CC BY
73
(«DI
RESEARCH ARTICLE
https://doi.org/10.17059/ekon.reg.2022-2-6 UDC 336.761
Kazi Sohaga), Shaiara Husainb), Kristina Chukavinac), Md Al Mamund)
a> c) Ural Federal University, Ekaterinburg, Russian Federation b) University of Western Australia, Crawley, Australia d) La Trobe University, Bundoora, Australia a) https://orcid.org/0000-0002-0976-2357; e-mail: ksokhag@urfu.ru
b) https://orcid.org/0000-0002-7228-3869
c) https://orcid.org/0000-0002-0189-2923
d) https://orcid.org/0000-0002-6540-9195
Policy Uncertainty, Oil Price, Stock Market and Precious Metal Markets Volatility Spillovers in the Russian Economy1
The Russian economy is emerging, meaning that natural resources play a dominant role in economic development. Given the considerable volatility in resource prices, we investigate the volatility spillovers among policy uncertainty, international oil prices, exchange rate, stock index and metal prices covering the period of 2 July 2008 to 15 May 2020 for the Russian economy applying Dynamic Connectedness based on Time-Varying Parameter Vector Autoregression (TVP-VAR). Our empirical investigation demonstrates that gold price, Russian policy uncertainty, oil price and stock index are net volatility contributors, whereas palladium, platinum, silver and exchange rate are net volatilities receivers. Market capitalisation and silver market are found to be the highest net contributor and net receiver, respectively. The palladium appears as a net volatility receiver initially, just after the global financial crisis. The Russian economic policy uncertainty appears to be the dominant volatility contributor from 2008 to 2014, but onward it turned to be a net volatility receiver. Over the year 2014, gold price was the prominent volatility contributor to another market when the oil price dropped significantly. The total connectivity of the markets are highly anchored with several exogenous shocks, including economic sanction, adoption of floating exchange rate, oil price plunge. Our empirical findings provide several policy implications to portfolio managers and Russian regional stakeholders.
Keywords: volatility spillovers, TVP-VAR, policy uncertainty, oil price, exchange rate, metal price, gold price, stock index, silver price, Russian Federation
Acknowledgements
The article has been prepared with the support of the grant of RFBR and INSF, code: 20-510-56021 "Modeling the future of Oil Demand and Fiscal Sustainability: Evidence from Iran and Russia".
For citation: Sohag, K., Husain, S., Chukavina, K. & Al Mamun, Md. (2022). Policy Uncertainty, Oil Price, Stock Market and Precious Metal Markets Volatility Spillovers in the Russian Economy. Ekonomika regiona [Economy of regions], 18(2), 383397, https://doi.org/10.17059/ekon.reg.2022-2-6.
1 © Sohag K., Husain S., Chukavina K., Al Mamun Md Text. 2022.
Экономмка peruoHa,T. 18, Bun. 2 (2022)
ИССЛЕДОВАТЕЛЬСКАЯ СТАТЬЯ
К. Сохаг а), Ш. Хусейн б), К. Чукавинав), Мд Аль Мамунг)
а> в) Уральский федеральный университет, г. Екатеринбург, Российская Федерация б) Университет Западной Австралии, Кроули, Австралия г) Университет Ла Троба, Бандура, Австралия а) https://orcid.org/0000-0002-0976-2357; е-таИ: ksokhag@urfu.ru
б) https://orcid.org/0000-0002-7228-3869
в) https://orcid.org/0000-0002-0189-2923
г) https://orcid.org/0000-0002-6540-9195
Влияние эффектов перетекания волатильности на политическую неопределенность, цены на нефть, биржу и рынки драгоценных металлов в российской экономике
Российская экономика — это развивающаяся экономика, природные ресурсы играют доминирующую роль в экономическом развитии страны. Следовательно, на национальную экономику влияет значительная волатильность цен на ресурсы. В статье исследуется влияние эффектов перетекания волатильности на политическую неопределенность, мировые цены на нефть, обменный курс, фондовые индексы и цены на металлы в российской экономике за период со 2 июля 2008 г. по 15 мая 2020 г. Для анализа использована модель векторной авторегрессии с изменяющимися во времени параметрами (TVP-VAR). Проведенное эмпирическое исследование показывает, что цена на золото, политическая неопределенность, цена на нефть и фондовый индекс являются источниками волатильности. В то же время, волатильность влияет на такие факторы, как палладий, платина, серебро и обменный курс рубля. Рыночная капитализация является чистым донором, рынок серебра — чистым получателем. Палладий стал источником чистой волатильности после мирового финансового кризиса. Неопределенность российской экономической политики была основным источником волатильности с 2008 по 2014 гг., однако впоследствии волатильность других факторов оказывала на нее большее влияние. В 2014 г., когда цена на нефть значительно снизилась, цена на золото была основным источником волатильности для других рынков. Полная связанность рынков в значительной степени зависит от ряда экзогенных потрясений, таких как экономические санкции, введение режима плавающего обменного курса, падение цен на нефть. Исходя из представленного анализа, сформулировано несколько рекомендаций для портфельных инвесторов и стейкхолдеров в российских регионах.
Ключевые слова: эффекты перетекания волатильности, TVP-VAR, политическая неопределенность, цена на нефть, обменный курс, цена на металлы, цена на золото, фондовый индекс, цена на серебро, Российская Федерация
Благодарность
Данная статья написана при поддержке гранта РФФИ и INSF «Моделирование взаимозависимости спроса на нефть и фискальной устойчивости на примере рынков России и Ирана» (проект № 20-510-56021).
Для цитирования: Сохаг К., Хусейн Ш., Чукавина К., Аль Мамун Мд. Влияние эффектов перетекания волатильности на политическую неопределенность, цены на нефть, биржу и рынки драгоценных металлов в российской экономике // Экономика региона. 2022. Т. 18, вып. 2. С. 383-397. https://doi.org/10.17059/ekon.reg.2022-2-6.
1. Introduction
The Russian economy is emerging where natural resources play a dominant role in economic development (Malle, 2013). The economy is also considered to be endowed with various mineral resources such as oil, natural gas, gold, silver, platinum and palladium. Russia preserves six percent of the world deposit of oil and three percent of global gas deposits1. The Russian economy has experienced a steady increment in the corpo-
1 The Mineral Industry of Russia on the Mineral Resources Program portion of the USGS website. Retrieved from: https://www.usgs.gov/energy-and-minerals/mineral-resour ces-program.
rate income tax earned as the profit share of all the extractive companies ranging from 18.6 % to 22.7 % in response to increasing oil prices over 2005-2013 (Sabitiva, Shavaleyeva, 2015). In addition, Sohag, Gainetdinova and Mariev (2021) documented that an increase in oil price appreciates the Russian rouble. Accordingly, these precious metal resources' price volatilities have significant consequences on fiscal sustainability, eventually affecting Russia's economic growth. Besides, Russia faces economic sanctions, including in oil exploration and production equipment paralysing international oil revenue. The sanctions have resulted in large scale capital outflows leading to the
collapse of the RUR exchange rate in recent times. In this paper, however, we focus on whether economic policy uncertainty partly explains the nature of price volatilities in the precious metals market, stock market and the exchange rate market. With this background, we attempt to analyse the impact of policy uncertainty on explaining the price dynamics in precious metals markets, stock market and exchange rate market in the context of Russia.
