The Ukrainian crisis, economic sanctions, oil shock and commodity currency: Analysis based on EMD approach

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Abstract

The sanctions imposed against Russia in 2014 coincided with a shock in the oil market. It is believed that both the sanctions and the fall in prices over oil have affected both the ruble exchange rate, which devalued by 2 times in relation to the pre-crisis level. The authors of the article assess the impact of sanctions on the ruble exchange rate using ensemble empirical mode decomposition and Hurst exponent. Based on the theory of an effective market, the results of the article shown that in 2014–2015 there was no direct impact of sanctions on the ruble exchange rate.

Additionally, the impact of panic in the foreign exchange market on the exchange rate has been estimated, and it is also shown that the foreign exchange market has a long memory.

Introduction

In 2014, amid the Ukrainian crisis, the United States and European Union imposed sanctions to exert economic pressure on Russia. At the same time, there was a so-called oil shock in the oil market, when in a few months the price of oil fell from around $111 in July 2014 to $48 per barrel in January 2015. Thus, the Russian economy, on the one hand, is highly dependent on oil price, and on the other hand, external restrictions are imposed by the US and EU.

In theory, the main aim of the sanctions is to change or to limit behavior of the target and bring to the end unacceptable activities resulting to significant impact on the economy (in theory, unacceptable damage to target`s economy). More than that, sanctions to Russia were reinforced by the oil shock, which in turn affected all oil-producing countries. Conjoined effect of sanctions and the oil shock on the Russian economy make difficult adequate assessment of external political interference impact on its economy.

Recent developments in the oil market in 2014–2015 significantly affected the economy of oil-producing countries, including Russia, through the depreciation of the national currency against the US dollar. It is customary to call the currencies of such countries the “commodity currency” (Coudert et al., 2015). Indeed, oil prices directly affect the exchange rate of oil-producing countries (De Vita and Trachanas, 2016). There are a number of papers which study various aspects of oil and oil shock impact (Kilian, 2014), for example - on the exchange rates (Golub, 1983; Brahmasrene et al., 2014)), on interest rates (Cunado et al., 2015), on equity markets (Chen et al., 1986; Diaz et al., 2016)), on the economy of certain regions (Cunado et al., 2015), as well as on economic growth of individual countries (Tuzova and Qayum, 2016) and economic resilience (Dudian et al., 2017). Also, there are papers describing oil price in terms of geopolitical events (Bariviera et al., 2017b), structural breaks (Sun and Shi, 2015) and under economic and statistical constraints (Wang et al., 2015). It is possible to figure out that effects of the oil shock are well studied.

Unlike other oil-producing countries, as already mentioned, Russia has suffered even more due to sanctions (Tuzova and Qayum, 2016) on the financial, oil and gas sectors of the country. Assessment of the impact of sanctions is also well understood in different aspects (Caruso, 2003), such as impact on policies (Lam, 1990), on trade (Caruso, 2003), on the country`s economy in a broad sense (Torbat, 2005) and employment (Abdusalam and Saleh, 2008).

In conditions of such a severe shock in oil revenues, reinforced by the actual closure of external debt markets due to sanctions imposed against Russia, the economy and forex exchange market could no longer remain in the rest trying to find new stable equilibrium state.

In this paper, we study the transitional processes in the Russian economy from the beginning of the oil shock and imposed constraints in terms of Treasury sanctions. Papers describing the influence of the oil shock and sanctions on the Russian economy are not so common. For instance, one of the previous research was carried out (Tuzova and Qayum, 2016) to assess the impact of sanctions and the oil shock on the performance of Russia’s economic growth using econometric methods (vector autoregression), based on earlier publications (Ito, 2008). Regardless of research provide by (Tuzova and Qayum, 2016), (Dreger et al., 2016) have also used vector autoregression and the GARCH model to analyze how sanctions effect exchange rate. Generally speaking, the idea of using vector autoregression in such analysis is not new, and has been extensively used since the early 80 s (Burbidge and Harrison, 1984). Therefore, the main task of this article is to analyze the impact of sanctions, oil shock on the ruble exchange rate without using a vector autoregressive system on which there is a large number of works.

The question of the effectiveness of sanctions against a particular country is always debatable. For example, according to (Hufbauer, 2007), only one-third of them have been successful, at least partially. According to a recent survey, carried out by the Bloomberg agency (Tanas et al., 2017), if the U.S. eases its curbs, the Russian currency would appreciate 5 percent to 10 percent/. On the other hand, (Dreger et al., 2016) shows that the Russian ruble is unaffected by the Western sanctions. In recent studies concerning oil price shocks and exchange rate movements (Volkov and Yuhn, 2016), authors put a disclaimer that “the period is also associated with the sanctions that were placed on Russia by the United States and the European Union. It is likely that the drop in the value of the ruble is at least in part attributed to the geopolitical landscape”.

