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Journal articles on the topic 'Cross-commodity'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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1

Ding, Shusheng, and Yongmin Zhang. "Cross market predictions for commodity prices." Economic Modelling 91 (September 2020): 455–62. http://dx.doi.org/10.1016/j.econmod.2020.06.019.

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2

Kunkler, Michael. "Commodity Market Heterogeneity and Cross-Market Integration." Applied Finance Letters 6, no. 01 (December 6, 2017): 16–27. http://dx.doi.org/10.24135/afl.v6i01.61.

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We evaluate the recent levels of heterogeneity and cross-market integration for fluctuations in commodity futures returns for a post-financial-crisis data sample. We find that a single commodity-market risk factor explains 30.6% of the total variation in commodity futures returns. The commodity-market risk factor is significantly correlated with the dominant market-wide risk factors from other asset classes: +66.7% with a market risk factor for the US equity market; -74.2% with a US dollar risk factor for the FX market; and -27.8% with an interest-rate level risk factor for the US interest rate market. Thus, a part of the systematic variation in the commodity market is integrated with other asset classes.
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3

Meyers, William H., S. Devadoss, and Michael D. Helmar. "Agricultural trade liberalization: Cross-commodity and cross-country impact products." Journal of Policy Modeling 9, no. 3 (September 1987): 455–82. http://dx.doi.org/10.1016/0161-8938(87)90025-1.

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4

Doncova, Olesya, and V. Zas'ko. "Institutional Framework for Cross-Border Commodity Trade." Scientific Research and Development. Economics of the Firm 9, no. 3 (October 7, 2020): 43–48. http://dx.doi.org/10.12737/2306-627x-2020-43-48.

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The article analyzes the basic strategies and business models of international commodity trade. The success factors of the organization of an effective system of commodity sales are highlighted: 1) a reliable network of global communication, which is provided by highly qualified personnel; 2) the ability to attract resources in international financial markets; 3) control over the objects of the basic logistics infrastructure of cross-border trade; 4) timely digital transformation of business. The article concludes that the current organizational mechanism for cross-border commodity trade is based on the following key success factors: an effective network of global business contacts, access to Bank financing and risk hedging tools, qualified personnel, and effective digitalization of business processes. The intersection of the competencies that lie in these planes allows us to obtain a stable competitive advantage in the most important commodity markets for the world economy. From a practical point of view, the greatest synergy of the key success factors of cross-border trade is achieved in the main hubs, which is important to take into account when implementing projects to build organizations that are competitive in foreign markets.
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5

Jaimungal, Sebastian, and Vladimir Surkov. "Lévy-Based Cross-Commodity Models and Derivative Valuation." SIAM Journal on Financial Mathematics 2, no. 1 (January 2011): 464–87. http://dx.doi.org/10.1137/100791609.

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6

Wang, Qing, and Yiming Hu. "Cross-correlation between interest rates and commodity prices." Physica A: Statistical Mechanics and its Applications 428 (June 2015): 80–89. http://dx.doi.org/10.1016/j.physa.2015.02.053.

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7

Börger, Reik, Álvaro Cartea, Rüdiger Kiesel, and Gero Schindlmayr. "Cross-commodity analysis and applications to risk management." Journal of Futures Markets 29, no. 3 (March 2009): 197–217. http://dx.doi.org/10.1002/fut.20359.

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8

Benet, Bruce A. "Commodity futures cross hedging of foreign exchange exposure." Journal of Futures Markets 10, no. 3 (June 1990): 287–306. http://dx.doi.org/10.1002/fut.3990100307.

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9

Li, Bin, Cheng Sun, and Yang Zhou. "The cross section of Chinese commodity futures return." Journal of Management Science and Engineering 6, no. 2 (June 2021): 146–64. http://dx.doi.org/10.1016/j.jmse.2021.03.001.

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10

Chen, Hsiu-Lang. "Cross-Market Investor Sentiment in Commodity Exchange-Traded Funds." Credit and Capital Markets – Kredit und Kapital 48, no. 2 (June 2015): 171–206. http://dx.doi.org/10.3790/ccm.48.2.171.

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11

Ohsawa, Yoshiaki. "Cross-border shopping and commodity tax competition among governments." Regional Science and Urban Economics 29, no. 1 (January 1999): 33–51. http://dx.doi.org/10.1016/s0166-0462(97)00028-8.