In general, the literature demonstrates a keen interest in assessing the impact of policy uncertainty on various markets in the post-financial crisis period (Antonakakis, Chatziantoniou, Filis, 2013). Specifically, economic policy response changes following the unanticipated oil price shocks. The interplay between the oil price shocks and policy uncertainty influences the financial market by altering expected cash flows and discount rates. The increment in the price of inputs and a substantial reduction in the production process cause inflation and a decline in the investors' expectations regarding the stock market, contributing to the nexus between oil price, policy uncertainty, and financial market (Hamilton, 1996; Sadorsky, 1999). The change in expected discounted cash flows regulates asset price suggested by the economic theories (Williams, 1938; Fisher, 1930; Filis, Degiannakis, Floros, 2011). The firmlevel uncertainty regarding investment return is responsible for cyclical fluctuations in aggerate investment in the economy (Elder, Seletis 2010). The firm-level uncertainty affects the investment in oil and other precious metals as these investments are contained in most individual and institutional investors' portfolios (Sari et al. 2010).
The ongoing economic sanctions on Russia have depressed the private sector, resulting in a decline in gross capital formation. This has squeezed down access to global financial markets, thereby reducing capital inflows. The high dependence of a major oil-importing country like Russia on oil revenue makes it even more vulnerable to the extent that it affects financial market by bringing large changes in cash flows in response to even insignificant changes in oil prices and exchange rates (Dabrowski, 2019; Huang et al., 2017). Economic policy uncertainty arises as the Russian government has lost a significant portion of its revenue due to economic sanctions, therefore unable to provide due financial support to the private sector.
We study the volatility transmission mechanism among the oil, gold, silver, palladium, platinum, policy uncertainty, exchange rate and market capitalisation in the context of Russia. Following
the literature where spillover impacts have been mostly studied applying different specifications of VAR, we apply a Time-Varying Parameter Vector Autoregression (TVP-VAR) approach to analyse the data span over the period from 2 July 2008 to 15 May 2020. We demonstrate the appropriateness of our methodology by arguing that the TVP-VAR is an upgraded version of VAR which is insensitive to outliers, helps prevent losing observations and is independent of the size of the rolling window (Antonakakis, Chatziantoniou, Gabauer, 2020).
Our analysis highlights that gold price, Russian policy uncertainty, oil price and stock index are net volatility contributors. Since oil is the chief export commodity of Russia, any volatilities in the oil prices contribute to policy uncertainty and market capitalisation by changing the expected cash flows, which also determines the prices of other valuable assets. Mainly gold price co-moves in the same direction with policy uncertainty, oil prices and market capitalisation because the increasing use of gold as an investment asset to combat inflationary pressure and inflationary expectations is a common trend. Any fluctuation in oil prices will tend to influence Russian policy uncertainty, the gold market and market capitalisation in the same direction. These findings are in line with the current literature findings where stock prices have been shown to share a positive relationship with gold prices (Mensi et al., 2014) and oil prices have been shown to be associated with higher stock indices for BRICS countries (Ono, 2011). However, palladium, platinum, silver and exchange rates are found to be net receivers. The exchange rate becomes net receiver as it absorbs any volatility in oil prices since oil prices are denominated in the dollar exchange rate. Market capitalisation and silver market are found to be the highest net contributor and net receiver, respectively.
Anecdotally, there are instances of recent harmonisation among oil prices, metal prices and exchange rate in the context of Russia. However, literature acknowledges that the variation in exchange rates has spillover impacts on global crude oil market and domestic stock returns as well (Sari, Hammoudeh, Soytas, 2010; Bouoiyour et al., 2015; Gavin, 1989; Reboredo, Rivera-Castro, Ugolini, 2016). Among other precious metals, increasing gold use as an investment asset to combat inflationary pressure and inflationary expectations is a common trend. Moreover, the more significant industrial usage of precious metal cousins, including platinum and palladium, is another crucial reason for substituting these metals, leading to coherence among their prices. Individual and institutional investors' portfolios contain
both oil and precious metals priced in US dollars; therefore, the dollar exchange rate contributes to both oil and precious metals (Sari, Hammoudeh, Soytas, 2010).
Furthermore, the impact of oil price changes on stock prices has been assessed due to the increment in financial integration among the countries where oil prices volatility has been shown to propagate the stock market through their influence on expected dividends and cashflows (Jones, Kaul, 1996; El-Sharif et al., 2005). On the contrary, some other studies denote an inverse relationship between stock prices and oil and gas prices for US. and Australia (Huang et al., 1996; Sadorsky, 1999) and Australia (Faff, Brailsford, 1999). However, these researches have been carried out for Canada, Greece, US, UK and the Australian economy. Hence, the interconnections among oil price changes, precious metal prices, policy uncertainty, stock prices, and the exchange rate have been overlooked in the context of Russian economy. Analysing the nexus between these can help contribute to policy formulation and investment strategies for oil-exporting countries like Russia.
Quantifying the impact of uncertainty shocks on macroeconomic activity has been a common research area in recent literature, predominantly using different specifications of VAR approaches (Bloom, 2009; Baker, Bloom, Davis, 2014; Caggiano, Castelnuovo, Figueres, 2013). A substantial amount of literature analyses the spillover impact of US macroeconomic shocks on the business cycle and financial markets at a global context (Kim, 2001; Favero, Giavazzi, 2008). There has also been a comparative discussion between US uncertainty shock and area-specific uncertainty shock by estimating the impact of uncertainty shock on European aggregates (Colombo, 2013). Russia, one of the BRICS economies, is particularly vulnerable to global economic factors as it is a significant recipient of global investment flows and one of the principal consumers of commodities (Mensi et al., 2014). Thence, the impact of policy uncertainty on the Russian economy, which has not been addressed in the literature, is worth studying. The Russian economy has importance in terms of the abundance of its natural resources, and as a result, it is a principal recipient of global investment flows. Russia's policy uncertainty is also of paramount importance due to the reasons mentioned earlier and its promising economic growth. Hence, no previous literature assessed the volatility transmission among the oil prices, exchange rate, political uncertainty, market capitalisation and other precious metals, including gold,
silver, palladium, platinum for Russia. We, therefore, claim that our research questions are unprecedented in the literature.
2. Review of Literature
Examination of the connectedness among economic policy uncertainty, precious metal prices, oil prices and macroeconomic indicators (the exchange rate and market capitalisation) is of interest for academics, investors, portfolio managers and policymakers (Yang, 2019). The subject is more critical for an economy like Russian, where hydrocarbon and precious metal revenues play an essential role in the fiscal stance, and, eventually the whole economy.
There exists an extensive group of studies investigating effects of economic policy uncertainty on economic recessions and recoveries, real economic activity and asset pricing models (Baker, Bloom, Davis, 2016; Bloom, 2009; Bloom, 2014; Brogaard, Detzel ,2015), as well as uncertainty spillovers across various countries (Antonakakis, Chatziantoniou, Filis, 2014; Bhattarai, Chatterjee, Park, 2019; Caggiano, Castelnuovo, Figueres, 2020; Colombo, 2013; Klößner, Sekkel, 2014). In this section, we are focusing on the group of studies on volatility spillovers and dynamic connectedness of financial and commodity markets, with the special attention on economic policy uncertainty issues. In terms of methodology, most volatility spillover studies in financial and commodity markets rely on various modifications of generalised autoregressive conditional het-eroskedasticity (GARCH) type models, i. e. Vector Autoregressive GARCH, Exponential GARCH, Fractionally Integrated GARCH, Univariate, Bivariate and Multivariate GARCH, Dynamic Conditional Correlation GARCH, etc. (Kang, Ratti, Vespignani, 2017; Basher, Sadorsky, 2016; Mensi et al., 2014; Creti, Joets, Mignon, 2013; Arouri, Jouini, Nguyen, 2012). Some studies implement D.Y. approach (Diebold and Yilmaz, 2014) considering time and frequency domain (Husain et al., 2019; Barunik, Krehlik, 2017). For instance, Barunik, Kocenda and Vacha (2016b), Mensi et al., (2013), Creti, Joets and Mignon (2013) and Choi and Hammoudeh, (2010), among others, examine interrelations between commodity and stock markets in a time-varying perspective and find linkages between these assets with increased volatility over time. Gold and silver transmit information to other commodity futures markets (WTI, corn, wheat, and rice) (Kang, Mclver, Yoon, 2017), while real oil prices have positive impact on gold (Tiwari, Sahadudheen, 2015). Finally, palladium, gold and platinum are strong contributors to the
volatility spillover among crude oil, stock market and other precious metals indices, and crude oil, titanium, steel and silver are net receivers (Husain et al., 2019). Overall, existing research on the relations between commodity and stock markets is limited to the effort of uncovering volatility spillover effects and market co-movements under both time and frequency domain (Ji et al., 2018; Khalfaoui, Boutahar, Boubaker, 2015; Mensi et al. 2013; Arouri, Jouini, Nguyen, 2011; Arouri, Jouini, Nguyen, 2012).