One could derive 2 hypotheses:

Null Hypothesis: According to data scientist (Dreger et al., 2016), there is no effect on the ruble exchange rate

Alternative hypothesis: According to business analyst opinion (Tanas et al., 2017), the quantitative depreciation effect of the sanction impact on the ruble exchange rate is estimated at 5–10% to so called “fair exchange rate”.

Hence, it is necessary to confirm or refute each hypotheses. The task is complicated by the fact that econometric methods such as vector autoregression, co-integration and GARCH were used in the studies mentioned already (Dreger et al., 2016). To confirm or refute the hypotheses, the authors propose to use methods of signal processing: such as empirical mode decomposition by Hilbert-Huang (Huang et al., 2003, 1998). It is worth noting, that method is based on earlier work (Bedrosian, 1963; Priestley, 1965; Shekel, 1953). This method can decompose non-stationary data into component wave forms. A sharp change in the signal structure resulting from impulsive exposure will be detected. And given the fact that the external (signal) events are known, we can compare the time of occurrence of changes in the signal and time of external events.

The basic idea is that news or events affect the exchange rate (Makin, 2016), so the individual waves in the original signal should carry suitable information. Thus, by using decomposition by Hilbert-Huang we can analyze the transition processes in the Russian economy in the context of the exchange rate for the Russian ruble against the US dollar.

In this work, with the help of the empirical mode decomposition method, we analyze the behavior of one of the commodities currencies during the oil shock of 2014–2015. As a result of the analysis, we obtain the original signal (time-series) decomposition into oscillatory components, which are interpreted and discussed at the end of the paper.

There are a large number of works devoted to the decomposition of oil prices on different bases and further predicting, for example, by using the wavelet transform (Yousefi et al., 2005), as well as through the decomposition by Hilbert-Huang, also known as the EMD, such as the works on forecasting the price of oil (Yu et al., 2008) and similar works that followed them (He et al., 2012).

The EMD method was applied to the analysis of the currency exchange rate by many researchers. For instance, the exchange rates were predicted using EMD algorithms (Yang and Lin, 2011).The key difference of this article from similar works is that we are concentrated on the impact of sanctions on the ruble while taking into account the fall in oil prices.

The article is arranged as follows: In Section 2, we describe the methodology of decomposition by Hilbert-Huang (Huang et al., 2003, 1998) and Hurst analysis (Hurst, 1965). Section 3 describes some specific aspects of the relationship between oil price and commodity currency in the case of the Russian ruble from a historical point of view. Short discussion of the source of data for such analysis is also presented in the Section 3. Section 4 describes the obtained results and final conclusions are presented in the Section 5.

Section snippets

Hilbert–Huang transform and end effect

As already mentioned, the authors use the method Empirical mode decomposition for analysis of the non-steady state of Russian economics during the oil shock in 2014–2015. A distinctive feature of the data used in this research is the non-stationarity of the process.

When using the EMD method, the input signal xt is divided into the sum of several intrinsic mode functions (IMFs):xt=i=1NIMFit+rtwhereIMFi

Intrinsic Mode Functions for i componentrt

residual

An IMF is a function that satisfies the

Oil price

From a historical point of view, the Russian ruble has a high correlation with oil price (Beckmann et al., 2016; Brahmasrene et al., 2014) with breaks every 3–4 years (see Fig. 2, Fig. 3).

Indeed, the Russian government budget highly depends on oil and gas revenues, and the ruble, Russia’s currency, are also highly influenced by oil prices. It is known that the correlation between the Russian ruble and the oil price is about 0.80, with 80% of the ruble moves determined by changes in oil prices (

Results and discussions

Again, the main idea of the article is to assess the impact of the sanctions. By analogy, described in (Huang et al., 2017), we decompose time series describing forex rates into 5-time scales, which represent a series of time scales, namely 1 day, 7 days, 14 days, 30 days, 60 days, 90 days,180 days, 360 days. The lower scales could capture the detail of the original time series, and the higher scales are proficient at identifying the trend changes. The corresponding relation between the scales

Conclusions

In this study, it was used decomposition by Hilbert-Huang to analyze transients in the Russian economy since the beginning of the crisis. On the basis of the transients decomposition, it was shown that the Russian currency exchange market is not efficient. Indeed, according to the theory of an effective market, information on the imposition of sanctions should immediately affect the ruble exchange rate, but this does not happen. We compared the timeline for the introduction of sanctions and

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