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12

Liu, Li. "Cross-correlations between crude oil and agricultural commodity markets." Physica A: Statistical Mechanics and its Applications 395 (February 2014): 293–302. http://dx.doi.org/10.1016/j.physa.2013.10.021.

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13

Struck, Clemens, and Enoch Cheng. "The Cross Section of Commodity Returns: A Nonparametric Approach." Journal of Financial Data Science 2, no. 3 (June 17, 2020): 86–103. http://dx.doi.org/10.3905/jfds.2020.1.034.

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14

Brooks, Chris, Adrian Fernandez-Perez, Joëlle Miffre, and Ogonna Nneji. "Commodity risks and the cross-section of equity returns." British Accounting Review 48, no. 2 (June 2016): 134–50. http://dx.doi.org/10.1016/j.bar.2016.03.001.

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15

Perumandla, Swamy, and Padma Kurisetti. "Commodity Transaction Tax (CTT)." International Journal of Asian Business and Information Management 12, no. 2 (April 2021): 16–36. http://dx.doi.org/10.4018/ijabim.20210401.oa2.

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This study aims to examine the time-varying correlations and volatility linkages between commodity and equity markets before and after the implementation of the commodity transaction tax (CTT) in India in 2013. The study utilizes symmetric and asymmetric DCC-EGARCH model to estimate correlation dynamics. Evidence suggests that the volatility and dynamic correlation linkages between commodities and equity markets are significantly affected by the triggering events. The time-varying correlations of Comdex-Nifty 50 show an unintended steep decline in the post-CTT period. It is an indication of a “flight to quality” phenomenon, where investors move capital from non-agricultural commodity futures to other cross markets and international markets. However, DCC of Comdex-Dhaanya pair is highly volatile in the post-CTT period and also noticed an increased correlation and volatility between the Dhaanya-Nifty 50 pair. Moreover, the correlation dynamics reveal a certain degree of interdependence between the cross markets, which are lower especially during the triggering episodes.
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16

Bickel, Warren K., Reid D. Landes, Darren R. Christensen, Lisa Jackson, Bryan A. Jones, Zeb Kurth-Nelson, and A. David Redish. "Single- and cross-commodity discounting among cocaine addicts: the commodity and its temporal location determine discounting rate." Psychopharmacology 217, no. 2 (April 14, 2011): 177–87. http://dx.doi.org/10.1007/s00213-011-2272-x.

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17

Lu, Xinsheng, Jianfeng Li, Ying Zhou, and Yubo Qian. "Cross-correlations between RMB exchange rate and international commodity markets." Physica A: Statistical Mechanics and its Applications 486 (November 2017): 168–82. http://dx.doi.org/10.1016/j.physa.2017.05.088.

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18

Sanders, Dwight R., and Scott H. Irwin. "A speculative bubble in commodity futures prices? Cross-sectional evidence." Agricultural Economics 41, no. 1 (January 2010): 25–32. http://dx.doi.org/10.1111/j.1574-0862.2009.00422.x.

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19

Nielsen, Soren Bo. "A Simple Model of Commodity Taxation and Cross-border Shopping." Scandinavian Journal of Economics 103, no. 4 (December 2001): 599–623. http://dx.doi.org/10.1111/1467-9442.00262.

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20

Shang, Hua, Ping Yuan, and Lin Huang. "Macroeconomic factors and the cross-section of commodity futures returns." International Review of Economics & Finance 45 (September 2016): 316–32. http://dx.doi.org/10.1016/j.iref.2016.06.008.

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21

Bannigidadmath, Deepa, and Paresh Kumar Narayan. "Economic news and the cross-section of commodity futures returns." Journal of Behavioral and Experimental Finance 31 (September 2021): 100540. http://dx.doi.org/10.1016/j.jbef.2021.100540.

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22

Chang, Jui-Chuan, and Ching-Chuan Tsong. "Exchange Rate Pass-Through and Monetary Policy: A Cross-Commodity Analysis." Emerging Markets Finance and Trade 46, no. 6 (November 2010): 106–20. http://dx.doi.org/10.2753/ree1540-496x460607.

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23

이성규. "Cross-Border Shopping and Commodity Tax Competition in Imperfectly Competitive Markets." Journal of Economic Research (JER) 13, no. 2 (November 2008): 183–210. http://dx.doi.org/10.17256/jer.2008.13.2.001.