In comparison to the strand of research revealing oil price shock's impact on stock markets or equity markets with the use of GARCH type models, only several studies focused on its impact on metal prices, interest rates and exchange rates with the use of D.Y. extensions (Guhathakurta Dash, Maitra, 2020; Awartani, Aktham, Cherif, 2016; Yang, Zhou 2017; Mandaci, Cagli, Ta§kin, 2020). Moreover, prior studies on the interrelations between oil, precious metals and stock market indicators mainly focused on developed economies, with few exceptions (Bouri et al., 2017; Ghosh, Kanjilal, 2016; Raza, et al., 2016; Jain, Biswal, 2016; Sadorsky, 2014).
Considering an aspect of economic policy uncertainty, the existing group of studies does not investigate in details the nature of connectedness between policy uncertainty and oil price shocks. There are only a few recent studies in this context. Focusing on the US market, Yang (2019) postulates that, regarding economic policy uncertainty, the crude oil price is information receiver and that US economic policy uncertainty reflects tremendous significance in the long run. Focusing on dynamic connectedness and spillover effects in oil-importing countries, Wang and Lee (2020) reveal robust results on the impact of fiscal policy uncertainty, exchange rate policy uncertainty, monetary policy uncertainty, and trade policy uncertainty on crude oil returns. Dynamic connectedness between three identified structural oil price shocks and gold price in the presence of economic uncertainty is considered in the study (Mokni et al., 2020); one of the main findings is that economic policy uncertainty has a significant impact on the dynamic connectedness. In addition to the scarcity of existing research regarding dynamic connectedness and policy uncertainty, to the best of our knowledge, there exists no other study in this context of Russian economy applying TVP-VAR model extension of the Diebold and Yilmaz (2014) technique. Our study's primary goal is to fill this gap in the literature by using novel data.
Russia is an important member of BRICS, a major global economic block. It is an important mem-
ber of oil-exporting countries as one of the biggest energy supplier in Europe (Fang, You, 2014; Filis, Chatziantoniou, 2014; Malik & Umar, 2019). In contrast to extensive international evidence, current literature considering Russian evidence on energy market — stock market nexus is somewhat limited. For example, Fang and You (2014) found that only supply-side oil price shocks have a significant positive effect on the Russian stock market. Huang et al., (2017) showed oil price and exchange rate volatilities across time influence the Russian stock market. Bouoiyour et al. (2015) demonstrated the bidirectional long-run relationship between oil price and real exchange rate, whereas the direct impact of the oil price on the real exchange rate is conditional to various mac-roeconomic control variables. Overall, there is no evidence of the dynamic linkage among policy uncertainty, stock market, oil price and precious metal markets volatilities spillover in Russian settings.
3. Data and Methodology 3.1. Data and Preliminary Analysis
We utilise the daily data from 2 July 2008 to 15 May 2020 in our empirical setup. Table 1 describes our variables, definition and sources.
The balanced availability of all series determines our sample period. The primarily concerned variable is Russian economic policy uncertainty, which is constructed based on the key economic policy terms in the newspaper articles. We consider market capitalisation, which is the sum of the product of share price times the number of shares outstanding for all listed domestic companies. Anecdotal evidence shows that the stock market is susceptible to policy uncertainty. Since Russia is highly dependent on hydrocarbon exports, we take international oil price, which explains the country's foreign currency reserve, and exchange rate. Exchange rate volatility, which is relatively high in Russia, is sensitive to Russian economic policy, trade and international relations. Due to its plausible role, this study includes daily exchange rate (Rouble/1USD). Figure 1 shows rouble devalued sharply in the mid of 2014 and onward due to imposition of economic sanction. Concurrently, the international price plunged due to thriving US shale oil production and gaining efficiency. Russia is a top-3 country in terms of producing minerals including gold, platinum and palladium and silver. We consider the daily price of gold, platinum and palladium and silver in our price volatility spillover framework. Figure 1 shows that prices of precious metals are soaring overtime, contrary to the
Table 1
Data, definition and sources
Variable Definition Source
Policy uncertainty index (PUI) To measure policy-related economic uncertainty for Russia, we construct an index based on frequency counts of newspaper articles http://www.policyuncertainty.com/index.html
Market capitalisation (LMC) The sum of the product of share price times the number of shares outstanding for all listed domestic companies Bank of Russia https://www.cbr.ru/eng/hd_base/
Oil Price (Oil) Spot crude oil price in dollars per barrel Energy Information Administration https:// www.eia.gov/
Official exchange rate (EXR) Average weighted rate (Rouble/US dollar) Bank of Russia https://www.cbr.ru/eng/hd_base/
Gold Price (Gold) Reference prices for refined gold per gram Bank of Russia https://www.cbr.ru/eng/hd_base/
Silver Price (Silver) Reference prices for refined silver per gram Bank Russia https://www.cbr.ru/eng/hd_base/
Platinum Price (Platinum) Reference prices for refined platinum per gram Bank of Russia https://www.cbr.ru/eng/hd_base/
Palladium price (Palladium) Reference prices for refined Palladium per gram Bank of Russia https://www.cbr.ru/eng/hd_base/
oil price, which is partially helping to lower the oil price induced fiscal pressure.
3.2. Econometric Approach
This study applies dynamic connectedness under time-varying parameter vector autoregression (TVP-VAR) approach proposed by Antonakakis and Gabauer (2017) which is an updated version of dynamic connectedness or spillover impact proposed by Diebold and Yilmaz (Diebold and Yilmaz, 2009; Diebold and Yilmaz, 2012; Diebold and Yilmaz, 2014). The current framework includes a changing variance via a stochastic volatility Kalman Filter estimation, along with forgetting factors developed by Koop and Korobilis (2014). Therefore, this approach can overcome the biases that a standard technique often encounters due to arbitrarily selection of rolling window size. It is argued that an arbitrary selection of rolling window size leads an inconsistent parameter and reduces valuable observations. Dynamic connectedness under time-varying parameter vector autoregression (TVP-VAR) approach is also robust in the case of a less frequent and short span of time-series data.
