 presents the IMF statistics for WTI and Brent, indicating no notable difference.
Table 2 Measures of IMFs and the residue for the WTI and Brent daily crude oil price.
WTI Brent Mean period Correlation coefficient Variance as % of observed Mean period Correlation coefficient Variance as % of observed IMF1 6.73 0.0985 1.02 5.30 0.0912 0.55 IMF2 13.46 0.1863 2.24 12.07 0.1794 2.02 IMF3 38.89 0.5365 9.92 35 0.5179 10.74 IMF4 175 0.9301 71.87 175 0.9277 73.09 Residue 0.0603 0.60 0.0878 0.32
Obviously, IMF4 is the principal mode of the sequence, exhibiting a correlation coefficient of 0.9301 (WTI) and 0.9277 (Brent), alongside a variance contribution of 71.87% (WTI) and 73.09% (Brent). Conversely, the remaining IMFs display considerably lower correlation coefficients and variance contributions, among which the correlation coefficient of the largest IMF3 is 0.5365 (WTI) and 0.5179 (Brent), accompanied by a variance contribution of 9.92% (WTI) and 10.74% (Brent).
The IMF4 is normalized to the [0,1] range and compared with the original series. It is found that the trend of the IMF4 is consistent with the overall trend of the WTI and Brent crude oil prices, as revealed in Fig. 5. Specifically, the IMF4 increased from its low point in July 2021 and reached its peak in June 2022, after which it started to decline. According to Zhang et al. ([
Graph: Fig. 5Normalization trend of WTI, Brent, and IMF4.Comparing the normalized IMF4 trend with the crude oil price trend, it was found that the two are consistent, indicating that IMF4 can be used as the primary mode of oil price.
Furthermore, we analyze the high-frequency IMFs. Table 2 indicates that the cycle of IMF1 is roughly one week[
However, the influence of these high-frequency fluctuations tends to zero in the whole event window. We test the sum of c
Table 3 T-test of IMF reconstruction of WTI and Brent daily crude oil price.
WTI Brent Mean Mean 1 0.00000251 1.0000 0.00000172 1.0000 2 0.0000127 0.9999 0.0000129 0.9999 3 0.0003481 0.9991 0.0004193 0.9990 4 88.52375 0.0000 93.21652 0.0000
Accordingly, the change in IMF4 can be interpreted as the impact of the Russia–Ukraine war. Figure 6 illustrates the proportion of price changes attributable to each IMF's impact on the overall price change. The WTI crude oil price exhibits a 70.72% price change associated with IMF4, while the Brent crude oil price reports a 73.62% change linked to IMF4. Specifically, these results suggest that the Russia–Ukraine war resulted in a 70.72% change in WTI crude oil price and a 73.62% change in Brent crude oil price during the event window.
Graph: Fig. 6Price change percentage of IMFs.The Russia–Ukraine war resulted in a 70.72% change in WTI crude oil price and a 73.62% change in Brent crude oil price during the event window.
So, does the Russia–Ukraine war amplify the volatility of the crude oil market? To explore this, we employ the Hilbert–Huang transform (HHT) technique, as put forth by Huang et al. ([
Graph: Fig. 7Normalized instantaneous frequency in the range of [0.5,1].The event window shows a higher and denser instantaneous frequency than the estimation window, which indicates that the Russia–Ukraine war has indeed amplified the volatility of the crude oil market.
The findings above highlight the short-term influences of the Russia–Ukraine war on crude oil prices. To investigate whether this war has caused any long-term changes in the trend of crude oil prices, the current study employs Bai and Perron's ([
Through the above analysis, we can get the impact of the Russia–Ukraine war on crude oil prices as follows:
- The multiresolution causality test indicates a significant one-way causal relationship between all IMFs of GPR and crude oil prices. In contrast, no causal relationship was observed between the US dollar index and crude oil prices. Additionally, the study found a slow downward trend in oil prices after the Federal Reserve's third round of interest rate increases, suggesting that the impact of a stronger US dollar can be negligible during the event window. However, the study notes that the spread of COVID-19 mutant strains and the Federal Reserve's interest rate hike may all contribute to the decline of crude oil prices during the event window. Despite this, the war still led to a substantial rise in crude oil prices, indicating that the analysis method employed in this study provides a lower limit of the war's impact on crude oil prices, with the actual impact exceeding the measured value.
- Event analysis reveals that the Russia–Ukraine war and its subsequent events amplified the high-frequency fluctuation of crude oil prices, resulting in an increase of $37.14, or 52.33% (WTI), and $41.49, or 56.33% (Brent). The Russia–Ukraine war accounts for 70.72% of the fluctuation in WTI crude oil prices and 73.62% of the fluctuation in Brent crude oil prices during the event window while also causing a fundamental shift in the long-term trend of crude oil prices.