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24

Sekine, Atsushi, and Takayuki Tsuruga. "Effects of commodity price shocks on inflation: a cross-country analysis." Oxford Economic Papers 70, no. 4 (June 21, 2018): 1108–35. http://dx.doi.org/10.1093/oep/gpy015.

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25

Jiang, Wenchao, Zhimeng Yin, Ruofeng Liu, Zhijun Li, Song Min Kim, and Tian He. "Boosting the Bitrate of Cross-Technology Communication on Commodity IoT Devices." IEEE/ACM Transactions on Networking 27, no. 3 (June 2019): 1069–83. http://dx.doi.org/10.1109/tnet.2019.2913980.

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26

Bickel, Warren K., M. J. Wesley, J. Shin, Mikhail N. Koffarnus, T. Lohrenz, and P. R. Montague. "Neural correlates of cross-commodity discounting in cocaine users and controls." Drug and Alcohol Dependence 140 (July 2014): e13-e14. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.058.

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27

Ahumada, Hildegart, and Magdalena Cornejo. "Explaining commodity prices by a cointegrated time series-cross section model." Empirical Economics 48, no. 4 (June 6, 2014): 1667–90. http://dx.doi.org/10.1007/s00181-014-0827-5.

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28

Hirst, Jason M., and Florence D. DiGennaro Reed. "Cross-Commodity Discounting of Monetary Outcomes and Access to Leisure Activities." Psychological Record 66, no. 4 (September 27, 2016): 515–26. http://dx.doi.org/10.1007/s40732-016-0201-4.

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29

Moody, Lara N., Allison N. Tegge, and Warren K. Bickel. "Cross-Commodity Delay Discounting of Alcohol and Money in Alcohol Users." Psychological Record 67, no. 2 (April 24, 2017): 285–92. http://dx.doi.org/10.1007/s40732-017-0245-0.

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30

HINZ, JURI, and MARTINA WILHELM. "PRICING FLOW COMMODITY DERIVATIVES USING FIXED INCOME MARKET TECHNIQUES." International Journal of Theoretical and Applied Finance 09, no. 08 (December 2006): 1299–321. http://dx.doi.org/10.1142/s0219024906004001.

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In this work, the valuation of energy-related financial contracts written on prices of flow commodities (such as natural gas, oil and electrical power) will be elaborated. Due to restrictions on storability of the underlying, the pricing of flow commodity derivatives is not trivial and thus correct valuation is still under discussion. In this paper, an axiomatic setting is followed, which provides a connection to interest rate theory, whose toolkit we utilize to consistently price frequently quoted flow commodity options such as caps, floors, collars and cross commodity spreads.
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31

Gozgor, G., and B. Kablamaci. "The linkage between oil and agricultural commodity prices in the light of the perceived global risk." Agricultural Economics (Zemědělská ekonomika) 60, No. 7 (July 18, 2014): 332–42. http://dx.doi.org/10.17221/183/2013-agricecon.

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The paper examines a systematic interrelationship between the world oil and agricultural commodity prices, taking the role of the USD and the perceived global market risks into consideration for the period from January 1990 to June 2013. The authors initially determine the significant cross-sectional dependence in a large balanced panel framework for 27 commodity prices, and then apply the second generation panel unit root (PUR) tests. Findings from the PUR tests clearly suggest that there is a strong unit root in agricultural commodity prices. In addition, the empirical findings from the fixed effects panel data, panel co-integration analysis, the Panel-Wald Causality tests, and the common correlated effects mean group estimations strongly show that the world oil price and the weak USD have positive impacts on almost all agricultural commodity prices. There are also retained the adjuvant effects of the escalatory perceived global market risk upon most agricultural commodity prices.  
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32

Gopinath, Munisamy, He Min, and Steven Buccola. "Technical Barriers to Interstate Trade: Noxious Weed Regulations." Journal of Agricultural and Applied Economics 42, no. 4 (November 2010): 617–30. http://dx.doi.org/10.1017/s1074070800003849.

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We focus on regulations controlling the spread of noxious weeds, especially the trade effects of regulatory differences across U.S. states. We specify a gravity model for each state's seed, nursery product, and commodity trade with each other state. Within the gravity model, we examine the role of cross-state regulatory congruence arising from ecological and agronomic characteristics and interest-group lobbying. A spatial-autoregressive Tobit model is estimated with a modified expectation-maximization algorithm. Results show that weed regulatory congruence positively affects interstate trade. By fostering cross-state regulatory differences, consumer and commodity-producer lobbying reduce the value of interstate trade by about two percent per annum.
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33

Kurach, Radosław. "Stocks, Commodities and Business Cycle Fluctuations – Seeking the Diversification Benefits." Equilibrium 7, no. 4 (December 31, 2012): 101–16. http://dx.doi.org/10.12775/equil.2012.029.