TVP-VAR approach can be exhibited as follows
Yt = btXt-1 + ^ I Ft_1 ~ N (0, St), (1)
pt=pt_1 +VV I F-! ~ N (0, R), (2)
where Yt indicates a column matrix (N x 1) conditional volatility vector, Y t is the lagged conditional vector of Y t following Np x 1 order or matrix. R t is the time-varying coefficient matrix follow-
ing the N x Np dimension. et is the vector of error terms following N x 1 dimension along with N x N time-varying covariance matrix St. The vector of the coefficient matrix Rt relies on their respective values b t - j following N x Np dimensional residual matrix along with an N x N variance-covar-iance matrix. This approach subsequently measures the generalised connectedness following Diebold and Yilmaz (2014) considering time-varying parameters and error covariances. This framework eventually allows to estimate volatility spillover by utilising generalised impulse response functions (GIRF), and generalised forecast error variance decompositions (GFEVD) suggested by Koop, Pesaran and Potter (1996) and Pesaran and Shin (1998), respectively. Note that, we transform the VAR to its vector moving average (VMA) representation to estimate GIRF and GFEVD following the Wold theorem as follows:
Y = P Y-! + et, (3)
Y = Ae, (4)
Ac, t = I, (5)
A ,t = Px, t A-1, t +-+Pp,tA-pt, (6)
where pt = [p!, t, p2, t, ..., Rj' and At = A t, A2, , ..., A ,]', therefore R., and A. f are N x N dimensional
p, tJ' i^i, t i, t
parameter matrices.
GIRF exhibits the responses of all respective variables after a shock in variable i.
As our model does not follow a structural modelling, we estimate the differences between a J — step-ahead forecast in the case if variable i is
30 n 25 20 15 -10 5
0
CO
Policy Uncertainity
10
26,5 -,
26
25,5
a <u (J
25
24,5
24 -
23,5
Market Capitalisation
41 10
90 80 70 -60 50 40 -30 20 10 0
7000 6000
m5000 4000 -
S 2000 -
= 1000 ce
0
Exchange Rate
Oil Price
Palladium Price
14 10
5000
m4000
о
3000
Л 2000
1000
Gold Price
4 10
3000
Platinum
50
40
E
12 30 -a
10
о
(N
0
shocked as well as not shocked. The difference can be estimated to the shock in variable i, as follows
GIR ( J, s, ,t,Ft) =
E(Yt+j I e,,t =sj,t,F-1 )-E(Yt+j | Ft_1 ),
о
(N
0
Fig. 1. Trend Analysis
Silver
о
(N
0
о
(N
0
о
(N
0
о
(N
0
g ît\ AJ,tStej,t sj,t
о
(N
0
s j ,t 4 sjj ,t > (8)
V4, (J) = S-2A,
(9)
0
К 20 -
0
where J indicates the forecast period of time, 8. t, the selection vector with one on the 7th position and zero otherwise, and Ft the information set until t _ 1. Subsequently, we estimate GFEVD that can be explained as the variance share one variable has on others. The estimated variances are eventually normalised, so that each row added up to one, indicating that all variables together describe 100 % of variable's i forecast error variance. This is estimated as follows
I *
♦ I ( J)= -NTÏ-=
II m
j=1 t=1
(10)
with £*1 (J) = 1 and £*Nt (J) = N. Applying i=1 i,j=1 GFEVD, we estimate the total connectedness index by
N
£ *i,t (J)
Cgg (J ) = if7-100, (11)
£ *g,t ( j)
i, 7=1
I ♦ g,t ( J )
i ,j=1, i*j
N
100.
(12)
This framework of connectedness demonstrates how shocks in a variable spillover to other variables. First, we observe the case where variable i transmits its shock to all other variables j, shown as
j ,t ( J ) =
iV
I j ( J )
j=1,i
N
100.
(13)
£* L (J)
i=1
Second, we calculate the directional connectedness variable i receives from variables j, called total directional connectedness from others, defined as
Çl j ,t ( J ) =
I ♦ h ( J )
j=1,i
N
I ♦ g,t ( J )
100.
(14)
Finally, we subtract total directional connectedness to others from total directional connectedness from others to obtain the net total directional connectedness, which can be interpreted as the 'power' of variable i, or, its influence on the whole variables' network.
C?,t (J) = Ci>j,t (J)_(J). (15)
If the net total directional connectedness of variable i is positive, it means that variable i influences the network more than being influenced by that. By contrast, if the net total directional connectedness is negative, it means that variable i is driven by the network
4. Results and Discussion
We estimate the volatility spillover effects among several macroeconomic indicators including Russian economic policy uncertainty, exchange rate, stock market and different precious metal markets including gold, silver, platinum and pallidum. Table 4 presents the results highlighting the total volatility of spillover effects. The ith and jth entry in each panel are estimated contribution to the forecast-error variance of variable i coming from market j. The diagonal coefficients of Table 4 present the autoregressive or own lag values effect on the forecast-error variance, while the off-diagonal coefficients present cross-market spillover. The last column of Table 4 reports ith variables receive the magnitude of volatility from the vector jth variables. The third last row of Table 4 highlights the total volatility spillover effect that each variable contributes to other variables. The last row highlights the net volatility contribution of each variable by subtracting total volatility receives from the total volatility contribution, respectively. The net positive values on the last row indicate the net volatility contributors, whereas the negative values represent the net volatility receivers. Our model is explained by 50 % volatility spillover in all the selected markets. Our analysis demonstrates that gold, policy uncertainty, oil and market capitalisation are net volatility contributors whereas palladium, platinum, silver and exchange rate are net volatility receivers. In our model, the stock market and silver market are found to be the highest volatility contributor and receiver, respectively.
Policy uncertainty index (PUI) is influenced by the lagged values of economic policy uncertainty by 67.15 %. Table 4 also shows that the volatility of PUI is the reason for more than 5 % volatility in the foreign exchange rate through the channel of import and export. Our empirical findings support the proposition of Beckmann and Czudaj (2017) who document a strong association between policy uncertainty and exchange rate. The announcement of any economic decision influences the exchange rate as different economic agents react based on either adaptive or rational expectation. PUI also contributes more than 5 % volatility in the stock market. Our estimated result echoes a couple of empirical and seminal
i=1
Table 2
Summary Statistics
PUI GOLD EXR PLATINUM PALLADIUM OILPRICE LMC SILVER
Mean 6.125189 1919.375 45.94689 1675.560 1290.787 77.02315 25.72614 27.75130
Median 5.349053 1717.890 35.84353 1677.340 930.1950 71.79500 25.78650 30.57000
Maximum 27.67387 4217.370 84.07080 2746.690 5923.000 143.9500 26.16336 44.53000
Minimum 0.630901 602.4700 23.02503 664.7800 144.3200 9.120000 24.50646 7.810000
Std. Dev. 3.713458 732.0766 16.45657 304.1315 1045.014 26.79085 0.287529 7.776761
Skewness 1.995622 0.337800 0.254311 -0.196035 1.752926 0.169180 -1.932899 -0.689269
Kurtosis 10.59746 2.430994 1.353727 4.540158 6.312629 1.876670 7.386406 2.593841
Jarque-Bera 13306.36 140.9566 536.3835 456.3287 4203.120 248.6619 6176.081 373.1365
Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Sum 26558.82 8322409. 199225.7 7265228. 5596851. 333972.4 111548.5 120329.6
Sum Sq. Dev. 59778.64 2.32E+09 1173999. 4.01E+08 4.73E+09 3111445. 358.3868 262172.2
Observations 4336 4336 4336 4336 4336 4336 4336 4336
Table 3
TVP-VAR-Static
Palladium Gold Platinum Silver EXR PUI OIL MC FROM
PALLADIUM 74.322 8.236 14.188 3.158 0.03 0.035 0.025 0.007 25.678
GOLD 6.942 52.903 28.683 11.104 0.264 0.038 0.033 0.033 47.097
PLATINUM 10.773 28.412 52.75 7.942 0.084 0.001 0.029 0.01 47.25
SILVER 3.741 14.861 11.718 69.44 0.052 0.083 0.097 0.008 30.56
EXR 0.016 0.036 0.051 0.013 98.273 0.016 1.459 0.136 1.727
PUI 0.07 0.003 0.027 0.028 0.118 99.601 0.063 0.09 0.399
OIL 0.025 0.14 0.047 0.056 2.278 0.016 96.292 1.146 3.708
MC 0.026 0.026 0.018 0.01 0.152 0.019 0.316 99.433 0.567
Contribution TO others 21.592 51.714 54.731 22.311 2.979 0.208 2.021 1.431 156.987