- The impact of the Russia–Ukraine war on Brent crude oil prices is more pronounced than its impact on WTI crude oil prices. According to Eurostat data, Europe's oil demand is approximately 650 million tons, with self-produced oil accounting for about 240 million tons and import demand accounting for ~410 million tons. Oil import dependence is around 63%. Kpler's monitoring data for 2021 shows that oil imports from Russia amounted to about 120 million tons, representing 29% of the total import volume, and dependence on Russian oil was around 18%. Among these imports, the Netherlands', Italy's, and Turkey's demand for Russian oil accounted for 35%, 24%, and 42% of their total imports, respectively. As a significant energy importer in Europe, the Russia–Ukraine war substantially impacts Europe, increasing Brent crude oil prices by $41.40. Additionally, the war caused the price difference between Brent crude oil and WTI crude oil to increase sharply. On March 23, 2022, the price difference between the two was as high as $12.64 per barrel.
- The effects of high-frequency IMF fluctuations are transient. For example, during the war, the favorable or unfavorable signals released by the Russia–Ukraine negotiations will cause the price of crude oil to jump or fall quickly. However, such fluctuations dissipate swiftly as the price is adjusted to negate their impact.
- The primary impact of the Russia–Ukraine war on crude oil prices is reflected in low-frequency IMF fluctuations. The fluctuation cycle within low-frequency IMF aligns with the broader fluctuations observed in crude oil prices. During the analysis window, the increase in crude oil prices was caused by the Russia–Ukraine war. This finding is particularly noteworthy as, prior to the war, crude oil prices had not exhibited any significant upward trends.
As the second largest oil producer globally, Russia's war with Ukraine and its chain of events have significantly impacted the world energy market. Given its importance as a pillar of the global economy, the trend of oil prices has long been a subject of inquiry for both industry and academic circles (He et al., [
Initially, VMD was employed to decompose crude oil prices, the GPR index, and the US dollar index, and a multiresolution causality test was performed. The results showed that all IMFs decomposed by the GPR index had a significant one-way causality with all IMFs decomposed by crude oil price, while there was no causal relationship between the US dollar index and crude oil prices. Accordingly, the impact of the interest rate hike by the Federal Reserve could be disregarded in the event analysis, and the lower limit of the oil price increase caused by the Russia–Ukraine war was determined. Afterward, identifying the main mode of crude oil prices and using event analysis, it was found that (
Given the strategic importance of crude oil and the formation mechanism of prices in a seller's market, it is apparent that oil prices are highly susceptible to extreme events, particularly geopolitical conflicts in major oil-producing nations. Energy security is challenged (Zhao et al., [
The EMC analysis framework proposed in this study is deemed suitable for gauging the net impact of sudden and transient extreme events, such as wars and geopolitical conflicts, on commodity prices. However, careful consideration should be given when employing this methodology. For events that have a long duration and trigger multiple chain reactions, such as the COVID-19 pandemic, it is crucial to delineate multiple event windows and clearly define the research objects. Each event window should be evaluated independently to ensure accurate measurements and analysis.
Within the window of analysis for the Russia–Ukraine war, the added influence of the spread of COVID-19 mutant strains and the impact of the Federal Reserve's interest rate hike led to a decline in crude oil prices. Nevertheless, the Russia–Ukraine war still resulted in a significant upsurge in crude oil prices. Thus, the analysis result in this study can obtain only the lower limit of the impact of the war on crude oil prices. Although the causal testing showed negligible impacts of other factors, the actual impact still exceeded the value calculated in this paper. The upcoming research should focus on developing a technique to peel off these factors together and measure the impact of the Russia–Ukraine war. Moreover, it is necessary to more precisely identify the effect of the Russia–Ukraine war on each IMF while considering varied driving factors, such as the equilibrium of supply and demand or the price of substitutes. Furthermore, conducting an in-depth assessment of the long-term impact of the Russia–Ukraine war on oil prices is also imperative. These concerns will be the focus of our future research.
The research was supported by the National Natural Science Foundation of China (72073124, 71988101), the grant from MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS (E2810801), Fundamental Research Funds for the Central Universities (Grant no. UCAS-E2ET0808X2), the National Social Science Foundation of China (22VRC055).
QZ: Writing—original draft preparation, software, data curation, visualization, writing— reviewing and editing. YH: Conceptualization, methodology, formal analysis, validation, writing—reviewing and editing, funding acquisition. JJ: Supervision, project administration. SW: Supervision, project administration.
The datasets analyzed during the current study are available in the Harvard Dataverse, https://doi.org/10.7910/DVN/UDPWJY.
This article does not contain any studies with human participants performed by any of the authors.
Informed consent is not applicable. The study used secondary data. The authors did not directly engage any participants.
The authors declare no competing interests.
Graph: supplemental material
Graph: Dataset 1
The online version contains supplementary material available at https://doi.org/10.1057/s41599-023-02526-9.
Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
By Qi Zhang; Yi Hu; Jianbin Jiao and Shouyang Wang
Reported by Author; Author; Author; Author