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In this study we empirically verify the diversification potential of different commodity sectors for equity portfolios. We also try to find the explanation of varying cross-sectoral diversification benefits by verifying the relationship between macroeconomic variables and commodity indices. We employ correlation analysis for our purposes. The obtained results indicate that Precious Metal and Livestock are valuable equity portfolio diversifiers, while Industrial Metals volatility has much in common with the fluctuations of broad stock market.
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34

Bakshi, Gurdip, Xiaohui Gao, and Alberto G. Rossi. "Understanding the Sources of Risk Underlying the Cross Section of Commodity Returns." Management Science 65, no. 2 (February 2019): 619–41. http://dx.doi.org/10.1287/mnsc.2017.2840.

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35

Tsitakis, D., S. Xanthopoulos, and A. N. Yannacopoulos. "A closed-form solution for the price of cross-commodity electricity derivatives." Physica A: Statistical Mechanics and its Applications 371, no. 2 (November 2006): 543–51. http://dx.doi.org/10.1016/j.physa.2006.03.037.

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36

Li, Zhihui, and Xinsheng Lu. "Cross-correlations between agricultural commodity futures markets in the US and China." Physica A: Statistical Mechanics and its Applications 391, no. 15 (August 2012): 3930–41. http://dx.doi.org/10.1016/j.physa.2012.02.029.

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37

Kim, Seungku. "Bsense: Practical Cross-Technology Communication Utilizing Beacon Frames of Commodity WiFi APs." IEEE Transactions on Wireless Communications 19, no. 2 (February 2020): 901–14. http://dx.doi.org/10.1109/twc.2019.2949818.

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38

Jiang, Huayun, Neda Todorova, Eduardo Roca, and Jen-Je Su. "Agricultural commodity futures trading based on cross-country rolling quantile return signals." Quantitative Finance 19, no. 8 (February 21, 2019): 1373–90. http://dx.doi.org/10.1080/14697688.2019.1571682.

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39

Sheng, Biyun, Fu Xiao, Letian Sha, and Lijuan Sun. "Deep Spatial–Temporal Model Based Cross-Scene Action Recognition Using Commodity WiFi." IEEE Internet of Things Journal 7, no. 4 (April 2020): 3592–601. http://dx.doi.org/10.1109/jiot.2020.2973272.

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40

Green, Rikard, Karl Larsson, Veronika Lunina, and Birger Nilsson. "Cross-commodity news transmission and volatility spillovers in the German energy markets." Journal of Banking & Finance 95 (October 2018): 231–43. http://dx.doi.org/10.1016/j.jbankfin.2017.10.004.

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41

Pritschmann, Ricarda K., Ali M. Yurasek, and Richard Yi. "A review of cross-commodity delay discounting research with relevance to addiction." Behavioural Processes 186 (May 2021): 104339. http://dx.doi.org/10.1016/j.beproc.2021.104339.

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42

Hvidt, Morten, and Søren Bo Nielsen. "Non-cooperative vs. Minimum- Rate Commodity Taxation." German Economic Review 2, no. 4 (December 1, 2001): 315–26. http://dx.doi.org/10.1111/1468-0475.00042.

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Abstract This paper demonstrates, within a simple two-country model of commodity taxation and cross-border shopping, that the tax revenue (welfare) effects of a minimum tax requirement depend crucially on the character of the initial non-cooperative tax equilibrium, i.e. whether it is Nash or Stackelberg.
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43

Wei, Ching-Chun. "Empirical Analysis of “Volatilitysurprise” between Dollar Exchange Rate and CRB Commodity Future Markets." International Journal of Economics and Finance 8, no. 9 (August 24, 2016): 117. http://dx.doi.org/10.5539/ijef.v8n9p117.