Contribution including own 95.914 104.617 107.481 91.75 101.252 99.809 98.313 100.864 TCI
Net spillovers -4.086 4.617 7.481 -8.25 1.252 -0.191 -1.687 0.864 19.623
Table 4
TVP-VAR-Dynamic
PUI STM Gold OIL Palladium Platinum Silver EXR FROM
PUI 67.152 5.924 4.238 5.362 4.07 3.931 3.908 5.416 32.848
STM 5.225 69.874 3.768 5.929 3.636 3.706 3.405 4.457 30.126
Gold 4.479 4.632 34.078 4.991 14.672 13.925 12.799 10.424 65.922
OIL 5.087 6.786 5.154 60.737 4.644 4.761 4.243 8.588 39.263
Palladium 4.462 4.882 14.13 6.347 40.537 11.238 9.374 9.03 59.463
Platinum 4.955 5.689 15.851 4.972 12.012 38.565 11.417 6.538 61.435
Silver 4.259 5.326 17.123 5.646 7.139 13.547 41.134 5.826 58.866
EXR 5.038 6.991 9.515 10.153 10.766 5.134 4.511 47.892 52.108
Contribution TO others 33.504 40.229 69.779 43.4 56.94 56.242 49.657 50.279 400.032
Contribution including own 100.656 110.103 103.857 104.137 97.477 94.807 90.791 98.171 TCI
Net spillovers 0.656 10.103 3.857 4.137 -2.523 -5.193 -9.209 -1.829 50.004
studies who relate the role of policy uncertainty in translating stock volatilities (Boutchkova et al., 2012; Pastor, Veronesi, 2012; Durnev, 2011; Goodell, Vahamaa, 2013). Prior literature documents that the stock market volatility is sensitive with macroeconomic policies as corporate react in term of their investment decisions. Policy uncertainty has the least influence on silver market
volatility, which can be attributed to the fact that its price is relatively low compared to other precious metals and less elastic with any exogenous shocks. Policy uncertainty contributes 33.5 % of volatiles to other variables while its own volatility is contributed by 32.8 %; hence, it appears to be a net contributor. Other market price volatilities significantly influence the economic policy
Fig. 2. Net Volatility Spillover effect
since the Russian government requires to accommodate inevitable changes in other markets, especially oil price and exchange rate. Our propositions are reflected by pairwise assessment as Table 4 shows PUI is the net contributor of volatility spillovers to mainly precious metal prices including gold, platinum and silver.
In contrast, Russian PUI in influenced by the stock market, oil price, palladium price and exchange rate volatiles. Our findings provide an insight that Russian policy is influenced by the in-
ternational oil price rather than its ability to influence. Although Russia is one of the biggest oil-producing countries with relatively lower extraction and refinery cost, Russia has less influence against its rival due to OPEC curtail and their aligned oil-exporting countries. Nevertheless, we believe that Russian policy uncertainty is influenced by both the demand-driven and supply-driven oil price shocks unlike OPEC countries (Baumeister, Peersman, 2013; Hamilton, 2009; Kilian, 2009; Lippi, Nobili, 2012)
Stock market (STM) appears to be the highest net volatility contributor (10.10 %) to the other markets. Table 4 reports that STM influences the volatility of other respective variables about 40.229 % while it is influenced by 30.12 % from the rest of the seven variables. STM mostly influences policy uncertainty, exchange rate and oil price, as well as STM is influenced by them. The highest and lowest volatility contribution of STM is found to be 6.99 % towards exchange rate and 4.63 % towards the gold market. On the contrary,
011 marker has the strongest influence and palladium has the least influence on STM.
The gold market appears to be the highest total (69.77 %) and third-largest net (3.85 %) volatility contributor to other concerning markets. Table 4 shows that the gold market is the reason for 14.13 %, 15.851 % and 17.123 % volatility in palladium, platinum and silver markets, respectively. In contrast, gold price volatility is explained mainly by palladium price, exchange rate and policy uncertainty. About 34.07 % price volatility of the gold market price is influenced by its own lagged price. Russia's oil export meets around
12 % of global oil demand. Thus, international oil price plays an important role in the Russian balance of payment, exchange rate and other markets. Oil price contributes 43.4 % volatility to the other markets. Interestingly, our time-varying analysis shows that oil price net contributor influences the Russian policy uncertainty. Either way, oil price and exchange rate appear to be the highest volatility contributors of each other.
Russian exchange rate (EXR) is sensitive to the countries' geopolitical issues as exchange rate with USD climbed up from roughly from 35 RUB to 65 RUB per 1 USD. Our empirical analysis shows that EXR is a net volatility receiver. Russia faces economic sanctions including in oil exploration and production equipment and services which aggravates large scale capital outflows leading to the collapse of the RUR exchange rate in 2014-2015. Interestingly, EXR has a net spillover effect on economic policy uncertainty (5.416 - 5.038 = 0.378), gold price (10.424 - 9.515 = 0.909), platinum price (6.538 - 5.134 = 1.404) and silver price (5.826 - 4.511 = 1.315). EXR receives net volatility spillover from the stock market (4.457 - 6.991 = = -2.534), oil price (8.588 - 10.153 = -1.565) and palladium price (9.030 - 10.766 = 1.736). Our empirical findings are in harmony with prior literature, where they document that a variation in exchange rates has spillover impacts on global crude oil market and domestic stock returns as well (Sari, Hammoudeh, Soytas, 2010; Bouoiyour et al., 2015;
Gavin, 1989; Reboredo, Rivera-Castro, Ugolini, 2016).
Figure 2 reports the net volatility spillover effect of each market. The figure shows that palladium appears as a net volatility receiver at the beginning, just after the global financial crisis. Over the year 2014, gold price was the prominent volatility contributor to another market when the oil price dropped significantly. Russian exchange rate often receives the volatility spillover from the other market, consistent with (Sohag et al., 2021). Interestingly, Russian economic policies are often induced by the other markets.
5. Conclusion
The world economy is characterised by an unnatural fluctuation due to various market connectedness, financial crisis, policy uncurtaining, pandemic, endogenous and economic policy uncertainty. In this study, we examined the dynamic connectedness among economic policy uncertainty, international oil price, exchange rate, stock market index and the prices of various precious metals in the context of the Russian economy. To this end, we applied a dynamic connectedness volatility spillover approach under time-Varying Parameter Vector Autoregression (TVP-VAR) framework to analyse daily data for the period from 2 July 2008 to 15 May 2020. The conducted research yielded several interesting findings. Stock market volatility is found to be the main volatility spillover contributor to other markets considered in this study. Our empirical investigation demonstrates that economic policy uncertainty is the smallest net volatility spillover contributor to other markets. Besides, the international oil price and gold price appear to be net volatility spillover contributors. In contrast, our analysis highlights that silver, platinum and palladium markets, as well as exchange rate, are net volatility receivers from stock, gold and international oil price market as well as economic policy uncertainty. The silver market is found to be the main net volatility spillover receiver.
Our empirical findings can be helpful to portfolio managers for hedging purposes as well as the Russian economy, primarily focusing on natural resource extracting regions. For instance, the gold price is a useful hedge against the silver price as we found the gold price is the highest volatility contributor to the silver price. Russian economic policy uncertainty also highly influences the Moscow stock exchange and exchange rate; thus, the policy stability is vital to stabilize the respective indicators.