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This paper used the five multivariate GARCH models (including BEKK, CCC, DCC, VARMA-CCC and VARMA-DCC) to analyze the mean and volatility interaction of volatility surprise between US dollar exchange and CRB future index (including agricultural, energy, commodity and precious metal equity index). The empirical findings exhibit that significant own short and long-term persistence effects and the cross-markets volatility surprise spillover short and long-term persistence effects between dollar exchange rate and CRB commodity future equity index markets in five multivariate GARCH models. Besides that, the residual diagnostic test indicated that VARMA-DCC models is the best suitable model to modeling the dollar exchange rate with CRB commodity equity index.
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44

Poměnková, Jitka, and Zuzana Toufarová. "Analysis of consumer behaviour when purchasing selected commodity groups concerning the effect of price, habit, discount and product characteristics." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 56, no. 6 (2008): 93–102. http://dx.doi.org/10.11118/actaun200856060093.

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The aim of the paper is consumer behaviour analysis when purchasing selected commodity groups concerning the effect of price, habit, discount and product characteristics. Analysis proceed from the Czech household marketing research, where 726 households were electronically questioned. As mentioned above, selected factors for the analysis were habit, products‘ characteristics, price and discount actions.Primary aim is to measure the correspondence of selected factors influence on consumer behaviour during purchase decision making process of selected commodity groups. Interpretation is based on two-tier evaluation. First level represents commodity groups distinction by the character of goods and subsequent evaluation of goods characteristics correspondence in accordance with each influencing factor. Second one represents behaviour of commodity group in cross-section of selected factors. For consumer behaviour analysis chi-square test was used. Before its application the data set (responses) was divided according to the ten-point scale into three interval’ groups.
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45

Belke, Ansgar, and Jonas Keil. "Financial integration, global liquidity and global macroeconomic linkages." Journal of Economic Studies 43, no. 1 (January 11, 2016): 16–26. http://dx.doi.org/10.1108/jes-02-2015-0026.

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Purpose – The purpose of this paper is to analyse the effect of financial integration on several macroeconomic variables from a global perspective. Design/methodology/approach – The authors apply a cointegrated vector autoregression model using quarterly data for 1980-2009. Analysing the interactions of globally aggregated measures capturing cross-border financial transactions, monetary liquidity, output, consumer and commodity prices, the authors focus on the dissection of short-run and long-run dynamics. Findings – The authors find that increasing financial integration has a positive impact driving GDP. The authors also find evidence of two-way causality between commodity prices and financial flows. The results suggest that commodity prices are driven by financial integration and the gap between the dynamics of commodity prices and financial flows is closed by global liquidity injected by central banks. Originality/value – The paper contributes to the empirical literature by analysing the overall impact of global financial integration and of global liquidity on global macroeconomic variables in a unified framework.
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46

Firoozi, Fathali. "On the Second-Best Foreign Investment Policy and Pattern of Commodity Trade." American Economist 42, no. 1 (March 1998): 34–41. http://dx.doi.org/10.1177/056943459804200103.

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An agreement on free commodity trade often does not preclude countries from protecting their national interests through restrictive policies toward cross-border movements of production factors (e.g., capital and labor). A number of studies have suggested second-best international capital flows (welfare maximizing under free commodity trade) that officials of a country must encourage via various policy measures. However, an emerging literature indicates that policy toward foreign direct investment is being increasingly utilized as a new form of protectionism under free trade. Utilizing a generalized Heckscher-Ohlin model, this study characterizes the necessary adjustments to the suggested second-best foreign investment policies of a country when there is an extraneous protectionist objective regarding the pattern of trade in a commodity. An implication is that until all production factors can freely move internationally without policy impediments of a participating country, unrestricted commodity trade alone cannot achieve its full potential in removing protectionism and setting comparative advantage as the basis for trade. (JEL F21, F15)
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47

BLAIR, GRAEME, DARIN CHRISTENSEN, and AARON RUDKIN. "Do Commodity Price Shocks Cause Armed Conflict? A Meta-Analysis of Natural Experiments." American Political Science Review 115, no. 2 (January 19, 2021): 709–16. http://dx.doi.org/10.1017/s0003055420000957.

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Scholars of the resource curse argue that reliance on primary commodities destabilizes governments: price fluctuations generate windfalls or periods of austerity that provoke or intensify civil conflict. Over 350 quantitative studies test this claim, but prominent results point in different directions, making it difficult to discern which results reliably hold across contexts. We conduct a meta-analysis of 46 natural experiments that use difference-in-difference designs to estimate the causal effect of commodity price changes on armed civil conflict. We show that commodity price changes, on average, do not change the likelihood of conflict. However, there are cross-cutting effects by commodity type. In line with theory, we find price increases for labor-intensive agricultural commodities reduce conflict, while increases in the price of oil, a capital-intensive commodity, provoke conflict. We also find that price increases for lootable artisanal minerals provoke conflict. Our meta-analysis consolidates existing evidence, but also highlights opportunities for future research.
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48

Park, Jaehwan. "Volatility Transmission between Oil and LME Futures." Applied Economics and Finance 5, no. 2 (January 21, 2018): 65. http://dx.doi.org/10.11114/aef.v5i2.2944.

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This paper investigates the volatility transmission between oil and base metals to assess the possibility of hedge strategy across commodity markets. In order to identify the volatility linkage of oil to the base metals, the bivariate GARCH model is applied using daily returns data period over 2000-2016. It is found that evidence of volatility transmission between oil and base metals is somewhat strong with a 1% significant level. This result suggests the investment idea of commodity hedging strategy of cross-market is important.
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49

Valluri, Subhakara. "Commodity Indices Risk and Return Analysis Against Libor Benchmark." Applied Studies in Agribusiness and Commerce 12, no. 3-4 (December 13, 2018): 55–66. http://dx.doi.org/10.19041/apstract/2018/3-4/7.

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This study analyze the risk and return characteristics of commodity index investments against the LIBOR benchmark. Commodity-based asset allocation strategies can be optimized by benchmarking the risk and return characteristics of commodity indices with LIBOR index rate. In this study, we have considered agriculture, energy, and precious metals commodity indices and LIBOR index to determine the risk and return characteristics using estimation techniques in terms of expected return, standard deviation, and geometric mean. We analyzed the publicly available daily market data from 10/9/2001 to 12/30/2016 for benchmarking commodity indices against LIBOR. S&P GSCI Agriculture Index (SGK), S&P GSCI Energy Index (SGJ), and S&P GSCI Precious Metals Index (SGP) are taken to represent each category of widely traded commodities in the regression analysis. Our study uses time series data based on daily prices. Alternative forecasting methodologies for time series analysis are used to cross-check the results. The forecasting techniques used are Holt-Winters Exponential Smoothing and ARIMA. This methodology predicts forecasts using smoothening parameters. The empirical research has shown that the risk of each of the commodity index that represents agriculture, energy, and precious metals sector is smaller compared to its return, whereas LIBOR based interest rate benchmark shows higher risk compared to its return in recession, non-recession and overall periods. JEL Classification: C43, G13, G15
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50

Shubin, Ilia. "Cross-Border Trade of Russian Regions in 2013–2019." Spatial Economics 17, no. 2 (2021): 34–56. http://dx.doi.org/10.14530/se.2021.2.034-056.

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The article examines the cross-border trade of the Russian regions, taking into account the indicators of its volume and commodity structure, and the level of economic complexity. It is concluded that the cross-border trade in Russian regions has, in general, low intensity. This is due to several factors: most of the border areas are located on the economic periphery, both of Russia and of neighboring countries, there are physical and geographical barriers in many areas, and the development of the border area is low. In some cases, the low economic potential of a neighboring country or the existing geopolitical restrictions prevent the growth of trade. Against this background, two sections of the border area stand out: the Russian-Belarusian and the Russian-Chinese. In the first case, the development of trade is facilitated by the absence of customs barriers, historic ties and ethno-cultural proximity, a high degree of infrastructure development of border areas; in the second – by the huge scale of the economy of the neighboring country and a large potential volume of trade with it. In terms of the commodity structure of cross-border trade and its complexity, Russian border regions are usually suppliers of relatively simple goods: raw materials or products of the first processing stages, and import goods of higher complexity, which generally corresponds to the foreign trade specialization of Russia. In 2013–2019, the volume of cross-border trade in Russian regions significantly decreased, mainly due to a reduction in consumer and investment imports caused by a decrease in demand. The strongest decline occurred in cross-border trade with Ukraine. The volume of cross-border trade increased during this period in the Russian-Finnish and Russian-Estonian sections of the border (due to an increase in the volume of exports of nickel matte and mineral fertilizers). The changes in the commodity structure of cross-border trade that took place in 2013–2019 indicate the consolidation of the existing specialization of Russian border regions as suppliers of raw materials and simple products in trade with neighboring countries (except for the republics of the former USSR)
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