Экономмка peruoHa,T. 18, Bun. 2 (2022)
References
Abhyankar, A., Xu, B. & Wang, J. (2013). Oil price shocks and the stock market: evidence from Japan. The Energy Journal, 34(2), 199-222.
Aloui, R. & Aissa, M. S. Ben. (2016). Relationship between oil, stock prices and exchange rates: A vine copula based GARCH method. The North American Journal of Economics and Finance, 37, 458-471. DOI: 10.1016/j.najef.2016.05.002
Aloui, R., Hammoudeh, S. & Nguyen, D. K. (2013). A time-varying copula approach to oil and stock market dependence: The case of transition economies. Energy Economics, 39, 208-221. DOI: 10.1016/j.eneco.2013.04.012
Anjum, H. & Malik, F. (2019). Oil prices and exchange rates. In: Routledge Handbook of Energy Economics. Routledge.
Antonakakis, N. (2012). Exchange return co-movements and volatility spillovers before and after the introduction of euro. Journal of International Financial Markets, Institutions and Money, 22(5), 1091-1109. DOI: 10.1016/j.int-fin.2012.05.009.
Antonakakis, N. & Filis, G. (2013). Oil prices and stock market correlation: a time-varying approach. International Journal of Energy and Statistics, 1(1), 17-29. DOI: 10.1142/S2335680413500026.
Antonakakis, N. & Gabauer, D. (2017). Refined Measures of Dynamic Connectedness based on TVP-VAR. MPRA Paper No. 78282, 14.
Antonakakis, N., Chatziantoniou, I. & Filis, G. (2013). Dynamic co-movements of stock market returns, implied volatility and policy uncertainty. Economics Letters, 120(1), 87-92. DOI: 10.1016/j.econlet.2013.04.004.
Antonakakis, N., Chatziantoniou, I. & Filis, G. (2014). Dynamic spillovers of oil price shocks and economic policy uncertainty. Energy Economics, 44, 433-447. DOI: 10.1016/j.eneco.2014.05.007.
Antonakakis, N., Chatziantoniou, I. & Gabauer, D. (2020). Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4), 84. DOI: 10.3390/ jrfm13040084.
Arouri, M. E. H., Jouini, J. & Nguyen, D. K. (2011). Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management. Journal of International Money and Finance, 30(7), 1387-1405. DOI: 10.1016/j. jimonfin.201L07.008
Arouri, M. E. H., Jouini, J. & Nguyen, D. K. (2012). On the impacts of oil price fluctuations on European equity markets: Volatility spillover and hedging effectiveness. Energy Economics, 34(2), 611-617. DOI: 10.1016/j.eneco.2011.08.009.
Awartani, B., Aktham, M., & Cherif, G. (2016). The connectedness between crude oil and financial markets: Evidence from implied volatility indices. Journal of Commodity Markets, 4(1), 56-69. DOI: 10.1016/j.jcomm.2016.11.002.
Baker, S. R., Bloom, N. & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636. DOI: 10.1093/qje/qjw024.
Barunik, J. & Krehlik, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296. DOI: 10.1093/jjfinec/nby001.
Barunik, J., Kocenda, E. & Vacha, L. (2016a). Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers. Journal of Financial Markets, 27, 55-78. DOI: 10.1016/j.finmar.2015.09.003.
Barunik, J., Kocenda, E. & Vacha, L. (2016b). Gold, oil, and stocks: Dynamic correlations. International Review of Economics & Finance, 42, 186-201. DOI: 10.1016/j.iref.2015.08.006.
Basher, S. A., Haug, A. A. & Sadorsky, P. (2012). Oil prices, exchange rates and emerging stock markets. Energy Economics, 34(1), 227-240. DOI: 10.1016/j.eneco.2011.10.005.
Basher, S. A., Haug, A. A. & Sadorsky, P. (2018). The impact of oil-market shocks on stock returns in major oil-exporting countries. Journal of International Money and Finance, 86, 264-280. DOI: 10.1016/j.jimonfin.2018.05.003.
Basher, S. A. & Sadorsky, P. (2016). Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics, 54, 235-247. DOI: 10.1016/j.eneco.2015.11.022.
Baumeister, C. & Peersman, G. (2013). Time-varying effects of oil supply shocks on the U.S. economy. American Economic Journal: Macroeconomics, 5(4), 1-28. DOI: 10.1257/mac.5.4.1.
Beckmann, J. & Czudaj, R. (2013). Oil prices and effective dollar exchange rates. International Review of Economics & Finance, 27, 621-636. DOI: 10.1016/j.iref.2012.12.002.
Beckmann, J. & Czudaj, R. (2017). Exchange rate expectations and economic policy uncertainty. European Journal of Political Economy, 47, 148-162. DOI: 10.1016/j.ejpoleco.2016.06.003.
Benassy-Ouere, A., Mignon, V. & Penot, A. (2007). China and the relationship between the oil price and the dollar. Energy Policy, 35(11), 5795-5805. DOI: 10.1016/j.enpol.2007.05.035.
Bhattarai, S., Chatterjee, A. & Park, W. Y. (2019). Global spillover effects of U.S. uncertainty. Journal of Monetary Economics. DOI: 10.1016/j.jmoneco.2019.05.008.
Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623-685. DOI: 10.3982/ECTA6248.
Bloom, N. (2014). Fluctuations in uncertainty. Journal of Economic Perspectives, 28(2), 153-176. DOI: 10.1257/ jep.28.2.153
Bodenstein, M., Erceg, C. J. & Guerrieri, L. (2011). Oil shocks and external adjustment. Journal of International Economics, 83(2), 168-184. DOI: 10.1016/j.jinteco.2010.10.006.
Boubaker, H. & Raza, S. A. (2017). A wavelet analysis of mean and volatility spillovers between oil and BRICS stock markets. Energy Economics, 64, 105-117. DOI: 10.1016/j.eneco.2017.01.026.
Bouoiyour, J., Selmi, R., Tiwari, A. K. & Shahbaz, M. (2015). The nexus between oil price and Russia's real exchange rate: Better paths via unconditional vs conditional analysis. Energy Economics, 51, 54-66. DOI: 10.1016/j. eneco.2015.06.001.
Boutchkova, M., Doshi, H., Durnev, A. & Molchanov, A. (2012). Precarious politics and return volatility. The Review of Financial Studies, 25(4), 1111-1154. DOI: 10.1093/rfs/hhr100.
Bouri, E., Jain, A., Biswal, P. C. & Roubaud, D. (2017). Cointegration and nonlinear causality amongst gold, oil, and the Indian stock market: Evidence from implied volatility indices. Resources Policy, 52, 201-206. DOI: 10.1016/j.resour-pol.2017.03.003.
Bouri, E., Shahzad, S. J. H., Raza, N. & Roubaud, D. (2018). Oil volatility and sovereign risk of BRICS. Energy Economics, 70, 258-269. DOI: 10.1016/j.eneco.2017.12.018.
Brogaard, J. & Detzel,A. (2015). The asset-pricing implications of government economic policy uncertainty. Management Science, 61(1), 3-18. DOI: 10.1287/mnsc.2014.2044.
Caggiano, G., Castelnuovo, E. & Figueres, J. M. (2020). Economic policy uncertainty spillovers in booms and busts. Oxford Bulletin of Economics and Statistics, 82(1), 125-155. DOI: 10.1111/obes.12323.
Choi, K. & Hammoudeh, S. (2010). Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment. Energy Policy, 38(8), 4388-4399. DOI: 10.1016/j.enpol.2010.03.067.
Colombo, V. (2013). Economic policy uncertainty in the U.S.: Does it matter for the Euro area? Economics Letters, 121(1), 39-42. DOI: 10.1016/j.econlet.2013.06.024.
Creti, A., Joets, M. & Mignon, V. (2013). On the links between stock and commodity markets' volatility. Energy Economics, 37, 16-28. DOI: 10.1016/j.eneco.2013.01.005.
Dabrowski, M. (2019). Factors determining Russia's long-term growth rate. Russian Journal of Economics, 5(4), 328353. DOI: 10.32609/j.ruje.5.49417.
Degiannakis, S., Filis, G. & Floros, C. (2013). Oil and stock returns: Evidence from European industrial sector indices in a time-varying environment. Journal of International Financial Markets, Institutions and Money, 26(C), 175-191. DOI: 10.1016/j.intfin.2013.05.007.
Degiannakis, S., Filis, G. & Panagiotakopoulou, S. (2018). Oil price shocks and uncertainty: How stable is their relationship over time? Economic Modelling, 72, 42-53. DOI: 10.1016/j.econmod.2018.01.004.
Diebold, F. X. & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119(534), 158-171. DOI: 10.1111/j.1468-0297.2008.02208.x.
Diebold, F. X. & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66. DOI: 10.1016/j.ijforecast.2011.02.006.
Diebold, F. X. & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119-134. DOI: 10.1016/j.jeconom.2014.04.012.
Durnev, A. (2011). Politics, instability, and international investment flows. SSRN 1785033. DOI: 10.2139/ssrn.1785033.
Elder, J. & Serletis, A. (2010). Oil price uncertainty. Journal of Money, Credit and Banking, 42(6), 1137-1159. DOI: 10.1111/j.1538-4616.2010.00323.x.
El-Sharif, I., Brown, D., Burton, B., Nixon, B. & Russell, A. (2005). Evidence on the nature and extent of the relationship between oil prices and equity values in the UK. Energy economics, 27(6), 819-830. DOI: 10.1016/j.eneco.2005.09.002.
Fang, C.-R. & You, S.-Y. (2014). The impact of oil price shocks on the large emerging countries' stock prices: Evidence from China, India and Russia. International Review of Economics & Finance, 29, 330-338. DOI: 10.1016/j. iref.2013.06.005.
Favero, C., & Giavazzi, F. (2008). Should the Euro area be run as a closed economy? American Economic Review, 98(2), 138-45. DOI: 10.1257/aer.98.2.138.
Filis, G. & Chatziantoniou, I. (2014). Financial and monetary policy responses to oil price shocks: Evidence from oil-importing and oil-exporting countries. Review of Quantitative Finance and Accounting, 42(4), 709-729. DOI: 10.1007/ s11156-013-0359-7.
Filis, G., Degiannakis, S. & Floros, C. (2011). Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries. International Review of Financial Analysis, 20(3), 152-164. DOI: 10.1016/j. irfa.2011.02.014.
Fisher, I. (1930). The Theory of Interest. New York: Macmillan.
Gavin, M. (1989). The stock market and exchange rate dynamics. Journal of international money and finance, 8(2), 181-200. DOI: 10.1016/0261-5606(89)90022-3.
Ghosh, S. & Kanjilal, K. (2016). Co-movement of international crude oil price and Indian stock market: Evidences from nonlinear cointegration tests. Energy Economics, 53, 111-117. DOI: 10.1016/j.eneco.2014.11.002.
Goodell, J. W. & Vahamaa, S. (2013). US presidential elections and implied volatility: The role of political uncertainty. Journal of Banking & Finance, 37(3), 1108-1117. DOI: 10.1016/j.jbankfin.2012.12.001.
Guhathakurta, K., Dash, S. R. & Maitra, D. (2020). Period specific volatility spillover based connectedness between oil and other commodity prices and their portfolio implications. Energy Economics, 85, 104566. DOI: 10.1016/j. eneco.2019.104566.
Gurgun, G. & Unalmi§, 1. (2014). Is gold a safe haven against equity market investment in emerging and developing countries? Finance Research Letters, 11(4), 341-348. DOI: 10.1016/j.frl.2014.07.003.
Экономмка peruoHa,I. 18, Bun. 2 (2022)
Hamilton, J. D. (1983). Oil and the macroeconomy since world war II. Journal of Political Economy, 91(2), 228-248. DOI: 10.1086/261140.
Hamilton, J. D. (1996). This is what happened to the oil price-macroeconomy relationship. Journal of Monetary Economics, 38(2), 215-220. DOI: 10.1016/S0304-3932(96)01282-2.
Hamilton, J. D. (2009a). Causes and Consequences of the Oil Shock of 2007-08. National Bureau of Economic Research. DOI: 10.3386/w15002.
Hamilton, J. D. (2009b). Understanding crude oil prices. The Energy Journal, 30(2).
Huang, S., An, H., Gao, X., Wen, S. & Hao, X. (2017). The multiscale impact of exchange rates on the oil-stock nexus: Evidence from China and Russia. Applied Energy, 194, 667-678. DOI: 10.1016/j.apenergy.2016.09.052
Huang, Y. & Feng, G. U. O. (2007). The role of oil price shocks on China's real exchange rate. China Economic Review, 18(4), 403-416. DOI: 10.1016/j.chieco.2006.02.003
Husain, S., Tiwari, A. K., Sohag, K. & Shahbaz, M. (2019). Connectedness among crude oil prices, stock index and metal prices: An application of network approach in the USA. Resources Policy, 62, 57-65. DOI: 10.1155/2019/1562130.
Jain, A. & Biswal, P. C. (2016). Dynamic linkages among oil price, gold price, exchange rate, and stock market in India. Resources Policy, 49, 179-185. DOI: 10.1016/j.resourpol.2016.06.001
Ji, Q., Liu, B. Y., Zhao, W. L. & Fan, Y. (2018). Modelling dynamic dependence and risk spillover between all oil price shocks and stock market returns in the BRICS. International Review of Financial Analysis, 68. DOI: 10.1016/j. irfa.2018.08.002.
Jones, C. M. & Kaul, G. (1996). Oil and the stock markets. The Journal of Finance, 51(2), 463-491. DOI: 10.1111/ j.1540-6261.1996.tb02691.x.
Kang, S. H., McIver, R. & Yoon, S.-M. (2017). Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Economics, 62, 19-32. DOI: 10.1016/j.eneco.2016.12.011
Kang, W. & Ratti, R. A. (2013). Structural oil price shocks and policy uncertainty. Economic Modelling, 35, 314-319. DOI: 10.1016/j.econmod.2013.07.025.
Kang, W., Ratti, R. A. & Vespignani, J. L. (2017). Oil price shocks and policy uncertainty: New evidence on the effects of U.S. and non-US oil production. Energy Economics, 66, 536-546. DOI: 10.1016/j.eneco.2017.01.027.
Khalfaoui, R., Boutahar, M. & Boubaker, H. (2015). Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis. Energy Economics, 49, 540-549. DOI: 10.1016/j.eneco.2015.03.023.
Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review, 99(3), 1053-1069. DOI: 10.1257/aer.99.3.1053.
Kilian, L. & Park, C. (2009). The impact of oil price shocks on the U.S. stock market. International Economic Review, 50(4), 1267-1287. DOI: 10.1111/j.1468-2354.2009.00568.x.
Kim, S. (2001). International transmission of US monetary policy shocks: Evidence from VAR's. Journal of monetary Economics, 48(2), 339-372. DOI: 10.1016/S0304-3932(01)00080-0.
Klößner, S. & Sekkel, R. (2014). International spillovers of policy uncertainty. Economics Letters, 124(3), 508-512. DOI: 10.1016/j.econlet.2014.07.015.
Koop, G. & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101-116. DOI: 10.1016/j.euroecorev.2014.07.002.
Koop, G., Pesaran, M. H. & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of econometrics, 74(1), 119-147. DOI: 10.1016/0304-4076(95)01753-4.
Krugman, P. (1983). Oil shocks and exchange rate dynamics. In: Exchange rates and international macroeconomics (pp. 259-284). University of Chicago Press.
Lippi, F. & Nobili, A. (2012). Oil and the macroeconomy: a quantitative structural analysis. Journal of the European Economic Association, 10(5), 1059-1083. DOI: 10.1111/j.1542-4774.2012.01079.x.
Malik, F. & Umar, Z. (2019). Dynamic connectedness of oil price shocks and exchange rates. Energy Economics, 84, 104501. DOI: 10.1016/j.eneco.2019.104501.
Malle, S. (2013). Economic modernization and diversification in Russia. Constraints and challenges. Journal of Eurasian Studies, 4(1), 78-99. DOI: 10.1016/j.euras.2012.07.004.
Mandaci, P. E., Cagli, E. & Ta§kin, D. (2020). Dynamic connectedness and portfolio strategies: Energy and metal markets. Resources Policy, 68, 101778. DOI: 10.1016/j.resourpol.2020.101778.
Mensi, W., Beljid, M., Boubaker, A. & Managi, S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling, 32, 15-22. DOI: 10.1016/j.econmod.2013.01.023.
Mensi, W., Hammoudeh, S., Reboredo, J. C. & Nguyen, D. K. (2014). Do global factors impact BRICS stock markets? A quantile regression approach. Emerging Markets Review, 19, 1-17.
Mokni, K., Hammoudeh, S., Ajmi, A. N. & Youssef, M. (2020). Does economic policy uncertainty drive the dynamic connectedness between oil price shocks and gold price? Resources Policy, 69, 101819. DOI: 10.1016/j.resour-pol.2020.101819.
Pastor, L. & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The Journal of Finance, 67(4), 1219-1264. DOI: 10.1111/j.1540-6261.2012.01746.x.
Pesaran, H. H. & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics letters, 58(1), 17-29. DOI: 10.1016/S0165-1765(97)00214-0.
Raza, N., Shahzad, S. J. H., Tiwari, A. K. & Shahbaz, M. (2016). Asymmetric impact of gold, oil prices and their volatilities on stock prices of emerging markets. Resources Policy, 49, 290-301. DOI: 10.1016/j.resourpol.2016.06.011.
Reboredo, J. C. (2012). Modelling oil price and exchange rate co-movements. Journal of Policy Modeling, 34(3), 419-440. DOI: 10.1016/j.jpolmod.2011.10.005.
Reboredo, J. C. (2013). Is gold a safe haven or a hedge for the U.S. dollar? Implications for risk management. Journal of Banking & Finance, 37(8), 2665-2676. DOI: 10.1016/j.jbankfin.2013.03.020.
Reboredo, J. C., Rivera-Castro, M. A. & Ugolini, A. (2016). Downside and upside risk spillovers between exchange rates and stock prices. Journal of Banking & Finance, 62, 76-96. DOI: 10.1016/j.jbankfin.2015.10.011.
Sabitova, N. & Shavaleyeva, C. (2015). Oil and gas revenues of the Russian federation: Trends and Prospects. Procedia Economics and Finance, 27, 423-428. DOI: 10.1016/S2212-5671(15)01016-3.
Sadorsky, P. (1999). Oil price shocks and stock market activity. Energy economics, 21(5), 449-469. DOI: 10.1016/ S0140-9883(99)00020-1.
Sadorsky, P. (2014). Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat. Energy Economics, 43, 72-81. DOI: 10.1016/j.eneco.2014.02.014.
Sari, R., Hammoudeh, S. & Soytas, U. (2010). Dynamics of oil price, precious metal prices, and exchange rate. Energy Economics, 32(2), 351-362. DOI: 10.1016/j.eneco.2009.08.010.
Singh, V. K., Nishant, S. & Kumar, P. (2018). Dynamic and directional network connectedness of crude oil and currencies: Evidence from implied volatility. Energy Economics, 76, 48-63. DOI: 10.1016/j.eneco.2018.09.018.
Sohag, K., Gainetdinova, A. & Mariev, O. (2021). The response of exchange rates to economic policy uncertainty: Evidence from Russia. Borsa Istanbul Review. DOI: 10.1016/j.bir.2021.07.002.
Tiwari, A. K. & Sahadudheen, I. (2015). Understanding the nexus between oil and gold. Resources Policy, 46, 85-91. DOI: 10.1016/j.resourpol.2015.09.003.
Wang, E. Z. & Lee, C. C. (2020). Dynamic spillovers and connectedness between oil returns and policy uncertainty. Applied Economics, 52(35), 3788-3808. DOI: 10.1080/00036846.2020.1722794.
Williams, B. J. (1938). The Theory of Investment Value. Cambridge: Harvard University Press.
Yang, L. (2019). Connectedness of economic policy uncertainty and oil price shocks in a time domain perspective. Energy Economics, 80, 219-233. DOI: 10.1016/j.eneco.2019.01.006.
Yang, Z. & Zhou, Y. (2017). Quantitative easing and volatility spillovers across countries and asset classes. Management Science, 63(2), 333-354. DOI: 10.1287/mnsc.2015.2305.
About the authors
Kazi Sohag — PhD in Economics, Academic Head of the Laboratory for International and Regional Economics, Graduate School of Economics and Management, Ural Federal University; https://orcid.org/0000-0002-0976-2357 (Ekaterinburg, 620075, Russian Federation; e-mail: ksokhag@urfu.ru).
Shaiara Husain — PhD Scholar, Department of Economics, University of Western Australia; https://orcid.org/0000-0002-7228-3869 (Crawley, Perth, 6009, Australia; e-mail: shaiara.husain@research.uwa.edu.au).
Kristina Chukavina — Masters in Economics, Senior Lecturer, Graduate School of Economics and Management, Ural Federal University; https://orcid.org/0000-0002-0189-2923 (Ekaterinburg, 620075, Russian Federation; e-mail: k.v.chuka-vina@urfu.ru).
Md Al Mamun — PhD in Finance, Lecturer, Department of Economics and Finance, La Trobe University; https:// orcid.org/0000-0002-6540-9195 (Bundoora, Melbourne, 3086, Australia; e-mail: M.AlMamun@latrobe.edu.au).
Информация об авторах
Сохаг Кази — кандидат экономических наук, научный руководитель, Лаборатория международной и региональной экономики, Институт экономики и управления, Уральский федеральный университет; https://orcid. org/0000-0002-0976-2357 (Российская Федерация, 620075, г. Екатеринбург; e-mail: ksokhag@urfu.ru).
Хусейн Шайара — аспирант, экономический факультета, Университет Западной Австралии; https://orcid. org/0000-0002-7228-3869 (Австралия, 6009, г. Перт, Кроули; e-mail: shaiara.husain@research.uwa.edu.au).
Чукавина Кристина — магистр экономики, старший преподаватель, Институт экономики и управления, Уральский федеральный университет; https://orcid.org/0000-0002-0189-2923 (Российская Федерация, 620075, г. Екатеринбург; e-mail: k.v.chukavina@urfu.ru).
Аль Мамун Мд — PhD в области финансов, преподаватель, кафедра экономики и финансов, Университет Ла Троба; https://orcid.org/0000-0002-6540-9195 (Австралия, 3086, г. Мельбурн, Бандура; e-mail: M.AlMamun@latrobe. edu.au).
Дата поступления рукописи: 15.12.2020.
Прошла рецензирование: 21.01.2022.
Принято решение о публикации: 07.04.2022.
Received: 15 Dec 2020.
Reviewed: 21 Jan 2022.
Accepted: 07 Apr 2022.
